tag:blogger.com,1999:blog-6844433171488929452024-03-05T14:18:40.431-08:00RDKitRDKit experiments, tips, and tutorialsgreg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.comBlogger98125tag:blogger.com,1999:blog-684443317148892945.post-91297014566438966132022-01-21T21:01:00.001-08:002022-01-21T21:01:07.695-08:00The RDKit blog has moved<p> The RDKit blog is now hosted here: https://greglandrum.github.io/rdkit-blog/</p><p><br /></p>greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com2tag:blogger.com,1999:blog-684443317148892945.post-35657468253559648552020-11-18T00:36:00.005-08:002020-11-18T05:03:34.551-08:00Sphere exclusion clustering with the RDKit<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>Roger Sayle contributed an implementation of sphere-exclusion picking to the RDKit as part of the 2019.09 release and I recently realized that I'd never blogged about that code or how to use it to do compound clustering. So here's a short(ish) one.</p>
<p>The RDKit has had an implementation of the MaxMin algorithm for picking diverse compounds for quite a while (Roger made this a lot faster back in 2017). The input to the MaxMin picker is the number of diverse compounds you want. The new algorithm is different: you provide the minimum distance allowed between the compounds picked and it returns a set of compounds satisfying that constraint.</p>
<p>Both of these methods for picking diverse compounds can then be converted into clustering algorithms by defining those picked points to be cluster centroids and then assigning non-picked compounds to the nearest centroid. We'll do that here for the sphere-exclusion algorithm.</p>
<p>Further reading:</p>
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<li>for more about the sphere-exclusion picker and/or learn how it works: <a href="https://github.com/rdkit/UGM_2019/raw/master/Presentations/Sayle_Clustering.pdf">here's Roger's UGM presentation</a></li>
<li>Roger's UGM presentation describing his fast implementation of the MaxMin picker is <a href="https://github.com/rdkit/UGM_2017/raw/master/Presentations/Sayle_RDKitDiversity_Berlin17.pdf">here</a></li>
<li>Tim Dudgeon's <a href="https://rdkit.blogspot.com/2017/11/revisting-maxminpicker.html">guest post on this blog</a> provides a nice overview of the new MaxMin picker.</li>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">Chem</span>
<span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">DataStructs</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">Draw</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">IPythonConsole</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">rdDepictor</span><span class="p">,</span> <span class="n">rdMolDescriptors</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">SetPreferCoordGen</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">rdkit</span>
<span class="o">%</span><span class="k">pylab</span> inline
<span class="nb">print</span><span class="p">(</span><span class="n">rdkit</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
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<pre>Populating the interactive namespace from numpy and matplotlib
2020.09.1
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<h1 id="First-dataset">First dataset<a class="anchor-link" href="#First-dataset">¶</a></h1>
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<p>The dataset we'll start with is the "new Lessel and Briem" set that I put together as part of <a href="http://rdkit.blogspot.com/2019/10/a-new-lessel-and-briem-like-dataset.html">this blog post</a></p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">ms</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">Chem</span><span class="o">.</span><span class="n">SmilesMolSupplier</span><span class="p">(</span><span class="s1">'../data/BLSets_selected_actives.txt'</span><span class="p">)]</span>
<span class="nb">len</span><span class="p">(</span><span class="n">ms</span><span class="p">)</span>
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<pre>6359</pre>
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<p>We'll use MFP2 fingerprints:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">rdMolDescriptors</span>
<span class="n">fps</span> <span class="o">=</span> <span class="p">[</span><span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">GetMorganFingerprintAsBitVect</span><span class="p">(</span><span class="n">m</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2048</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">]</span>
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<p>The new sphere-exclusion code is available using the <code>LeaderPicker</code>:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit.SimDivFilters</span> <span class="kn">import</span> <span class="n">rdSimDivPickers</span>
<span class="n">lp</span> <span class="o">=</span> <span class="n">rdSimDivPickers</span><span class="o">.</span><span class="n">LeaderPicker</span><span class="p">()</span>
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<p>And we pick compounds by giving the picker the fingerprints and a minimum distance between cluster centroids. Here we're using a distance threshold of 0.65, which is the random-similarity threshold <a href="http://rdkit.blogspot.com/2013/10/fingerprint-thresholds.html">I found for MFP2 fingeprints</a>.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">thresh</span> <span class="o">=</span> <span class="mf">0.65</span> <span class="c1"># <- minimum distance between cluster centroids</span>
<span class="n">picks</span> <span class="o">=</span> <span class="n">lp</span><span class="o">.</span><span class="n">LazyBitVectorPick</span><span class="p">(</span><span class="n">fps</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">fps</span><span class="p">),</span><span class="n">thresh</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">picks</span><span class="p">))</span>
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<pre>535
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<p>For reference, here's how long that takes to run:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">timeit</span> lp.LazyBitVectorPick(fps,len(fps),thresh)
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<pre>41.9 ms ± 262 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
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<p>Let's look at some of those picked compounds:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">([</span><span class="n">ms</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">picks</span><span class="p">[:</span><span class="mi">12</span><span class="p">]],</span><span class="n">molsPerRow</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
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<img 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<p>Just to get a feeling for what's going on, calculate the similarities between the compounds that have been picked.</p>
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<div class="prompt input_prompt">In [8]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">DataStructs</span>
<span class="n">pickfps</span> <span class="o">=</span> <span class="p">[</span><span class="n">fps</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">picks</span><span class="p">]</span>
<span class="n">nearest</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">simhist</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">fpi</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">pickfps</span><span class="p">):</span>
<span class="n">tfps</span> <span class="o">=</span> <span class="n">pickfps</span><span class="p">[:]</span>
<span class="k">del</span> <span class="n">tfps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">sims</span> <span class="o">=</span> <span class="n">DataStructs</span><span class="o">.</span><span class="n">BulkTanimotoSimilarity</span><span class="p">(</span><span class="n">fpi</span><span class="p">,</span><span class="n">tfps</span><span class="p">)</span>
<span class="n">nearest</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">sims</span><span class="p">))</span>
<span class="n">simhist</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">sims</span><span class="p">)</span>
<span class="nb">sorted</span><span class="p">(</span><span class="n">nearest</span><span class="p">,</span><span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)[:</span><span class="mi">10</span><span class="p">]</span>
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<div class="prompt output_prompt">Out[8]:</div>
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<pre>[0.3492063492063492,
0.3492063492063492,
0.3492063492063492,
0.3492063492063492,
0.3488372093023256,
0.3488372093023256,
0.3488372093023256,
0.3488372093023256,
0.3488372093023256,
0.3488372093023256]</pre>
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<p>Remember that we defined a distance threshold of 0.65, so there should be no similarity values above 0.35 here. It's good to see that this is true.</p>
<p>Here's the histogram of distances</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">hist</span><span class="p">(</span><span class="n">simhist</span><span class="p">,</span><span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">);</span>
<span class="n">xlabel</span><span class="p">(</span><span class="s1">'similarity'</span><span class="p">);</span>
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<img src="data:image/png;base64,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<p>Now let's assign points to clusters. As mentioned above, we do that by defining the picked compounds to be the centroids and then assign each other compound in the dataset to the nearest cluster centroid.</p>
<p>We don't currently have a single call for doing this, so here's a Python function:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="k">def</span> <span class="nf">assignPointsToClusters</span><span class="p">(</span><span class="n">picks</span><span class="p">,</span><span class="n">fps</span><span class="p">):</span>
<span class="n">clusters</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">picks</span><span class="p">):</span>
<span class="n">clusters</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">idx</span><span class="p">)</span>
<span class="n">sims</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">picks</span><span class="p">),</span><span class="nb">len</span><span class="p">(</span><span class="n">fps</span><span class="p">)))</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">picks</span><span class="p">)):</span>
<span class="n">pick</span> <span class="o">=</span> <span class="n">picks</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">sims</span><span class="p">[</span><span class="n">i</span><span class="p">,:]</span> <span class="o">=</span> <span class="n">DataStructs</span><span class="o">.</span><span class="n">BulkTanimotoSimilarity</span><span class="p">(</span><span class="n">fps</span><span class="p">[</span><span class="n">pick</span><span class="p">],</span><span class="n">fps</span><span class="p">)</span>
<span class="n">sims</span><span class="p">[</span><span class="n">i</span><span class="p">,</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">best</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">sims</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">idx</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">best</span><span class="p">):</span>
<span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">picks</span><span class="p">:</span>
<span class="n">clusters</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="k">return</span> <span class="n">clusters</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">clusters</span> <span class="o">=</span> <span class="n">assignPointsToClusters</span><span class="p">(</span><span class="n">picks</span><span class="p">,</span><span class="n">fps</span><span class="p">)</span>
<span class="n">hist</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">clusters</span><span class="p">[</span><span class="n">x</span><span class="p">])</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">clusters</span><span class="p">]);</span>
<span class="n">xlabel</span><span class="p">(</span><span class="s1">'cluster size'</span><span class="p">);</span>
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<div class="prompt input_prompt">In [12]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">hist</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">clusters</span><span class="p">[</span><span class="n">x</span><span class="p">])</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">clusters</span><span class="p">],</span><span class="n">log</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
<span class="n">xlabel</span><span class="p">(</span><span class="s1">'cluster size'</span><span class="p">);</span>
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<p>Unfortunately this implementation for assigning compounds to clusters isn't particularly efficient since it makes a bunch of calls across the Python/C++ interface:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">timeit</span> assignPointsToClusters(picks,fps)
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<pre>360 ms ± 10.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
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<p>It's pretty sad to have this be so slow after the super-fast identification of the cluster centroids, but I hope to have the chance to improve the performance of this step in a future RDKit release.</p>
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<h2 id="Looking-at-the-clusters">Looking at the clusters<a class="anchor-link" href="#Looking-at-the-clusters">¶</a></h2>
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<p>Let's look at the compounds inside a couple of clusters in order to see how closely related they seem to be:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">clusts12</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">clusters</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">clusters</span><span class="p">[</span><span class="n">x</span><span class="p">])</span><span class="o">==</span><span class="mi">12</span><span class="p">]</span>
<span class="nb">len</span><span class="p">(</span><span class="n">clusts12</span><span class="p">)</span>
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<pre>10</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">([</span><span class="n">ms</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">clusters</span><span class="p">[</span><span class="n">clusts12</span><span class="p">[</span><span class="mi">0</span><span class="p">]]],</span><span class="n">molsPerRow</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
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<p>We can also look at the intra-cluster similarities</p>
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<div class="prompt input_prompt">In [16]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">intracluster_similarities</span><span class="p">(</span><span class="n">cluster</span><span class="p">,</span><span class="n">fps</span><span class="p">):</span>
<span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">cfps</span> <span class="o">=</span> <span class="p">[</span><span class="n">fps</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">cluster</span><span class="p">]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">fpid</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">cluster</span><span class="p">):</span>
<span class="n">tres</span> <span class="o">=</span> <span class="n">DataStructs</span><span class="o">.</span><span class="n">BulkTanimotoSimilarity</span><span class="p">(</span><span class="n">cfps</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">cfps</span><span class="p">)</span>
<span class="k">del</span> <span class="n">tres</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="n">res</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">tres</span><span class="p">)</span>
<span class="k">return</span> <span class="n">res</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">hist</span><span class="p">(</span><span class="n">intracluster_similarities</span><span class="p">(</span><span class="n">clusters</span><span class="p">[</span><span class="n">clusts12</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span><span class="n">fps</span><span class="p">));</span>
<span class="n">xlabel</span><span class="p">(</span><span class="s1">'Similarity'</span><span class="p">);</span>
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<div class="prompt input_prompt">In [18]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">([</span><span class="n">ms</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">clusters</span><span class="p">[</span><span class="n">clusts12</span><span class="p">[</span><span class="mi">1</span><span class="p">]]],</span><span class="n">molsPerRow</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
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<div class="prompt output_prompt">Out[18]:</div>
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
" />
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [19]:</div>
<div class="inner_cell">
<div class="input_area">
<div class="highlight hl-ipython3"><pre><span></span><span class="n">hist</span><span class="p">(</span><span class="n">intracluster_similarities</span><span class="p">(</span><span class="n">clusters</span><span class="p">[</span><span class="n">clusts12</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span><span class="n">fps</span><span class="p">));</span>
<span class="n">xlabel</span><span class="p">(</span><span class="s1">'Similarity'</span><span class="p">);</span>
</pre></div>
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<p>Both clusters are clearly include related compounds</p>
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<h2 id="Decreasing-the-sphere-radius">Decreasing the sphere radius<a class="anchor-link" href="#Decreasing-the-sphere-radius">¶</a></h2>
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<p>What about if we make the clusters tighter by decreasing the threshold distance?</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">thresh</span> <span class="o">=</span> <span class="mf">0.35</span> <span class="c1"># <- minimum distance between cluster centroids</span>
<span class="n">picks</span> <span class="o">=</span> <span class="n">lp</span><span class="o">.</span><span class="n">LazyBitVectorPick</span><span class="p">(</span><span class="n">fps</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">fps</span><span class="p">),</span><span class="n">thresh</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">picks</span><span class="p">))</span>
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<pre>1832
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">clusters</span> <span class="o">=</span> <span class="n">assignPointsToClusters</span><span class="p">(</span><span class="n">picks</span><span class="p">,</span><span class="n">fps</span><span class="p">)</span>
<span class="n">hist</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">clusters</span><span class="p">[</span><span class="n">x</span><span class="p">])</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">clusters</span><span class="p">]);</span>
<span class="n">xlabel</span><span class="p">(</span><span class="s1">'cluster size'</span><span class="p">);</span>
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<p>We've got more clusters and they are smaller. No big surprise</p>
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<p>And let's look at a couple of those</p>
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<div class="prompt input_prompt">In [22]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">clusts12</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">clusters</span> <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">clusters</span><span class="p">[</span><span class="n">x</span><span class="p">])</span><span class="o">==</span><span class="mi">12</span><span class="p">]</span>
<span class="nb">len</span><span class="p">(</span><span class="n">clusts12</span><span class="p">)</span>
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<div class="prompt output_prompt">Out[22]:</div>
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<pre>17</pre>
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<div class="prompt input_prompt">In [23]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">([</span><span class="n">ms</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">clusters</span><span class="p">[</span><span class="n">clusts12</span><span class="p">[</span><span class="mi">0</span><span class="p">]]],</span><span class="n">molsPerRow</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
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<div class="prompt output_prompt">Out[23]:</div>
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<img 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<div class="prompt input_prompt">In [24]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">hist</span><span class="p">(</span><span class="n">intracluster_similarities</span><span class="p">(</span><span class="n">clusters</span><span class="p">[</span><span class="n">clusts12</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span><span class="n">fps</span><span class="p">));</span>
<span class="n">xlabel</span><span class="p">(</span><span class="s1">'Similarity'</span><span class="p">);</span>
</pre></div>
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<div class="prompt input_prompt">In [25]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">([</span><span class="n">ms</span><span class="p">[</span><span class="n">x</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">clusters</span><span class="p">[</span><span class="n">clusts12</span><span class="p">[</span><span class="mi">1</span><span class="p">]]],</span><span class="n">molsPerRow</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
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<div class="prompt output_prompt">Out[25]:</div>
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<div class="prompt input_prompt">In [26]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">hist</span><span class="p">(</span><span class="n">intracluster_similarities</span><span class="p">(</span><span class="n">clusters</span><span class="p">[</span><span class="n">clusts12</span><span class="p">[</span><span class="mi">1</span><span class="p">]],</span><span class="n">fps</span><span class="p">));</span>
<span class="n">xlabel</span><span class="p">(</span><span class="s1">'Similarity'</span><span class="p">);</span>
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<p>Again, those mostly look quite similar to each other, maybe even more similar than before?</p>
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<h2 id="Impact-of-sphere-radius-on-the-number-of-clusters">Impact of sphere radius on the number of clusters<a class="anchor-link" href="#Impact-of-sphere-radius-on-the-number-of-clusters">¶</a></h2>
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<p>Look at the number of clusters as a function of the threshold</p>
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<div class="prompt input_prompt">In [27]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="n">arange</span><span class="p">(</span><span class="mf">0.65</span><span class="p">,</span><span class="mf">0.05</span><span class="p">,</span><span class="o">-</span><span class="mf">0.05</span><span class="p">):</span>
<span class="n">tpicks</span> <span class="o">=</span> <span class="n">lp</span><span class="o">.</span><span class="n">LazyBitVectorPick</span><span class="p">(</span><span class="n">fps</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">fps</span><span class="p">),</span><span class="n">thresh</span><span class="p">)</span>
<span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">thresh</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">tpicks</span><span class="p">)])</span>
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<div class="prompt input_prompt">In [28]:</div>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">scatter</span><span class="p">([</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">results</span><span class="p">],[</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">results</span><span class="p">]);</span>
<span class="n">ylabel</span><span class="p">(</span><span class="s1">'number of clusters'</span><span class="p">)</span>
<span class="n">xlabel</span><span class="p">(</span><span class="s1">'distance threshold'</span><span class="p">)</span>
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<pre>Text(0.5, 0, 'distance threshold')</pre>
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<h1 id="Trying-a-larger-dataset">Trying a larger dataset<a class="anchor-link" href="#Trying-a-larger-dataset">¶</a></h1>
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<p>I said that Roger's implementation was efficient, but the dataset above wasn't all that big. Let's try a larger one.</p>
<p>Here we'll use the full set of compounds I grabbed data for when building the <a href="http://rdkit.blogspot.com/2019/10/a-new-lessel-and-briem-like-dataset.html">new "Lessel and Briem" datasets</a>.</p>
<p>As an aside, this is also a nice opportunity to demonstrate using the <code>MultithreadedSmilesMolSupplier</code> that Shrey Aryan added to the RDKit as part of his <a href="https://github.com/rdkit/rdkit/pull/3363">2020 Google Summer of Code project</a>. This new supplier allows molecules to be constructed in parallel and can, in some situations, really speed things up.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">fps</span> <span class="o">=</span> <span class="p">[</span><span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">GetMorganFingerprintAsBitVect</span><span class="p">(</span><span class="n">m</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2048</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> \
<span class="n">Chem</span><span class="o">.</span><span class="n">MultithreadedSmilesMolSupplier</span><span class="p">(</span><span class="s1">'../data/BLSets_actives.txt'</span><span class="p">,</span><span class="n">numWriterThreads</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span><span class="n">delimiter</span><span class="o">=</span><span class="s1">'</span><span class="se">\t</span><span class="s1">'</span><span class="p">)</span> <span class="k">if</span> <span class="n">m</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">]</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"That took </span><span class="si">{</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span> <span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> seconds to build </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">fps</span><span class="p">)</span><span class="si">}</span><span class="s2"> fingerprints"</span><span class="p">)</span>
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<pre>That took 6.02 seconds to build 91663 fingerprints
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<p>Running that single threaded (i.e. using a normal <code>SmilesMolSupplier</code>) took 16.8 seconds on my machine.</p>
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<p>Pick the cluster centroids:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">lp</span> <span class="o">=</span> <span class="n">rdSimDivPickers</span><span class="o">.</span><span class="n">LeaderPicker</span><span class="p">()</span>
<span class="n">thresh</span> <span class="o">=</span> <span class="mf">0.65</span> <span class="c1"># <- minimum distance between cluster centroids</span>
<span class="n">picks</span> <span class="o">=</span> <span class="n">lp</span><span class="o">.</span><span class="n">LazyBitVectorPick</span><span class="p">(</span><span class="n">fps</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">fps</span><span class="p">),</span><span class="n">thresh</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">picks</span><span class="p">))</span>
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<pre>2997
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<p>How long does that take?</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="o">%</span><span class="k">timeit</span> lp.LazyBitVectorPick(fps,len(fps),thresh)
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<pre>5.14 s ± 320 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">clusters</span> <span class="o">=</span> <span class="n">assignPointsToClusters</span><span class="p">(</span><span class="n">picks</span><span class="p">,</span><span class="n">fps</span><span class="p">)</span>
<span class="n">t2</span><span class="o">=</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"That took </span><span class="si">{</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span> <span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> seconds"</span><span class="p">)</span>
<span class="n">hist</span><span class="p">([</span><span class="nb">len</span><span class="p">(</span><span class="n">clusters</span><span class="p">[</span><span class="n">x</span><span class="p">])</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">clusters</span><span class="p">]);</span>
<span class="n">xlabel</span><span class="p">(</span><span class="s1">'cluster size'</span><span class="p">);</span>
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<pre>That took 43.34 seconds
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<p>And, finally, look at the number of clusters and clustering time as a function of the sphere radius</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">results</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="p">(</span><span class="mf">0.2</span><span class="p">,</span><span class="mf">0.3</span><span class="p">,</span><span class="mf">0.4</span><span class="p">,</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.6</span><span class="p">,</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.8</span><span class="p">):</span>
<span class="n">lp</span> <span class="o">=</span> <span class="n">rdSimDivPickers</span><span class="o">.</span><span class="n">LeaderPicker</span><span class="p">()</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">picks</span> <span class="o">=</span> <span class="n">lp</span><span class="o">.</span><span class="n">LazyBitVectorPick</span><span class="p">(</span><span class="n">fps</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">fps</span><span class="p">),</span><span class="n">thresh</span><span class="p">)</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"distance threshold </span><span class="si">{</span><span class="n">thresh</span><span class="si">:</span><span class="s2"> .2f</span><span class="si">}</span><span class="s2">, </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">picks</span><span class="p">)</span><span class="si">}</span><span class="s2"> clusters in </span><span class="si">{</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span> <span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> seconds"</span><span class="p">)</span>
<span class="n">results</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">thresh</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">picks</span><span class="p">),</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span><span class="p">))</span>
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<pre>distance threshold 0.20, 34535 clusters in 66.25 seconds
distance threshold 0.30, 20627 clusters in 37.51 seconds
distance threshold 0.40, 11799 clusters in 21.47 seconds
distance threshold 0.50, 6811 clusters in 12.48 seconds
distance threshold 0.60, 4047 clusters in 7.02 seconds
distance threshold 0.70, 2021 clusters in 3.07 seconds
distance threshold 0.80, 558 clusters in 0.51 seconds
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<p>Those two track nicely with each other; more clusters = longer run time:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">subplots</span><span class="p">()</span>
<span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">results</span><span class="p">],[</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">results</span><span class="p">]);</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">'Sphere radius'</span><span class="p">);</span>
<span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Num clusters'</span><span class="p">);</span>
<span class="n">ax2</span> <span class="o">=</span> <span class="n">ax</span><span class="o">.</span><span class="n">twinx</span><span class="p">()</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">plot</span><span class="p">([</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">results</span><span class="p">],[</span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">results</span><span class="p">],</span><span class="n">c</span><span class="o">=</span><span class="s1">'r'</span><span class="p">);</span>
<span class="n">ax2</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">'Time (s)'</span><span class="p">);</span>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com1tag:blogger.com,1999:blog-684443317148892945.post-1052092521865395592020-10-22T19:45:00.000-07:002020-10-22T19:45:30.197-07:00Molecule highlighting and R-group decomposition<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>This one was inspired by a conversation that happened at the 2020 RDKit (virtual) UGM.</p>
<p>During Dominique Sydow's presentation she showed some pictures of molecules with some regions of the molecule highlighted (in her case to indicate which kinase pocket they interact with). Dominique had created the images by hand, but I wanted to explore what's possible using the 2020.09 RDKit release.</p>
<p>What this post is going to demonstrate is doing R-group decomposition (RGD) on a set of molecules that share a common scaffold, generating coordinates for those molecules that are aligned to the scaffold, and generating images of the molecules where the R groups are colored to make them easy to pick out.</p>
<p>The final images we create will look like this:</p>
<p><img src="data:image/png;base64,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" alt="image.png"></p>
<p>The rest of this post will go through the steps to create images like this.</p>
<p>Let's get started</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">Chem</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">Draw</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">IPythonConsole</span>
<span class="n">IPythonConsole</span><span class="o">.</span><span class="n">molSize</span><span class="o">=</span><span class="p">(</span><span class="mi">450</span><span class="p">,</span><span class="mi">350</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">rdRGroupDecomposition</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">rdqueries</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">rdDepictor</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">rdMolDraw2D</span>
<span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">Geometry</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">SetPreferCoordGen</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">SVG</span><span class="p">,</span><span class="n">Image</span>
<span class="kn">from</span> <span class="nn">ipywidgets</span> <span class="kn">import</span> <span class="n">interact</span>
<span class="kn">import</span> <span class="nn">rdkit</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rdkit</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
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<pre>2020.09.1
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<p>Start by reading in the data we will use. This is a collection of ChEMBL compounds with Ki data measured for CDK2. The dataset includes compounds from a number of different documents and, since these are medchem papers, many of the documents contain groups of compounds that share a common scaffold.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'../data/cdk2_rgd_dataset.csv'</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>assay_id</th>
<th>doc_id</th>
<th>description</th>
<th>assay_organism</th>
<th>assay_chembl_id</th>
<th>aidx</th>
<th>pref_name</th>
<th>activity_id</th>
<th>molregno</th>
<th>standard_relation</th>
<th>...</th>
<th>src_id (#1)</th>
<th>type</th>
<th>relation</th>
<th>value</th>
<th>units</th>
<th>text_value</th>
<th>standard_text_value</th>
<th>standard_inchi_key</th>
<th>canonical_smiles</th>
<th>compound_chembl_id</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>50641</td>
<td>17759</td>
<td>Inhibitory activity against human CDK2 (Cyclin...</td>
<td>NaN</td>
<td>CHEMBL658107</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>265814</td>
<td>68026</td>
<td>></td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>></td>
<td>20.00</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>RPXWUUDZINQPTJ-UHFFFAOYSA-N</td>
<td>CNc1nccc(n1)c2sc(C)nc2C</td>
<td>CHEMBL46474</td>
</tr>
<tr>
<th>1</th>
<td>50641</td>
<td>17759</td>
<td>Inhibitory activity against human CDK2 (Cyclin...</td>
<td>NaN</td>
<td>CHEMBL658107</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>265817</td>
<td>67880</td>
<td>=</td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>=</td>
<td>0.14</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>GDZTURHUKDAJGD-UHFFFAOYSA-N</td>
<td>Cc1nc(C)c(s1)c2ccnc(Nc3ccc(O)cc3)n2</td>
<td>CHEMBL442957</td>
</tr>
<tr>
<th>2</th>
<td>50641</td>
<td>17759</td>
<td>Inhibitory activity against human CDK2 (Cyclin...</td>
<td>NaN</td>
<td>CHEMBL658107</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>267078</td>
<td>67751</td>
<td>=</td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>=</td>
<td>6.50</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>CTFDMGIBHFQWKB-UHFFFAOYSA-N</td>
<td>Cc1nc(C)c(s1)c2ccnc(N)n2</td>
<td>CHEMBL47302</td>
</tr>
<tr>
<th>3</th>
<td>50641</td>
<td>17759</td>
<td>Inhibitory activity against human CDK2 (Cyclin...</td>
<td>NaN</td>
<td>CHEMBL658107</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>267081</td>
<td>67782</td>
<td>=</td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>=</td>
<td>1.20</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>HOKDXVAONYXHJK-UHFFFAOYSA-N</td>
<td>Cc1nc(C)c(s1)c2ccnc(Nc3ccccc3F)n2</td>
<td>CHEMBL297447</td>
</tr>
<tr>
<th>4</th>
<td>50641</td>
<td>17759</td>
<td>Inhibitory activity against human CDK2 (Cyclin...</td>
<td>NaN</td>
<td>CHEMBL658107</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>267084</td>
<td>67961</td>
<td>=</td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>=</td>
<td>0.11</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>XNKSRGHGPSHYIW-UHFFFAOYSA-N</td>
<td>CNc1nc(C)c(s1)c2ccnc(Nc3cccc(O)c3)n2</td>
<td>CHEMBL44119</td>
</tr>
</tbody>
</table>
<p>5 rows × 28 columns</p>
</div>
</div>
</div>
</div>
</div>
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<p>We pick a group of compounds by selecting all the rows with a given assay ID:</p>
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<div class="prompt input_prompt">In [3]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_doc1</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">assay_chembl_id</span><span class="o">==</span><span class="s1">'CHEMBL827377'</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_doc1</span><span class="p">))</span>
<span class="n">df_doc1</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
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<pre>91
</pre>
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<div class="prompt output_prompt">Out[3]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>assay_id</th>
<th>doc_id</th>
<th>description</th>
<th>assay_organism</th>
<th>assay_chembl_id</th>
<th>aidx</th>
<th>pref_name</th>
<th>activity_id</th>
<th>molregno</th>
<th>standard_relation</th>
<th>...</th>
<th>src_id (#1)</th>
<th>type</th>
<th>relation</th>
<th>value</th>
<th>units</th>
<th>text_value</th>
<th>standard_text_value</th>
<th>standard_inchi_key</th>
<th>canonical_smiles</th>
<th>compound_chembl_id</th>
</tr>
</thead>
<tbody>
<tr>
<th>47</th>
<td>302524</td>
<td>21080</td>
<td>Binding affinity for human cyclin-dependent ki...</td>
<td>Homo sapiens</td>
<td>CHEMBL827377</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>1438958</td>
<td>305637</td>
<td>></td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>></td>
<td>19.95</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>TWQUOUJLNRGSRZ-UHFFFAOYSA-N</td>
<td>Cc1ccc2c(c3ccnc(Nc4cccc(c4)C(F)(F)F)n3)c(nn2n1...</td>
<td>CHEMBL182493</td>
</tr>
<tr>
<th>48</th>
<td>302524</td>
<td>21080</td>
<td>Binding affinity for human cyclin-dependent ki...</td>
<td>Homo sapiens</td>
<td>CHEMBL827377</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>1438962</td>
<td>305651</td>
<td>></td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>></td>
<td>19.95</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>CYHPFZLFUXOCJJ-UHFFFAOYSA-N</td>
<td>Cc1ccc2c(c3ccnc(Nc4ccc(F)c(F)c4)n3)c(nn2n1)c5c...</td>
<td>CHEMBL182326</td>
</tr>
<tr>
<th>49</th>
<td>302524</td>
<td>21080</td>
<td>Binding affinity for human cyclin-dependent ki...</td>
<td>Homo sapiens</td>
<td>CHEMBL827377</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>1439061</td>
<td>305664</td>
<td>></td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>></td>
<td>19.95</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>MYSOMHSTKVRJRA-UHFFFAOYSA-N</td>
<td>Cc1ccc2c(c3ccnc(Nc4ccc5OCCOc5c4)n3)c(nn2n1)c6c...</td>
<td>CHEMBL183064</td>
</tr>
<tr>
<th>50</th>
<td>302524</td>
<td>21080</td>
<td>Binding affinity for human cyclin-dependent ki...</td>
<td>Homo sapiens</td>
<td>CHEMBL827377</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>1439063</td>
<td>305674</td>
<td>></td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>></td>
<td>19.95</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>VUGNSTAXWJUVEZ-UHFFFAOYSA-N</td>
<td>Cc1ccc2c(c3ccnc(Nc4ccc(Cl)c(c4)C(F)(F)F)n3)c(n...</td>
<td>CHEMBL361038</td>
</tr>
<tr>
<th>51</th>
<td>302524</td>
<td>21080</td>
<td>Binding affinity for human cyclin-dependent ki...</td>
<td>Homo sapiens</td>
<td>CHEMBL827377</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>1439065</td>
<td>305687</td>
<td>=</td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>=</td>
<td>3.98</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>BWBMBCPGRIOUNV-UHFFFAOYSA-N</td>
<td>C1CC1c2nn3ncccc3c2c4ccnc(Nc5ccccc5)n4</td>
<td>CHEMBL362296</td>
</tr>
</tbody>
</table>
<p>5 rows × 28 columns</p>
</div>
</div>
</div>
</div>
</div>
</div>
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<p>Look at some of the compounds:</p>
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<div class="prompt input_prompt">In [4]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rdDepictor</span><span class="o">.</span><span class="n">SetPreferCoordGen</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">smis</span> <span class="o">=</span> <span class="n">df_doc1</span><span class="p">[</span><span class="s1">'canonical_smiles'</span><span class="p">]</span>
<span class="n">cids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df_doc1</span><span class="o">.</span><span class="n">compound_chembl_id</span><span class="p">)</span>
<span class="n">ms</span> <span class="o">=</span> <span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">smis</span><span class="p">]</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">:</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">Compute2DCoords</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">(</span><span class="n">ms</span><span class="p">[:</span><span class="mi">12</span><span class="p">],</span><span class="n">legends</span><span class="o">=</span><span class="n">cids</span><span class="p">,</span><span class="n">molsPerRow</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
</pre></div>
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<img src="data:image/png;base64,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<p>Define a core. I'm doing this manually and am only specifically labeling four of the seven R-groups in this set of molecules. The others will be labelled automatically by the RGD code.</p>
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<div class="prompt input_prompt">In [5]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">core</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'c1cc(-c2c([*:1])nn3nc([*:2])ccc23)nc(N(c2ccc([*:4])c([*:3])c2))n1'</span><span class="p">)</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">SetPreferCoordGen</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">Compute2DCoords</span><span class="p">(</span><span class="n">core</span><span class="p">)</span>
<span class="n">core</span>
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<div class="prompt output_prompt">Out[5]:</div>
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<p>Some pre-processing work we need to do:</p>
<ul>
<li>convert the dummy atoms in the scaffold into query atoms that match anything</li>
<li>add hydrogens to the molecules</li>
<li>select only the subset of molecules which match the core</li>
<li>set a property on each atom which is used to track its original index (we use this later in the RGD analysis)</li>
</ul>
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<div class="prompt input_prompt">In [6]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ps</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">AdjustQueryParameters</span><span class="o">.</span><span class="n">NoAdjustments</span><span class="p">()</span>
<span class="n">ps</span><span class="o">.</span><span class="n">makeDummiesQueries</span><span class="o">=</span><span class="kc">True</span>
<span class="n">qcore</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">AdjustQueryProperties</span><span class="p">(</span><span class="n">core</span><span class="p">,</span><span class="n">ps</span><span class="p">)</span>
<span class="n">mhs</span> <span class="o">=</span> <span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">AddHs</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">addCoords</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">]</span>
<span class="n">mms</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">mhs</span> <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">HasSubstructMatch</span><span class="p">(</span><span class="n">qcore</span><span class="p">)]</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">mms</span><span class="p">:</span>
<span class="k">for</span> <span class="n">atom</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">GetAtoms</span><span class="p">():</span>
<span class="n">atom</span><span class="o">.</span><span class="n">SetIntProp</span><span class="p">(</span><span class="s2">"SourceAtomIdx"</span><span class="p">,</span><span class="n">atom</span><span class="o">.</span><span class="n">GetIdx</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mhs</span><span class="p">),</span><span class="nb">len</span><span class="p">(</span><span class="n">mms</span><span class="p">))</span>
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<pre>91 91
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<p>Now do the actual RGD:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rdkit</span><span class="o">.</span><span class="n">RDLogger</span><span class="o">.</span><span class="n">DisableLog</span><span class="p">(</span><span class="s1">'rdApp.warning'</span><span class="p">)</span>
<span class="n">groups</span><span class="p">,</span><span class="n">_</span> <span class="o">=</span> <span class="n">rdRGroupDecomposition</span><span class="o">.</span><span class="n">RGroupDecompose</span><span class="p">([</span><span class="n">qcore</span><span class="p">],</span><span class="n">mms</span><span class="p">,</span><span class="n">asSmiles</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">asRows</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
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<p>This is the function that actually does the work of generating aligned coordinates and creating the image with highlighted R groups</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="k">def</span> <span class="nf">highlight_rgroups</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">row</span><span class="p">,</span><span class="n">core</span><span class="p">,</span><span class="n">width</span><span class="o">=</span><span class="mi">350</span><span class="p">,</span><span class="n">height</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span>
<span class="n">fillRings</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">legend</span><span class="o">=</span><span class="s2">""</span><span class="p">,</span>
<span class="n">sourceIdxProperty</span><span class="o">=</span><span class="s2">"SourceAtomIdx"</span><span class="p">,</span>
<span class="n">lbls</span><span class="o">=</span><span class="p">(</span><span class="s1">'R1'</span><span class="p">,</span><span class="s1">'R2'</span><span class="p">,</span><span class="s1">'R3'</span><span class="p">,</span><span class="s1">'R4'</span><span class="p">)):</span>
<span class="c1"># copy the molecule and core</span>
<span class="n">mol</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">Mol</span><span class="p">(</span><span class="n">mol</span><span class="p">)</span>
<span class="n">core</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">Mol</span><span class="p">(</span><span class="n">core</span><span class="p">)</span>
<span class="c1"># -------------------------------------------</span>
<span class="c1"># include the atom map numbers in the substructure search in order to </span>
<span class="c1"># try to ensure a good alignment of the molecule to symmetric cores</span>
<span class="k">for</span> <span class="n">at</span> <span class="ow">in</span> <span class="n">core</span><span class="o">.</span><span class="n">GetAtoms</span><span class="p">():</span>
<span class="k">if</span> <span class="n">at</span><span class="o">.</span><span class="n">GetAtomMapNum</span><span class="p">():</span>
<span class="n">at</span><span class="o">.</span><span class="n">ExpandQuery</span><span class="p">(</span><span class="n">rdqueries</span><span class="o">.</span><span class="n">IsotopeEqualsQueryAtom</span><span class="p">(</span><span class="mi">200</span><span class="o">+</span><span class="n">at</span><span class="o">.</span><span class="n">GetAtomMapNum</span><span class="p">()))</span>
<span class="k">for</span> <span class="n">lbl</span> <span class="ow">in</span> <span class="n">row</span><span class="p">:</span>
<span class="k">if</span> <span class="n">lbl</span><span class="o">==</span><span class="s1">'Core'</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">rg</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="n">lbl</span><span class="p">]</span>
<span class="k">for</span> <span class="n">at</span> <span class="ow">in</span> <span class="n">rg</span><span class="o">.</span><span class="n">GetAtoms</span><span class="p">():</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">at</span><span class="o">.</span><span class="n">GetAtomicNum</span><span class="p">()</span> <span class="ow">and</span> <span class="n">at</span><span class="o">.</span><span class="n">GetAtomMapNum</span><span class="p">()</span> <span class="ow">and</span> \
<span class="n">at</span><span class="o">.</span><span class="n">HasProp</span><span class="p">(</span><span class="s1">'dummyLabel'</span><span class="p">)</span> <span class="ow">and</span> <span class="n">at</span><span class="o">.</span><span class="n">GetProp</span><span class="p">(</span><span class="s1">'dummyLabel'</span><span class="p">)</span><span class="o">==</span><span class="n">lbl</span><span class="p">:</span>
<span class="c1"># attachment point. the atoms connected to this</span>
<span class="c1"># should be from the molecule</span>
<span class="k">for</span> <span class="n">nbr</span> <span class="ow">in</span> <span class="n">at</span><span class="o">.</span><span class="n">GetNeighbors</span><span class="p">():</span>
<span class="k">if</span> <span class="n">nbr</span><span class="o">.</span><span class="n">HasProp</span><span class="p">(</span><span class="n">sourceIdxProperty</span><span class="p">):</span>
<span class="n">mAt</span> <span class="o">=</span> <span class="n">mol</span><span class="o">.</span><span class="n">GetAtomWithIdx</span><span class="p">(</span><span class="n">nbr</span><span class="o">.</span><span class="n">GetIntProp</span><span class="p">(</span><span class="n">sourceIdxProperty</span><span class="p">))</span>
<span class="k">if</span> <span class="n">mAt</span><span class="o">.</span><span class="n">GetIsotope</span><span class="p">():</span>
<span class="n">mAt</span><span class="o">.</span><span class="n">SetIntProp</span><span class="p">(</span><span class="s1">'_OrigIsotope'</span><span class="p">,</span><span class="n">mAt</span><span class="o">.</span><span class="n">GetIsotope</span><span class="p">())</span>
<span class="n">mAt</span><span class="o">.</span><span class="n">SetIsotope</span><span class="p">(</span><span class="mi">200</span><span class="o">+</span><span class="n">at</span><span class="o">.</span><span class="n">GetAtomMapNum</span><span class="p">())</span>
<span class="c1"># remove unmapped hs so that they don't mess up the depiction</span>
<span class="n">rhps</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">RemoveHsParameters</span><span class="p">()</span>
<span class="n">rhps</span><span class="o">.</span><span class="n">removeMapped</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">tmol</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">RemoveHs</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">rhps</span><span class="p">)</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">GenerateDepictionMatching2DStructure</span><span class="p">(</span><span class="n">tmol</span><span class="p">,</span><span class="n">core</span><span class="p">)</span>
<span class="n">oldNewAtomMap</span><span class="o">=</span><span class="p">{}</span>
<span class="c1"># reset the original isotope values and account for the fact that</span>
<span class="c1"># removing the Hs changed atom indices</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">at</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">tmol</span><span class="o">.</span><span class="n">GetAtoms</span><span class="p">()):</span>
<span class="k">if</span> <span class="n">at</span><span class="o">.</span><span class="n">HasProp</span><span class="p">(</span><span class="n">sourceIdxProperty</span><span class="p">):</span>
<span class="n">oldNewAtomMap</span><span class="p">[</span><span class="n">at</span><span class="o">.</span><span class="n">GetIntProp</span><span class="p">(</span><span class="n">sourceIdxProperty</span><span class="p">)]</span> <span class="o">=</span> <span class="n">i</span>
<span class="k">if</span> <span class="n">at</span><span class="o">.</span><span class="n">HasProp</span><span class="p">(</span><span class="s2">"_OrigIsotope"</span><span class="p">):</span>
<span class="n">at</span><span class="o">.</span><span class="n">SetIsotope</span><span class="p">(</span><span class="n">at</span><span class="o">.</span><span class="n">GetIntProp</span><span class="p">(</span><span class="s2">"_OrigIsotope"</span><span class="p">))</span>
<span class="n">at</span><span class="o">.</span><span class="n">ClearProp</span><span class="p">(</span><span class="s2">"_OrigIsotope"</span><span class="p">)</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">at</span><span class="o">.</span><span class="n">SetIsotope</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
<span class="c1"># ------------------</span>
<span class="c1"># set up our colormap</span>
<span class="c1"># the three choices here are all "colorblind" colormaps</span>
<span class="c1"># "Tol" colormap from https://davidmathlogic.com/colorblind</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">51</span><span class="p">,</span><span class="mi">34</span><span class="p">,</span><span class="mi">136</span><span class="p">),(</span><span class="mi">17</span><span class="p">,</span><span class="mi">119</span><span class="p">,</span><span class="mi">51</span><span class="p">),(</span><span class="mi">68</span><span class="p">,</span><span class="mi">170</span><span class="p">,</span><span class="mi">153</span><span class="p">),(</span><span class="mi">136</span><span class="p">,</span><span class="mi">204</span><span class="p">,</span><span class="mi">238</span><span class="p">),(</span><span class="mi">221</span><span class="p">,</span><span class="mi">204</span><span class="p">,</span><span class="mi">119</span><span class="p">),(</span><span class="mi">204</span><span class="p">,</span><span class="mi">102</span><span class="p">,</span><span class="mi">119</span><span class="p">),(</span><span class="mi">170</span><span class="p">,</span><span class="mi">68</span><span class="p">,</span><span class="mi">153</span><span class="p">),(</span><span class="mi">136</span><span class="p">,</span><span class="mi">34</span><span class="p">,</span><span class="mi">85</span><span class="p">)]</span>
<span class="c1"># "IBM" colormap from https://davidmathlogic.com/colorblind</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">100</span><span class="p">,</span><span class="mi">143</span><span class="p">,</span><span class="mi">255</span><span class="p">),(</span><span class="mi">120</span><span class="p">,</span><span class="mi">94</span><span class="p">,</span><span class="mi">240</span><span class="p">),(</span><span class="mi">220</span><span class="p">,</span><span class="mi">38</span><span class="p">,</span><span class="mi">127</span><span class="p">),(</span><span class="mi">254</span><span class="p">,</span><span class="mi">97</span><span class="p">,</span><span class="mi">0</span><span class="p">),(</span><span class="mi">255</span><span class="p">,</span><span class="mi">176</span><span class="p">,</span><span class="mi">0</span><span class="p">)]</span>
<span class="c1"># Okabe_Ito colormap from https://jfly.uni-koeln.de/color/</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[(</span><span class="mi">230</span><span class="p">,</span><span class="mi">159</span><span class="p">,</span><span class="mi">0</span><span class="p">),(</span><span class="mi">86</span><span class="p">,</span><span class="mi">180</span><span class="p">,</span><span class="mi">233</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="mi">158</span><span class="p">,</span><span class="mi">115</span><span class="p">),(</span><span class="mi">240</span><span class="p">,</span><span class="mi">228</span><span class="p">,</span><span class="mi">66</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="mi">114</span><span class="p">,</span><span class="mi">178</span><span class="p">),(</span><span class="mi">213</span><span class="p">,</span><span class="mi">94</span><span class="p">,</span><span class="mi">0</span><span class="p">),(</span><span class="mi">204</span><span class="p">,</span><span class="mi">121</span><span class="p">,</span><span class="mi">167</span><span class="p">)]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">colors</span><span class="p">):</span>
<span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">y</span><span class="o">/</span><span class="mi">255</span> <span class="k">for</span> <span class="n">y</span> <span class="ow">in</span> <span class="n">x</span><span class="p">)</span>
<span class="c1">#----------------------</span>
<span class="c1"># Identify and store which atoms, bonds, and rings we'll be highlighting</span>
<span class="n">highlightatoms</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="n">highlightbonds</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="n">atomrads</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">widthmults</span> <span class="o">=</span> <span class="p">{}</span>
<span class="n">rings</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">lbl</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">lbls</span><span class="p">):</span>
<span class="n">color</span> <span class="o">=</span> <span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="o">%</span><span class="k">len</span>(colors)]
<span class="n">rquery</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="n">lbl</span><span class="p">]</span>
<span class="n">Chem</span><span class="o">.</span><span class="n">GetSSSR</span><span class="p">(</span><span class="n">rquery</span><span class="p">)</span>
<span class="n">rinfo</span> <span class="o">=</span> <span class="n">rquery</span><span class="o">.</span><span class="n">GetRingInfo</span><span class="p">()</span>
<span class="k">for</span> <span class="n">at</span> <span class="ow">in</span> <span class="n">rquery</span><span class="o">.</span><span class="n">GetAtoms</span><span class="p">():</span>
<span class="k">if</span> <span class="n">at</span><span class="o">.</span><span class="n">HasProp</span><span class="p">(</span><span class="n">sourceIdxProperty</span><span class="p">):</span>
<span class="n">origIdx</span> <span class="o">=</span> <span class="n">oldNewAtomMap</span><span class="p">[</span><span class="n">at</span><span class="o">.</span><span class="n">GetIntProp</span><span class="p">(</span><span class="n">sourceIdxProperty</span><span class="p">)]</span>
<span class="n">highlightatoms</span><span class="p">[</span><span class="n">origIdx</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">color</span><span class="p">)</span>
<span class="n">atomrads</span><span class="p">[</span><span class="n">origIdx</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.4</span>
<span class="k">if</span> <span class="n">fillRings</span><span class="p">:</span>
<span class="k">for</span> <span class="n">aring</span> <span class="ow">in</span> <span class="n">rinfo</span><span class="o">.</span><span class="n">AtomRings</span><span class="p">():</span>
<span class="n">tring</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">allFound</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">for</span> <span class="n">aid</span> <span class="ow">in</span> <span class="n">aring</span><span class="p">:</span>
<span class="n">at</span> <span class="o">=</span> <span class="n">rquery</span><span class="o">.</span><span class="n">GetAtomWithIdx</span><span class="p">(</span><span class="n">aid</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">at</span><span class="o">.</span><span class="n">HasProp</span><span class="p">(</span><span class="n">sourceIdxProperty</span><span class="p">):</span>
<span class="n">allFound</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">break</span>
<span class="n">tring</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">oldNewAtomMap</span><span class="p">[</span><span class="n">at</span><span class="o">.</span><span class="n">GetIntProp</span><span class="p">(</span><span class="n">sourceIdxProperty</span><span class="p">)])</span>
<span class="k">if</span> <span class="n">allFound</span><span class="p">:</span>
<span class="n">rings</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">tring</span><span class="p">,</span><span class="n">color</span><span class="p">))</span>
<span class="k">for</span> <span class="n">qbnd</span> <span class="ow">in</span> <span class="n">rquery</span><span class="o">.</span><span class="n">GetBonds</span><span class="p">():</span>
<span class="n">batom</span> <span class="o">=</span> <span class="n">qbnd</span><span class="o">.</span><span class="n">GetBeginAtom</span><span class="p">()</span>
<span class="n">eatom</span> <span class="o">=</span> <span class="n">qbnd</span><span class="o">.</span><span class="n">GetEndAtom</span><span class="p">()</span>
<span class="k">if</span> <span class="n">batom</span><span class="o">.</span><span class="n">HasProp</span><span class="p">(</span><span class="n">sourceIdxProperty</span><span class="p">)</span> <span class="ow">and</span> <span class="n">eatom</span><span class="o">.</span><span class="n">HasProp</span><span class="p">(</span><span class="n">sourceIdxProperty</span><span class="p">):</span>
<span class="n">origBnd</span> <span class="o">=</span> <span class="n">tmol</span><span class="o">.</span><span class="n">GetBondBetweenAtoms</span><span class="p">(</span><span class="n">oldNewAtomMap</span><span class="p">[</span><span class="n">batom</span><span class="o">.</span><span class="n">GetIntProp</span><span class="p">(</span><span class="n">sourceIdxProperty</span><span class="p">)],</span>
<span class="n">oldNewAtomMap</span><span class="p">[</span><span class="n">eatom</span><span class="o">.</span><span class="n">GetIntProp</span><span class="p">(</span><span class="n">sourceIdxProperty</span><span class="p">)])</span>
<span class="n">bndIdx</span> <span class="o">=</span> <span class="n">origBnd</span><span class="o">.</span><span class="n">GetIdx</span><span class="p">()</span>
<span class="n">highlightbonds</span><span class="p">[</span><span class="n">bndIdx</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">color</span><span class="p">)</span>
<span class="n">widthmults</span><span class="p">[</span><span class="n">bndIdx</span><span class="p">]</span> <span class="o">=</span> <span class="mi">2</span>
<span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DCairo</span><span class="p">(</span><span class="n">width</span><span class="p">,</span><span class="n">height</span><span class="p">)</span>
<span class="n">dos</span> <span class="o">=</span> <span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span>
<span class="n">dos</span><span class="o">.</span><span class="n">useBWAtomPalette</span><span class="p">()</span>
<span class="c1">#----------------------</span>
<span class="c1"># if we are filling rings, go ahead and do that first so that we draw</span>
<span class="c1"># the molecule on top of the filled rings</span>
<span class="k">if</span> <span class="n">fillRings</span> <span class="ow">and</span> <span class="n">rings</span><span class="p">:</span>
<span class="c1"># a hack to set the molecule scale</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMoleculeWithHighlights</span><span class="p">(</span><span class="n">tmol</span><span class="p">,</span><span class="n">legend</span><span class="p">,</span><span class="nb">dict</span><span class="p">(</span><span class="n">highlightatoms</span><span class="p">),</span>
<span class="nb">dict</span><span class="p">(</span><span class="n">highlightbonds</span><span class="p">),</span>
<span class="n">atomrads</span><span class="p">,</span><span class="n">widthmults</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">ClearDrawing</span><span class="p">()</span>
<span class="n">conf</span> <span class="o">=</span> <span class="n">tmol</span><span class="o">.</span><span class="n">GetConformer</span><span class="p">()</span>
<span class="k">for</span> <span class="p">(</span><span class="n">aring</span><span class="p">,</span><span class="n">color</span><span class="p">)</span> <span class="ow">in</span> <span class="n">rings</span><span class="p">:</span>
<span class="n">ps</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">aidx</span> <span class="ow">in</span> <span class="n">aring</span><span class="p">:</span>
<span class="n">pos</span> <span class="o">=</span> <span class="n">Geometry</span><span class="o">.</span><span class="n">Point2D</span><span class="p">(</span><span class="n">conf</span><span class="o">.</span><span class="n">GetAtomPosition</span><span class="p">(</span><span class="n">aidx</span><span class="p">))</span>
<span class="n">ps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">pos</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">SetFillPolys</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">SetColour</span><span class="p">(</span><span class="n">color</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawPolygon</span><span class="p">(</span><span class="n">ps</span><span class="p">)</span>
<span class="n">dos</span><span class="o">.</span><span class="n">clearBackground</span> <span class="o">=</span> <span class="kc">False</span>
<span class="c1">#----------------------</span>
<span class="c1"># now draw the molecule, with highlights:</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMoleculeWithHighlights</span><span class="p">(</span><span class="n">tmol</span><span class="p">,</span><span class="n">legend</span><span class="p">,</span><span class="nb">dict</span><span class="p">(</span><span class="n">highlightatoms</span><span class="p">),</span><span class="nb">dict</span><span class="p">(</span><span class="n">highlightbonds</span><span class="p">),</span>
<span class="n">atomrads</span><span class="p">,</span><span class="n">widthmults</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">png</span> <span class="o">=</span> <span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">()</span>
<span class="k">return</span> <span class="n">png</span>
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<p>Interactively try that out on all the molecules in our set:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nd">@interact</span><span class="p">(</span><span class="n">idx</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">ms</span><span class="p">)))</span>
<span class="k">def</span> <span class="nf">draw_it</span><span class="p">(</span><span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">mms</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="n">row</span> <span class="o">=</span> <span class="n">groups</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="k">return</span> <span class="n">Image</span><span class="p">(</span><span class="n">highlight_rgroups</span><span class="p">(</span><span class="n">m</span><span class="p">,</span><span class="n">row</span><span class="p">,</span><span class="n">qcore</span><span class="p">,</span><span class="n">lbls</span><span class="o">=</span><span class="p">(</span><span class="s1">'R1'</span><span class="p">,</span><span class="s1">'R2'</span><span class="p">,</span><span class="s1">'R3'</span><span class="p">,</span><span class="s1">'R4'</span><span class="p">)))</span>
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<p>It would be cool to do see multiple molecules at once. Unforunately <code>DrawMolsToGridImage()</code> doesn't support the multiple highlighting we're doing here (we decided that the API for that would just be too complex; this may change in the future if we can figure out a sensible API for it), so we have to manually combine the images. Fortunately the pillow package makes that easy:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span> <span class="k">as</span> <span class="n">pilImage</span>
<span class="kn">from</span> <span class="nn">io</span> <span class="kn">import</span> <span class="n">BytesIO</span>
<span class="k">def</span> <span class="nf">draw_multiple</span><span class="p">(</span><span class="n">ms</span><span class="p">,</span><span class="n">groups</span><span class="p">,</span><span class="n">qcore</span><span class="p">,</span><span class="n">lbls</span><span class="p">,</span><span class="n">legends</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">nPerRow</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span><span class="n">subImageSize</span><span class="o">=</span><span class="p">(</span><span class="mi">250</span><span class="p">,</span><span class="mi">200</span><span class="p">)):</span>
<span class="n">nRows</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ms</span><span class="p">)</span><span class="o">//</span><span class="n">nPerRow</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ms</span><span class="p">)</span><span class="o">%</span><span class="k">nPerRow</span>:
<span class="n">nRows</span><span class="o">+=</span><span class="mi">1</span>
<span class="n">nCols</span> <span class="o">=</span> <span class="n">nPerRow</span>
<span class="n">imgSize</span> <span class="o">=</span> <span class="p">(</span><span class="n">subImageSize</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="n">nCols</span><span class="p">,</span><span class="n">subImageSize</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="n">nRows</span><span class="p">)</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">pilImage</span><span class="o">.</span><span class="n">new</span><span class="p">(</span><span class="s1">'RGB'</span><span class="p">,</span><span class="n">imgSize</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">m</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ms</span><span class="p">):</span>
<span class="n">col</span> <span class="o">=</span> <span class="n">i</span><span class="o">%</span><span class="k">nPerRow</span>
<span class="n">row</span> <span class="o">=</span> <span class="n">i</span><span class="o">//</span><span class="n">nPerRow</span>
<span class="k">if</span> <span class="n">legends</span><span class="p">:</span>
<span class="n">legend</span> <span class="o">=</span> <span class="n">legends</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">legend</span> <span class="o">=</span> <span class="s1">''</span>
<span class="n">png</span> <span class="o">=</span> <span class="n">highlight_rgroups</span><span class="p">(</span><span class="n">m</span><span class="p">,</span><span class="n">groups</span><span class="p">[</span><span class="n">i</span><span class="p">],</span><span class="n">qcore</span><span class="p">,</span><span class="n">lbls</span><span class="o">=</span><span class="n">lbls</span><span class="p">,</span><span class="n">legend</span><span class="o">=</span><span class="n">legend</span><span class="p">,</span>
<span class="n">width</span><span class="o">=</span><span class="n">subImageSize</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">height</span><span class="o">=</span><span class="n">subImageSize</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">bio</span> <span class="o">=</span> <span class="n">BytesIO</span><span class="p">(</span><span class="n">png</span><span class="p">)</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">pilImage</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">bio</span><span class="p">)</span>
<span class="n">res</span><span class="o">.</span><span class="n">paste</span><span class="p">(</span><span class="n">img</span><span class="p">,</span><span class="n">box</span><span class="o">=</span><span class="p">(</span><span class="n">col</span><span class="o">*</span><span class="n">subImageSize</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">row</span><span class="o">*</span><span class="n">subImageSize</span><span class="p">[</span><span class="mi">1</span><span class="p">]))</span>
<span class="n">bio</span> <span class="o">=</span> <span class="n">BytesIO</span><span class="p">()</span>
<span class="n">res</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">bio</span><span class="p">,</span><span class="nb">format</span><span class="o">=</span><span class="s1">'PNG'</span><span class="p">)</span>
<span class="k">return</span> <span class="n">bio</span><span class="o">.</span><span class="n">getvalue</span><span class="p">()</span>
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<p>Now let's look at the first 16 molecules in the dataset:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Image</span><span class="p">(</span><span class="n">draw_multiple</span><span class="p">(</span><span class="n">mms</span><span class="p">[:</span><span class="mi">16</span><span class="p">],</span><span class="n">groups</span><span class="p">,</span><span class="n">qcore</span><span class="p">,(</span><span class="s1">'R1'</span><span class="p">,</span><span class="s1">'R2'</span><span class="p">,</span><span class="s1">'R3'</span><span class="p">,</span><span class="s1">'R4'</span><span class="p">),</span><span class="n">subImageSize</span><span class="o">=</span><span class="p">(</span><span class="mi">300</span><span class="p">,</span><span class="mi">250</span><span class="p">)))</span>
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
"
>
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<p>Repeat that analysis with the compounds from another document just to make sure we did everything sufficiently generally:</p>
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<div class="input">
<div class="prompt input_prompt">In [12]:</div>
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<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_doc2</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">assay_chembl_id</span><span class="o">==</span><span class="s1">'CHEMBL658107'</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_doc2</span><span class="p">))</span>
<span class="n">df_doc2</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
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<div class="output_subarea output_stream output_stdout output_text">
<pre>33
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<div class="output_area">
<div class="prompt output_prompt">Out[12]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>assay_id</th>
<th>doc_id</th>
<th>description</th>
<th>assay_organism</th>
<th>assay_chembl_id</th>
<th>aidx</th>
<th>pref_name</th>
<th>activity_id</th>
<th>molregno</th>
<th>standard_relation</th>
<th>...</th>
<th>src_id (#1)</th>
<th>type</th>
<th>relation</th>
<th>value</th>
<th>units</th>
<th>text_value</th>
<th>standard_text_value</th>
<th>standard_inchi_key</th>
<th>canonical_smiles</th>
<th>compound_chembl_id</th>
</tr>
</thead>
<tbody>
<tr>
<th>0</th>
<td>50641</td>
<td>17759</td>
<td>Inhibitory activity against human CDK2 (Cyclin...</td>
<td>NaN</td>
<td>CHEMBL658107</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>265814</td>
<td>68026</td>
<td>></td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>></td>
<td>20.00</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>RPXWUUDZINQPTJ-UHFFFAOYSA-N</td>
<td>CNc1nccc(n1)c2sc(C)nc2C</td>
<td>CHEMBL46474</td>
</tr>
<tr>
<th>1</th>
<td>50641</td>
<td>17759</td>
<td>Inhibitory activity against human CDK2 (Cyclin...</td>
<td>NaN</td>
<td>CHEMBL658107</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>265817</td>
<td>67880</td>
<td>=</td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>=</td>
<td>0.14</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>GDZTURHUKDAJGD-UHFFFAOYSA-N</td>
<td>Cc1nc(C)c(s1)c2ccnc(Nc3ccc(O)cc3)n2</td>
<td>CHEMBL442957</td>
</tr>
<tr>
<th>2</th>
<td>50641</td>
<td>17759</td>
<td>Inhibitory activity against human CDK2 (Cyclin...</td>
<td>NaN</td>
<td>CHEMBL658107</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>267078</td>
<td>67751</td>
<td>=</td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>=</td>
<td>6.50</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>CTFDMGIBHFQWKB-UHFFFAOYSA-N</td>
<td>Cc1nc(C)c(s1)c2ccnc(N)n2</td>
<td>CHEMBL47302</td>
</tr>
<tr>
<th>3</th>
<td>50641</td>
<td>17759</td>
<td>Inhibitory activity against human CDK2 (Cyclin...</td>
<td>NaN</td>
<td>CHEMBL658107</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>267081</td>
<td>67782</td>
<td>=</td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>=</td>
<td>1.20</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>HOKDXVAONYXHJK-UHFFFAOYSA-N</td>
<td>Cc1nc(C)c(s1)c2ccnc(Nc3ccccc3F)n2</td>
<td>CHEMBL297447</td>
</tr>
<tr>
<th>4</th>
<td>50641</td>
<td>17759</td>
<td>Inhibitory activity against human CDK2 (Cyclin...</td>
<td>NaN</td>
<td>CHEMBL658107</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>267084</td>
<td>67961</td>
<td>=</td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>=</td>
<td>0.11</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>XNKSRGHGPSHYIW-UHFFFAOYSA-N</td>
<td>CNc1nc(C)c(s1)c2ccnc(Nc3cccc(O)c3)n2</td>
<td>CHEMBL44119</td>
</tr>
</tbody>
</table>
<p>5 rows × 28 columns</p>
</div>
</div>
</div>
</div>
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<div class="prompt input_prompt">In [13]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">smis</span> <span class="o">=</span> <span class="n">df_doc2</span><span class="p">[</span><span class="s1">'canonical_smiles'</span><span class="p">]</span>
<span class="n">cids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df_doc2</span><span class="o">.</span><span class="n">compound_chembl_id</span><span class="p">)</span>
<span class="n">ms</span> <span class="o">=</span> <span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">smis</span><span class="p">]</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">:</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">Compute2DCoords</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">(</span><span class="n">ms</span><span class="p">[:</span><span class="mi">12</span><span class="p">],</span><span class="n">legends</span><span class="o">=</span><span class="n">cids</span><span class="p">,</span><span class="n">molsPerRow</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
</pre></div>
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<div class="prompt output_prompt">Out[13]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">core</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'Cc1nc([*:3])sc1-c1ccnc(N([*:1])[*:2])n1'</span><span class="p">)</span>
<span class="n">ps</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">AdjustQueryParameters</span><span class="o">.</span><span class="n">NoAdjustments</span><span class="p">()</span>
<span class="n">ps</span><span class="o">.</span><span class="n">makeDummiesQueries</span><span class="o">=</span><span class="kc">True</span>
<span class="n">qcore</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">AdjustQueryProperties</span><span class="p">(</span><span class="n">core</span><span class="p">,</span><span class="n">ps</span><span class="p">)</span>
<span class="n">mhs</span> <span class="o">=</span> <span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">AddHs</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">addCoords</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">]</span>
<span class="n">mms</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">mhs</span> <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">HasSubstructMatch</span><span class="p">(</span><span class="n">qcore</span><span class="p">)]</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">mms</span><span class="p">:</span>
<span class="k">for</span> <span class="n">atom</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">GetAtoms</span><span class="p">():</span>
<span class="n">atom</span><span class="o">.</span><span class="n">SetIntProp</span><span class="p">(</span><span class="s2">"SourceAtomIdx"</span><span class="p">,</span><span class="n">atom</span><span class="o">.</span><span class="n">GetIdx</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mhs</span><span class="p">),</span><span class="nb">len</span><span class="p">(</span><span class="n">mms</span><span class="p">))</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">SetPreferCoordGen</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">Compute2DCoords</span><span class="p">(</span><span class="n">qcore</span><span class="p">)</span>
<span class="n">qcore</span>
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<pre>33 33
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
"
>
</div>
</div>
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</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [15]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rdkit</span><span class="o">.</span><span class="n">RDLogger</span><span class="o">.</span><span class="n">DisableLog</span><span class="p">(</span><span class="s1">'rdApp.warning'</span><span class="p">)</span>
<span class="n">groups</span><span class="p">,</span><span class="n">_</span> <span class="o">=</span> <span class="n">rdRGroupDecomposition</span><span class="o">.</span><span class="n">RGroupDecompose</span><span class="p">([</span><span class="n">qcore</span><span class="p">],</span><span class="n">mms</span><span class="p">,</span><span class="n">asSmiles</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">asRows</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
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</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [16]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nd">@interact</span><span class="p">(</span><span class="n">idx</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">mms</span><span class="p">)))</span>
<span class="k">def</span> <span class="nf">draw_it</span><span class="p">(</span><span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">mms</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="n">row</span> <span class="o">=</span> <span class="n">groups</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="k">return</span> <span class="n">Image</span><span class="p">(</span><span class="n">highlight_rgroups</span><span class="p">(</span><span class="n">m</span><span class="p">,</span><span class="n">row</span><span class="p">,</span><span class="n">qcore</span><span class="p">,</span><span class="n">lbls</span><span class="o">=</span><span class="p">(</span><span class="s1">'R1'</span><span class="p">,</span><span class="s1">'R2'</span><span class="p">,</span><span class="s1">'R3'</span><span class="p">)))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Image</span><span class="p">(</span><span class="n">draw_multiple</span><span class="p">(</span><span class="n">mms</span><span class="p">[:</span><span class="mi">12</span><span class="p">],</span><span class="n">groups</span><span class="p">,</span><span class="n">qcore</span><span class="p">,(</span><span class="s1">'R1'</span><span class="p">,</span><span class="s1">'R2'</span><span class="p">,</span><span class="s1">'R3'</span><span class="p">),</span><span class="n">subImageSize</span><span class="o">=</span><span class="p">(</span><span class="mi">300</span><span class="p">,</span><span class="mi">250</span><span class="p">)))</span>
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
"
>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [18]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df_doc3</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">assay_chembl_id</span><span class="o">==</span><span class="s1">'CHEMBL3101313'</span><span class="p">]</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">df_doc3</span><span class="p">))</span>
<span class="n">df_doc3</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>25
</pre>
</div>
</div>
<div class="output_area">
<div class="prompt output_prompt">Out[18]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>assay_id</th>
<th>doc_id</th>
<th>description</th>
<th>assay_organism</th>
<th>assay_chembl_id</th>
<th>aidx</th>
<th>pref_name</th>
<th>activity_id</th>
<th>molregno</th>
<th>standard_relation</th>
<th>...</th>
<th>src_id (#1)</th>
<th>type</th>
<th>relation</th>
<th>value</th>
<th>units</th>
<th>text_value</th>
<th>standard_text_value</th>
<th>standard_inchi_key</th>
<th>canonical_smiles</th>
<th>compound_chembl_id</th>
</tr>
</thead>
<tbody>
<tr>
<th>1129</th>
<td>1281340</td>
<td>76402</td>
<td>Displacement of B-Alexa-Fluor647 from CDK2 (un...</td>
<td>Homo sapiens</td>
<td>CHEMBL3101313</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>13859835</td>
<td>1610535</td>
<td><</td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td><</td>
<td>0.10</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>USOUMMYIFYDJEI-ZZTDINLMSA-N</td>
<td>COC[C@H](Cc1ccc(O)cc1)NC(=O)c2cc(C(=O)O)c3cc(\...</td>
<td>CHEMBL3099753</td>
</tr>
<tr>
<th>1130</th>
<td>1281340</td>
<td>76402</td>
<td>Displacement of B-Alexa-Fluor647 from CDK2 (un...</td>
<td>Homo sapiens</td>
<td>CHEMBL3101313</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>13859836</td>
<td>1610534</td>
<td>=</td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>=</td>
<td>0.10</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>DLJWCYCMLMVSML-FQEVSTJZSA-N</td>
<td>COC[C@H](Cc1ccc(O)cc1)NC(=O)c2cc(C(=O)O)c3cc(c...</td>
<td>CHEMBL3099752</td>
</tr>
<tr>
<th>1131</th>
<td>1281340</td>
<td>76402</td>
<td>Displacement of B-Alexa-Fluor647 from CDK2 (un...</td>
<td>Homo sapiens</td>
<td>CHEMBL3101313</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>13859837</td>
<td>1610533</td>
<td>=</td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>=</td>
<td>0.16</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>BHBDKGIDYKROMY-BAJJQUEBSA-N</td>
<td>CN(C)C(=O)[C@H](Cc1ccc(O)cc1)NC(=O)c2cc(C(=O)O...</td>
<td>CHEMBL3099751</td>
</tr>
<tr>
<th>1132</th>
<td>1281340</td>
<td>76402</td>
<td>Displacement of B-Alexa-Fluor647 from CDK2 (un...</td>
<td>Homo sapiens</td>
<td>CHEMBL3101313</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>13859838</td>
<td>1610532</td>
<td>=</td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>=</td>
<td>0.10</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>IYRLCOILNQBJEJ-ZNOKPGKASA-N</td>
<td>CNC(=O)[C@H](Cc1ccc(O)cc1)NC(=O)c2cc(C(=O)O)c3...</td>
<td>CHEMBL3099750</td>
</tr>
<tr>
<th>1133</th>
<td>1281340</td>
<td>76402</td>
<td>Displacement of B-Alexa-Fluor647 from CDK2 (un...</td>
<td>Homo sapiens</td>
<td>CHEMBL3101313</td>
<td>CLD0</td>
<td>Cyclin-dependent kinase 2</td>
<td>13859839</td>
<td>1610531</td>
<td>=</td>
<td>...</td>
<td>1</td>
<td>Ki</td>
<td>=</td>
<td>0.30</td>
<td>uM</td>
<td>NaN</td>
<td>NaN</td>
<td>DDIHZTFUIFPFOO-OAQYLSRUSA-N</td>
<td>CCC[C@H](Cc1ccc(O)cc1)NC(=O)c2cc(C(=O)O)c3cc(c...</td>
<td>CHEMBL3099749</td>
</tr>
</tbody>
</table>
<p>5 rows × 28 columns</p>
</div>
</div>
</div>
</div>
</div>
</div>
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<p>Finally, do another document, just because it's fun. :-)</p>
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<div class="prompt input_prompt">In [19]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">smis</span> <span class="o">=</span> <span class="n">df_doc3</span><span class="p">[</span><span class="s1">'canonical_smiles'</span><span class="p">]</span>
<span class="n">cids</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">df_doc3</span><span class="o">.</span><span class="n">compound_chembl_id</span><span class="p">)</span>
<span class="n">ms</span> <span class="o">=</span> <span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">smis</span><span class="p">]</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">:</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">Compute2DCoords</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">(</span><span class="n">ms</span><span class="p">[:</span><span class="mi">12</span><span class="p">],</span><span class="n">legends</span><span class="o">=</span><span class="n">cids</span><span class="p">,</span><span class="n">molsPerRow</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
</pre></div>
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<div class="prompt output_prompt">Out[19]:</div>
<div class="output_png output_subarea output_execute_result">
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src="data:image/png;base64,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<div class="prompt input_prompt">In [20]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">core</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'OC(=O)c1cc(C(=O)NC(C[*:1])[*:2])nc2ccc([*:3])cc12'</span><span class="p">)</span>
<span class="n">ps</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">AdjustQueryParameters</span><span class="o">.</span><span class="n">NoAdjustments</span><span class="p">()</span>
<span class="n">ps</span><span class="o">.</span><span class="n">makeDummiesQueries</span><span class="o">=</span><span class="kc">True</span>
<span class="n">qcore</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">AdjustQueryProperties</span><span class="p">(</span><span class="n">core</span><span class="p">,</span><span class="n">ps</span><span class="p">)</span>
<span class="n">mhs</span> <span class="o">=</span> <span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">AddHs</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">addCoords</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">]</span>
<span class="n">mms</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">mhs</span> <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">HasSubstructMatch</span><span class="p">(</span><span class="n">qcore</span><span class="p">)]</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">mms</span><span class="p">:</span>
<span class="k">for</span> <span class="n">atom</span> <span class="ow">in</span> <span class="n">m</span><span class="o">.</span><span class="n">GetAtoms</span><span class="p">():</span>
<span class="n">atom</span><span class="o">.</span><span class="n">SetIntProp</span><span class="p">(</span><span class="s2">"SourceAtomIdx"</span><span class="p">,</span><span class="n">atom</span><span class="o">.</span><span class="n">GetIdx</span><span class="p">())</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">mhs</span><span class="p">),</span><span class="nb">len</span><span class="p">(</span><span class="n">mms</span><span class="p">))</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">SetPreferCoordGen</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">Compute2DCoords</span><span class="p">(</span><span class="n">qcore</span><span class="p">)</span>
<span class="n">qcore</span>
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<pre>25 22
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
"
>
</div>
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</div>
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [21]:</div>
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<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rdkit</span><span class="o">.</span><span class="n">RDLogger</span><span class="o">.</span><span class="n">DisableLog</span><span class="p">(</span><span class="s1">'rdApp.warning'</span><span class="p">)</span>
<span class="n">groups</span><span class="p">,</span><span class="n">_</span> <span class="o">=</span> <span class="n">rdRGroupDecomposition</span><span class="o">.</span><span class="n">RGroupDecompose</span><span class="p">([</span><span class="n">qcore</span><span class="p">],</span><span class="n">mms</span><span class="p">,</span><span class="n">asSmiles</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">asRows</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
</pre></div>
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [22]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="nd">@interact</span><span class="p">(</span><span class="n">idx</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">mms</span><span class="p">)))</span>
<span class="k">def</span> <span class="nf">draw_it</span><span class="p">(</span><span class="n">idx</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
<span class="n">m</span> <span class="o">=</span> <span class="n">mms</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="n">row</span> <span class="o">=</span> <span class="n">groups</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span>
<span class="k">return</span> <span class="n">Image</span><span class="p">(</span><span class="n">highlight_rgroups</span><span class="p">(</span><span class="n">m</span><span class="p">,</span><span class="n">row</span><span class="p">,</span><span class="n">qcore</span><span class="p">,</span><span class="n">lbls</span><span class="o">=</span><span class="p">(</span><span class="s1">'R1'</span><span class="p">,</span><span class="s1">'R2'</span><span class="p">,</span><span class="s1">'R3'</span><span class="p">)))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Image</span><span class="p">(</span><span class="n">draw_multiple</span><span class="p">(</span><span class="n">mms</span><span class="p">[:</span><span class="mi">12</span><span class="p">],</span><span class="n">groups</span><span class="p">,</span><span class="n">qcore</span><span class="p">,(</span><span class="s1">'R1'</span><span class="p">,</span><span class="s1">'R2'</span><span class="p">,</span><span class="s1">'R3'</span><span class="p">),</span><span class="n">subImageSize</span><span class="o">=</span><span class="p">(</span><span class="mi">300</span><span class="p">,</span><span class="mi">250</span><span class="p">)))</span>
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<p>An aside here... it would be nice to include legends in these views too, but there's unfortunately a bug in v2020.09.1 that messes up the ring highlights when we do this:</p>
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<div class="prompt input_prompt">In [25]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Image</span><span class="p">(</span><span class="n">highlight_rgroups</span><span class="p">(</span><span class="n">mms</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">groups</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">qcore</span><span class="p">,</span><span class="n">lbls</span><span class="o">=</span><span class="p">(</span><span class="s1">'R1'</span><span class="p">,</span><span class="s1">'R2'</span><span class="p">,</span><span class="s1">'R3'</span><span class="p">),</span><span class="n">legend</span><span class="o">=</span><span class="n">cids</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
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<div class="prompt output_prompt">Out[25]:</div>
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<p>We will get that fixed for the 2020.09.2 release.</p>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com1tag:blogger.com,1999:blog-684443317148892945.post-77310404358060992282020-10-16T05:02:00.002-07:002020-10-16T05:08:23.312-07:002020 Virtual RDKit UGM<p>TL;DR: we had the UGM last week, it was great!</p><p>Last week we held the 9th annual RDKit UGM. The plan was to hold the event in Berlin, but thanks to covid19 we did a virtual event instead. After doing a bunch of reading and talking to numerous people, we decided to use a combination of Zoom to handle the video component and Discord to provide interaction.</p><p>We had the usual full program of talks covering diverse topics, lightning/poster talks, and even a panel discussion. As usual, the program and PDFs of the presentations that have been shared are in github: <a href="https://github.com/rdkit/UGM_2020">https://github.com/rdkit/UGM_2020</a></p><p>We did record the sessions and are investigating ways to effectively make those recordings available.</p><p>Some numbers about the event, since everyone loves numbers:</p><p>>550 registrations; ~300 unique attendees; ~200 people attending each presentation; >3000 messages in discord; 240 pre-UGM attendee survey responses, 161 of them from first time attendees; 24 poster/lightning presentations, 16 standard talks, 1 panel.</p><p>Here's the breakdown of the attendees (based on those pre-UGM survey responses):</p><p><span face="Roboto, RobotoDraft, Helvetica, Arial, sans-serif" id="docs-internal-guid-3f446bb5-7fff-70a7-b069-f7e245d6e633" style="background-color: white;"><img alt="Forms response chart. Question title: Where do you work?. Number of responses: 240 responses." height="227" 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" width="539" /></span></p><p>and how they're using the RDKit:</p><p><img alt="Forms response chart. Question title: How are you using the RDKit?. Number of responses: 240 responses." height="315" 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style="font-family: Roboto, RobotoDraft, Helvetica, Arial, sans-serif;" width="663" /></p><p>One interesting, and completely spontaneous thing that happened, is that people in the Discord channel started curating lists of interesting resources. Those are in the github repo: <a href="https://github.com/rdkit/UGM_2020/blob/master/info/curated_list_of_resources.md">https://github.com/rdkit/UGM_2020/blob/master/info/curated_list_of_resources.md</a></p><p>I wasn't sure how a community-centered event like the RDKit UGM would work in a virtual format, but it went much, much better than I expected. I think the combination of Zoom for presentations and discord for interaction worked really well, and the feedback from the attendees that we've gotten so far seems to agree. We actually ended up having a lot of interaction among attendees and high-quality discussions of the presentations despite (or perhaps because of... see below) the electronic format.</p><p>I wrote up a little blurb with some more details about using Discord for the meeting, which I've enclosed below in case anyone's interested.</p><p>In the end though, the UGM is an event that is about people and the community, and the RDKit community is just awesome (yeah, I'm biased, but other people keep saying it too!), so <i>of course</i> the event worked well. :-)</p><p>Many thanks are owed to my co-organizers Floriane and Okko, Jaime for helping us use Discord better and for providing live tech support, and Christiane from KNIME who made sure that the Zoom stuff all went well.</p><p>And, of course, thanks to the presenters and everyone else who attended and helped make the UGM such a success!</p><p>I can't wait until next year!</p><p><br /></p><p><br /></p><h3 style="text-align: left;">Some notes about using Discord</h3><p>Discord takes some getting used to and is clearly not perfect, but I think it worked pretty well. </p><p>Pros:</p><p>- Because it's at least somewhat asynchronous it provides an opportunity for people who like to carefully formulate questions and responses to participate in discussions.</p><p>- Essentially zero barrier for people to start using it.</p><p>- The contents of the discussions are easily available after the event and can be exported and archived.</p><p>- Super easy to create new channels to help focus discussions</p><p>Cons:</p><p>- It can be quite hard to follow a discussion, particularly if it's quite active. I imagine that this is going to be even more true for anyone who comes back in a week and tries to follow a conversation. I think threading systems (like email, slack, forums software like discourse, etc.) can help with this, but I know they drive some people crazy. Having a "Discord best practices" video/document to point to would be really helpful.</p><p>- It's not always clear where (which channel) a particular discussion should be taking place, and people make mistakes here. This isn't a big deal live, but there are some conversations that I want to follow up on and *finding* them in discord requires a bunch of searching. I don't think this is actually a perfectly solvable problem</p><p>- Discord came out of the gaming community, so it's really informal. This was (I think) fine for the RDKit UGM, where the volunteer nature of the event and the informality is an important part of the culture. But I could imagine it being more problematic for events which are striving for a professional and/or "branded" feel.</p><p>The quantity and quality of the "discussions" about the presentations that happened in discord was, overall, better than what happens in person (and I'm taking into account the challenges that discord poses in that assessment). In a traditional live event there's usually a pretty limited time available for Q&A/discussion immediately following a presentation, but with the chat-based system we had some discussions go on for hours. It's also nice that these discussions are publicly visible and can be read/followed by other attendees asynchronously, when they have time. At in-person events you can have great discussions about the presentations during breaks, lunches, etc., but these are typically within very small groups - the three people who get a chance to talk directly to the speaker have a great experience, but the rest of the attendees don't learn a thing.</p><p>After the fact it's great to know that I can go back to discord and re-read the discussion, get the URLs that were shared, etc. I don't need to worry about taking notes immediately afterwards each discussion or forgetting important details of what we talked about (I guess this doesn't happen to everyone, but it's a regular "feature" of conferences for me).</p><p><br /></p>greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com1tag:blogger.com,1999:blog-684443317148892945.post-42581113597097301132020-10-06T21:41:00.004-07:002020-10-06T21:45:50.245-07:00DOIs for RDKit releases<p>A quick one on something that came up in a chat during the 2020 Virtual RDKit UGM that is generally useful and perhaps not as well known as it should be.</p><p>We've set things up with the RDKit so that every release we do is automatically assigned a DOI. This is a service provided by <a href="https://zenodo.org/">Zenodo</a>, which is also the repository (in the archival sense, not the source-code sense) storing the release artifacts.</p><p>You can see the DOI for the most recent RDKit release in the READMEmd in the github UI:</p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhYaM5ghdkaE-QhbTyRfKcLkUk64xZQkBB6nM4AkAU04jrnz6kYul4H47lnJRHGu5aulyxM6_EJXLaaejCMW2ng3inucFFWFe35kBEylJ94RopP9eM4jSmvIX5bGa6CwZsKXTJGypCcMMA/" style="margin-left: 1em; margin-right: 1em;"><img data-original-height="158" data-original-width="519" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhYaM5ghdkaE-QhbTyRfKcLkUk64xZQkBB6nM4AkAU04jrnz6kYul4H47lnJRHGu5aulyxM6_EJXLaaejCMW2ng3inucFFWFe35kBEylJ94RopP9eM4jSmvIX5bGa6CwZsKXTJGypCcMMA/s16000/image.png" /></a></div>Clicking on that little banner will take you to the RDKit page at Zenodo, where you can see all the releases they've archived:<p></p><p></p><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEheYlPegrf0uyD_E74cqDkZurZC9kfXMGc-5cVwrCJ4y4mM9ETRYjo_p4LIenkMf4V7Ch3HZ7ECvvV8Jdd38zC0QnXX1FGAZHWYBNpTMZebIl-mvKVQn2nNYvrcRKWTUzz-YmNxu8OJ3BI/" style="margin-left: 1em; margin-right: 1em;"><img data-original-height="555" data-original-width="376" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEheYlPegrf0uyD_E74cqDkZurZC9kfXMGc-5cVwrCJ4y4mM9ETRYjo_p4LIenkMf4V7Ch3HZ7ECvvV8Jdd38zC0QnXX1FGAZHWYBNpTMZebIl-mvKVQn2nNYvrcRKWTUzz-YmNxu8OJ3BI/s16000/image.png" /></a></div><br />This can be super useful if you want the DOI for the specific RDKit release you are using in that paper you're working on.<p></p><p>Note: if you're using github to do software releases and would like to get DOIs for those releases, it's really easy to <a href="https://guides.github.com/activities/citable-code/">set this up</a>.</p><p><br /></p><p><br /><br /></p>greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com2tag:blogger.com,1999:blog-684443317148892945.post-52529616245489168202020-09-14T00:03:00.003-07:002020-09-14T00:03:26.152-07:00Interactively exploring ScaffoldNetwork options<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>The ScafffoldNetwork code added in RDKit release 2020.03 (described here: <a href="https://doi.org/10.1021/acs.jcim.0c00296">https://doi.org/10.1021/acs.jcim.0c00296</a>) has a lot of options. In this blog post I'll show how to use an interactive view based on <a href="https://github.com/QuantStack/ipycytoscape">ipycytoscape</a> to explore the scaffold networks and look at the effects of the various options. There's not a lot of text here since this is mainly about providing the interactive demo. Of course it doesn't work in blogger, so you'll need to download the notebook from <a href="https://github.com/greglandrum/rdkit_blog/blob/master/notebooks/Scaffold%20Network%20-%20Interactive%20Views%20and%20Exploring%20the%20Options.ipynb">the github repo</a> to try it yourself.</p>
<p>This draws on a <a href="https://iwatobipen.wordpress.com/2020/03/30/draw-scaffold-tree-as-network-with-molecular-image-rdkit-cytoscape/">blog post from pen</a> as well as some of the <a href="https://github.com/QuantStack/ipycytoscape/tree/master/examples">examples/tutorials</a> provided by Mariana Meireles and the rest of the ipycytoscape team.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">Chem</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">Draw</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">IPythonConsole</span>
<span class="n">IPythonConsole</span><span class="o">.</span><span class="n">drawOptions</span><span class="o">.</span><span class="n">maxFontSize</span><span class="o">=</span><span class="mi">18</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">rdDepictor</span>
<span class="kn">from</span> <span class="nn">urllib</span> <span class="kn">import</span> <span class="n">parse</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">rdMolDraw2D</span>
<span class="kn">import</span> <span class="nn">gzip</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">rdkit</span>
<span class="kn">import</span> <span class="nn">logging</span>
<span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">RDLogger</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Scaffolds</span> <span class="kn">import</span> <span class="n">rdScaffoldNetwork</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rdkit</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
<span class="o">%</span><span class="k">load_ext</span> sql
<span class="o">%</span><span class="k">pylab</span> inline
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<pre>2020.09.1dev1
Populating the interactive namespace from numpy and matplotlib
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<p>The dataset I'll use here is the set of "very active" molecules from ChEMBL that I put together for an <a href="https://rdkit.blogspot.com/2020/05/binary-molecules-and-cartridge.html">earlier blog post</a>. Start by reading that in and making sure we have unique SMILES:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">moldict</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">((</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolToSmiles</span><span class="p">(</span><span class="n">x</span><span class="p">),</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> \
<span class="n">Chem</span><span class="o">.</span><span class="n">ForwardSDMolSupplier</span><span class="p">(</span><span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">'../data/chembl26_very_active.sdf.gz'</span><span class="p">))</span> <span class="k">if</span> <span class="n">x</span><span class="o">.</span><span class="n">GetNumAtoms</span><span class="p">()</span><span class="o"><</span><span class="mi">75</span><span class="p">)</span>
<span class="nb">len</span><span class="p">(</span><span class="n">moldict</span><span class="p">)</span>
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<pre>22626</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">ms</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">moldict</span><span class="o">.</span><span class="n">values</span><span class="p">())</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">rdMolDraw2D</span>
<span class="kn">from</span> <span class="nn">urllib</span> <span class="kn">import</span> <span class="n">parse</span>
<span class="c1"># some code lightly adapted from pen: </span>
<span class="c1"># https://iwatobipen.wordpress.com/2020/03/30/draw-scaffold-tree-as-network-with-molecular-image-rdkit-cytoscape/</span>
<span class="k">def</span> <span class="nf">smi2svg</span><span class="p">(</span><span class="n">smi</span><span class="p">):</span>
<span class="n">mol</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="n">smi</span><span class="p">)</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">Chem</span><span class="o">.</span><span class="n">rdmolops</span><span class="o">.</span><span class="n">Kekulize</span><span class="p">(</span><span class="n">mol</span><span class="p">)</span>
<span class="k">except</span><span class="p">:</span>
<span class="k">pass</span>
<span class="n">drawer</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">350</span><span class="p">,</span> <span class="mi">300</span><span class="p">)</span>
<span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">PrepareAndDrawMolecule</span><span class="p">(</span><span class="n">drawer</span><span class="p">,</span><span class="n">mol</span><span class="p">)</span>
<span class="n">drawer</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">svg</span> <span class="o">=</span> <span class="n">drawer</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">()</span>
<span class="k">return</span> <span class="n">svg</span>
<span class="k">def</span> <span class="nf">smi2image</span><span class="p">(</span><span class="n">smi</span><span class="p">):</span>
<span class="n">svg_string</span> <span class="o">=</span> <span class="n">smi2svg</span><span class="p">(</span><span class="n">smi</span><span class="p">)</span>
<span class="n">impath</span> <span class="o">=</span> <span class="s1">'data:image/svg+xml;charset=utf-8,'</span> <span class="o">+</span> <span class="n">parse</span><span class="o">.</span><span class="n">quote</span><span class="p">(</span><span class="n">svg_string</span><span class="p">,</span> <span class="n">safe</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span>
<span class="k">return</span> <span class="n">impath</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">networkx</span> <span class="k">as</span> <span class="nn">nx</span>
<span class="kn">import</span> <span class="nn">ipycytoscape</span>
<span class="kn">import</span> <span class="nn">ipywidgets</span> <span class="k">as</span> <span class="nn">widgets</span>
<span class="kn">from</span> <span class="nn">ipywidgets</span> <span class="kn">import</span> <span class="n">interact</span><span class="p">,</span><span class="n">fixed</span>
<span class="n">tms</span> <span class="o">=</span> <span class="n">ms</span><span class="p">[:</span><span class="mi">50</span><span class="p">]</span>
<span class="k">def</span> <span class="nf">show_scaffolds</span><span class="p">(</span><span class="n">mols</span><span class="p">,</span><span class="n">which</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">layout</span><span class="o">=</span><span class="s1">'dagre'</span><span class="p">,</span><span class="n">includeGenericScaffolds</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">includeGenericBondScaffolds</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">includeScaffoldsWithAttachments</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">includeScaffoldsWithoutAttachments</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">pruneBeforeFragmenting</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">keepOnlyFirstFragment</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="c1"># the scaffold generation process is verbose, disable the messages sent to the info log:</span>
<span class="n">RDLogger</span><span class="o">.</span><span class="n">DisableLog</span><span class="p">(</span><span class="s1">'rdApp.info'</span><span class="p">)</span>
<span class="k">if</span> <span class="n">which</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">mols</span> <span class="o">=</span> <span class="p">[</span><span class="n">mols</span><span class="p">[</span><span class="n">which</span><span class="p">]]</span>
<span class="n">scaffParams</span> <span class="o">=</span> <span class="n">rdScaffoldNetwork</span><span class="o">.</span><span class="n">ScaffoldNetworkParams</span><span class="p">()</span>
<span class="n">scaffParams</span><span class="o">.</span><span class="n">collectMolCounts</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">scaffParams</span><span class="o">.</span><span class="n">includeGenericScaffolds</span> <span class="o">=</span> <span class="n">includeGenericScaffolds</span>
<span class="n">scaffParams</span><span class="o">.</span><span class="n">includeScaffoldsWithoutAttachments</span> <span class="o">=</span> <span class="n">includeScaffoldsWithoutAttachments</span>
<span class="n">scaffParams</span><span class="o">.</span><span class="n">keepOnlyFirstFragment</span> <span class="o">=</span> <span class="n">keepOnlyFirstFragment</span>
<span class="n">scaffParams</span><span class="o">.</span><span class="n">includeGenericBondScaffolds</span> <span class="o">=</span> <span class="n">includeGenericBondScaffolds</span>
<span class="n">scaffParams</span><span class="o">.</span><span class="n">includeScaffoldsWithAttachments</span> <span class="o">=</span> <span class="n">includeScaffoldsWithAttachments</span>
<span class="n">scaffParams</span><span class="o">.</span><span class="n">pruneBeforeFragmenting</span> <span class="o">=</span> <span class="n">pruneBeforeFragmenting</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">rdScaffoldNetwork</span><span class="o">.</span><span class="n">CreateScaffoldNetwork</span><span class="p">(</span><span class="n">mols</span><span class="p">,</span><span class="n">scaffParams</span><span class="p">)</span>
<span class="n">g</span> <span class="o">=</span> <span class="n">nx</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">Graph</span><span class="p">()</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">node</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">net</span><span class="o">.</span><span class="n">nodes</span><span class="p">):</span>
<span class="n">g</span><span class="o">.</span><span class="n">add_node</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="n">count</span><span class="o">=</span><span class="sa">f</span><span class="s1">'count: </span><span class="si">{</span><span class="n">net</span><span class="o">.</span><span class="n">molCounts</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="n">smiles</span><span class="o">=</span><span class="n">node</span><span class="p">,</span> <span class="n">img</span><span class="o">=</span><span class="n">smi2image</span><span class="p">(</span><span class="n">node</span><span class="p">),</span> <span class="n">hac</span><span class="o">=</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="n">node</span><span class="p">)</span><span class="o">.</span><span class="n">GetNumAtoms</span><span class="p">())</span>
<span class="n">g</span><span class="o">.</span><span class="n">add_edges_from</span><span class="p">([(</span><span class="n">e</span><span class="o">.</span><span class="n">beginIdx</span><span class="p">,</span><span class="n">e</span><span class="o">.</span><span class="n">endIdx</span><span class="p">)</span> <span class="k">for</span> <span class="n">e</span> <span class="ow">in</span> <span class="n">net</span><span class="o">.</span><span class="n">edges</span><span class="p">])</span>
<span class="n">directed</span> <span class="o">=</span> <span class="n">ipycytoscape</span><span class="o">.</span><span class="n">CytoscapeWidget</span><span class="p">()</span>
<span class="n">directed</span><span class="o">.</span><span class="n">set_style</span><span class="p">([{</span>
<span class="s1">'selector'</span><span class="p">:</span> <span class="s1">'node'</span><span class="p">,</span>
<span class="s1">'css'</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">'content'</span><span class="p">:</span> <span class="s1">'data(count)'</span><span class="p">,</span>
<span class="s1">'text-valign'</span><span class="p">:</span> <span class="s1">'top'</span><span class="p">,</span>
<span class="s1">'color'</span><span class="p">:</span> <span class="s1">'white'</span><span class="p">,</span>
<span class="s1">'text-outline-width'</span><span class="p">:</span> <span class="mi">2</span><span class="p">,</span>
<span class="s1">'text-outline-color'</span><span class="p">:</span> <span class="s1">'#9dbaea'</span><span class="p">,</span>
<span class="s1">'shape'</span> <span class="p">:</span> <span class="s1">'rectangle'</span><span class="p">,</span>
<span class="s1">'width'</span><span class="p">:</span><span class="mi">350</span><span class="p">,</span>
<span class="s1">'height'</span><span class="p">:</span><span class="mi">300</span><span class="p">,</span>
<span class="s1">'background-color'</span><span class="p">:</span> <span class="s1">'#9dbaea'</span><span class="p">,</span>
<span class="s1">'background-fit'</span><span class="p">:</span><span class="s1">'contain'</span><span class="p">,</span>
<span class="s1">'background-image'</span><span class="p">:</span><span class="s1">'data(img)'</span><span class="p">,</span>
<span class="p">}</span>
<span class="p">},</span>
<span class="p">{</span>
<span class="s1">'selector'</span><span class="p">:</span> <span class="s1">'edge'</span><span class="p">,</span>
<span class="s1">'style'</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">'width'</span><span class="p">:</span> <span class="mi">4</span><span class="p">,</span>
<span class="s1">'line-color'</span><span class="p">:</span> <span class="s1">'#9dbaea'</span><span class="p">,</span>
<span class="s1">'target-arrow-shape'</span><span class="p">:</span> <span class="s1">'triangle'</span><span class="p">,</span>
<span class="s1">'target-arrow-color'</span><span class="p">:</span> <span class="s1">'#9dbaea'</span><span class="p">,</span>
<span class="s1">'curve-style'</span><span class="p">:</span> <span class="s1">'bezier'</span>
<span class="p">}</span>
<span class="p">},</span>
<span class="p">{</span>
<span class="s1">'selector'</span><span class="p">:</span> <span class="s1">':selected'</span><span class="p">,</span>
<span class="s1">'css'</span><span class="p">:</span> <span class="p">{</span>
<span class="s1">'background-color'</span><span class="p">:</span> <span class="s1">'black'</span><span class="p">,</span>
<span class="s1">'line-color'</span><span class="p">:</span> <span class="s1">'black'</span><span class="p">,</span>
<span class="s1">'target-arrow-color'</span><span class="p">:</span> <span class="s1">'black'</span><span class="p">,</span>
<span class="s1">'source-arrow-color'</span><span class="p">:</span> <span class="s1">'black'</span><span class="p">,</span>
<span class="s1">'text-outline-color'</span><span class="p">:</span> <span class="s1">'black'</span>
<span class="p">}}</span>
<span class="p">])</span>
<span class="n">directed</span><span class="o">.</span><span class="n">set_layout</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">layout</span><span class="p">,</span> <span class="n">nodeSpacing</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">edgeLengthVal</span><span class="o">=</span><span class="mi">50</span><span class="p">)</span>
<span class="n">directed</span><span class="o">.</span><span class="n">graph</span><span class="o">.</span><span class="n">add_graph_from_networkx</span><span class="p">(</span><span class="n">g</span><span class="p">,</span> <span class="n">directed</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="k">return</span> <span class="n">directed</span>
<span class="nd">@interact</span><span class="p">(</span><span class="n">mols</span><span class="o">=</span><span class="n">fixed</span><span class="p">(</span><span class="n">tms</span><span class="p">),</span><span class="n">which</span><span class="o">=</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">len</span><span class="p">(</span><span class="n">tms</span><span class="p">)),</span><span class="n">layout</span><span class="o">=</span><span class="p">[</span><span class="s1">'dagre'</span><span class="p">,</span><span class="s1">'breadthfirst'</span><span class="p">,</span><span class="s1">'concentric'</span><span class="p">,</span><span class="s1">'cose'</span><span class="p">])</span>
<span class="k">def</span> <span class="nf">interactively_show_scaffolds</span><span class="p">(</span><span class="n">mols</span><span class="p">,</span><span class="n">which</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">layout</span><span class="o">=</span><span class="s1">'dagre'</span><span class="p">,</span><span class="n">includeGenericScaffolds</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">includeGenericBondScaffolds</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">includeScaffoldsWithAttachments</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span><span class="n">includeScaffoldsWithoutAttachments</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
<span class="n">pruneBeforeFragmenting</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
<span class="n">keepOnlyFirstFragment</span><span class="o">=</span><span class="kc">True</span><span class="p">):</span>
<span class="c1"># the scaffold generation process is verbose, disable the messages sent to the info log:</span>
<span class="n">RDLogger</span><span class="o">.</span><span class="n">DisableLog</span><span class="p">(</span><span class="s1">'rdApp.info'</span><span class="p">)</span>
<span class="k">if</span> <span class="n">which</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="k">return</span>
<span class="k">return</span> <span class="n">show_scaffolds</span><span class="p">(</span><span class="n">mols</span><span class="p">,</span><span class="n">which</span><span class="o">=</span><span class="n">which</span><span class="p">,</span><span class="n">layout</span><span class="o">=</span><span class="n">layout</span><span class="p">,</span><span class="n">includeGenericScaffolds</span><span class="o">=</span><span class="n">includeGenericScaffolds</span><span class="p">,</span>
<span class="n">includeGenericBondScaffolds</span><span class="o">=</span><span class="n">includeGenericBondScaffolds</span><span class="p">,</span>
<span class="n">includeScaffoldsWithAttachments</span><span class="o">=</span><span class="n">includeScaffoldsWithAttachments</span><span class="p">,</span>
<span class="n">includeScaffoldsWithoutAttachments</span><span class="o">=</span><span class="n">includeScaffoldsWithoutAttachments</span><span class="p">,</span>
<span class="n">pruneBeforeFragmenting</span><span class="o">=</span><span class="n">pruneBeforeFragmenting</span><span class="p">,</span><span class="n">keepOnlyFirstFragment</span><span class="o">=</span><span class="n">keepOnlyFirstFragment</span><span class="p">)</span>
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<div class="prompt input_prompt">Here's what it looks like in action:</div><div class="separator" style="clear: both; text-align: center;"><a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgeST3a6JRvGJKgMIoWynfDC70AjX3-EDNB7mUCIqS_e46HhDNqTbjYLUnl4cBdTyz03V6H2RxItKGIBbZTY7O9_yWbRCnM42AZdnL0ItzG_BI0wAuadKcrLwKWnQuqfUG4rHbbr0f0dqY/s1025/Peek+2020-09-14+08-57.gif" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="706" data-original-width="1025" height="430" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgeST3a6JRvGJKgMIoWynfDC70AjX3-EDNB7mUCIqS_e46HhDNqTbjYLUnl4cBdTyz03V6H2RxItKGIBbZTY7O9_yWbRCnM42AZdnL0ItzG_BI0wAuadKcrLwKWnQuqfUG4rHbbr0f0dqY/w625-h430/Peek+2020-09-14+08-57.gif" width="625" /></a></div><br /><div class="prompt input_prompt"><br /></div>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com0tag:blogger.com,1999:blog-684443317148892945.post-29145509991849142962020-08-24T02:17:00.005-07:002020-08-24T02:17:54.089-07:00Doing similarity searches with highly folded fingerprints<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>This one has been on the back burner for quite a while. When Pat Lorton was working on the initial version of <a href="https://github.com/schrodinger/gpusimilarity">gpusimilarity</a> and his presentation for the <a href="https://github.com/schrodinger/gpusimilarity/blob/master/gpusimilarity_rdkit_presentation.pdf">2018 RDKit UGM</a> he dealt with the limited amount of memory available on GPUs by loading highly folded fingerprints into the GPU, retrieving extra compounds for a TopN query, and then rescoring those compounds using full-sized fingerprints. I wanted to go back and look at the same problem again from the perspective of a threshold similarity query - i.e. "give me all of the results that have a similarity above my threshold" - instead of the TopN query - i.e. "give me the N nearest neighbors in the database" - Pat was looking at.</p>
<p>This blog post starts that. I'm not going to take a more general approach and look at the impact of fingerprint size on search results and performance at various threshold levels. I'm not going to actually use gpusimilarity in this particular post, but hopefully I will get to that in the future.</p>
<p>This post ended up being longer than I thought it was going to be... here's an overview of the pieces:</p>
<ul>
<li>Look at the impact of folding on computed similarity values. <a href="#Impact-of-fingerprint-size-on-computed-similarity">link</a></li>
<li>Look at the impact of folding on the number of results returned by a similarity search. <a href="#Comparing-the-the-number-of-neighbors-retrieved">link</a></li>
<li>How many hits do we miss by folding the fingerprints? <a href="#How-many-hits-do-we-miss?">link</a></li>
<li>How many extra hits do we retrieve when folding the fingerprints? <a href="#Efficiency,-look-at-the-number-of-extra-hits">link</a></li>
<li>Performance: what impact does fingerprint size have on search times in PostgreSQL? <a href="#In-PostgreSQL">link</a></li>
<li>Performance: what impact does fingerprint size have on search times using <a href="https://github.com/chembl/FPSim2">FPSim2</a>? <a href="#Using-FPSim2">link</a></li>
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<p>The TL;DR summary: When working with the RDKit's Morgan2 fingerprint (MFP2), I think it's reasonable to fold the fingerprints down to 128 bits, particularly when using higher similarity thresholds. This balances the number of hits missed against the number of extra hits retrieved and can result in significant performance improvements when using a specialized search tool like <a href="https://github.com/chembl/FPSim2">FPSim2</a>. The smaller fingerprints - 128 bit fingerprints are 1/16th the size of 2048 bit fingerprints - are faster to read from storage and allow us to fit considerably more fingerprints in the same amount of memory, which is particularly helpful with GPUs.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">Chem</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">rdMolDescriptors</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">IPythonConsole</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">Draw</span>
<span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">RDLogger</span>
<span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">rdBase</span>
<span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">DataStructs</span>
<span class="kn">import</span> <span class="nn">pickle</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">gzip</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rdBase</span><span class="o">.</span><span class="n">rdkitVersion</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">asctime</span><span class="p">())</span>
<span class="o">%</span><span class="k">pylab</span> inline
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<pre>2020.03.5
Mon Aug 24 05:12:00 2020
Populating the interactive namespace from numpy and matplotlib
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<h1 id="Impact-of-fingerprint-size-on-computed-similarity">Impact of fingerprint size on computed similarity<a class="anchor-link" href="#Impact-of-fingerprint-size-on-computed-similarity">¶</a></h1>
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<p>Start by looking at the impact of increased fingerprint folding - decreasing fingerprint size - on similarity values.</p>
<p>For this I use the <a href="https://rdkit.blogspot.com/2016/04/revisiting-similarity-comparison-set.html">similarity comparison set</a> that I put together a few years ago. I'm more interested in the impact on molecules that have a reasonable similiarty to each other, so I'm using the set of pairs that have tanimoto similarity of at least 0.6 with the Morgan1 fingerprint.</p>
<p>Generate the similarity values for multiple bit counts:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rows</span><span class="o">=</span><span class="p">[]</span>
<span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">'../data/chembl21_25K.mfp1.pairs.txt.gz'</span><span class="p">)</span><span class="o">.</span><span class="n">readlines</span><span class="p">():</span>
<span class="n">row</span> <span class="o">=</span> <span class="n">row</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
<span class="n">row</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="n">row</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
<span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">row</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sims</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">row</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">rows</span><span class="p">):</span>
<span class="n">m1</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">m2</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
<span class="n">fp1</span> <span class="o">=</span> <span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">GetMorganFingerprint</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
<span class="n">fp2</span> <span class="o">=</span> <span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">GetMorganFingerprint</span><span class="p">(</span><span class="n">m2</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
<span class="n">sims</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">DataStructs</span><span class="o">.</span><span class="n">TanimotoSimilarity</span><span class="p">(</span><span class="n">fp1</span><span class="p">,</span><span class="n">fp2</span><span class="p">))</span>
<span class="k">for</span> <span class="n">bitsize</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span><span class="mi">1024</span><span class="p">,</span><span class="mi">2048</span><span class="p">,</span><span class="mi">4096</span><span class="p">):</span>
<span class="n">fp1</span> <span class="o">=</span> <span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">GetMorganFingerprintAsBitVect</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">nBits</span><span class="o">=</span><span class="n">bitsize</span><span class="p">)</span>
<span class="n">fp2</span> <span class="o">=</span> <span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">GetMorganFingerprintAsBitVect</span><span class="p">(</span><span class="n">m2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">nBits</span><span class="o">=</span><span class="n">bitsize</span><span class="p">)</span>
<span class="n">sims</span><span class="p">[</span><span class="n">bitsize</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">DataStructs</span><span class="o">.</span><span class="n">TanimotoSimilarity</span><span class="p">(</span><span class="n">fp1</span><span class="p">,</span><span class="n">fp2</span><span class="p">))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">%</span><span class="k">5000</span>: print("Done:",i+1)
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<pre>Done: 5000
Done: 10000
Done: 15000
Done: 20000
Done: 25000
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<p>And then plot histograms to show how much the similarity changes relatively to the 4096 bit fingerprint for the different bit counts.</p>
<p>Positive values indicate that the folded fingerprint yields a higher similarity</p>
<p>I include the histogram twice: once with a linear y axis and once with a log y axis so that the behavior at the edges is more visible.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">figsize</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span>
<span class="n">bcounts</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2048</span><span class="p">,</span><span class="mi">1024</span><span class="p">,</span><span class="mi">512</span><span class="p">,</span><span class="mi">256</span><span class="p">,</span><span class="mi">128</span><span class="p">,</span><span class="mi">64</span><span class="p">)</span>
<span class="n">diffs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">bcount</span> <span class="ow">in</span> <span class="n">bcounts</span><span class="p">:</span>
<span class="n">diffs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sims</span><span class="p">[</span><span class="n">bcount</span><span class="p">])</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sims</span><span class="p">[</span><span class="mi">4096</span><span class="p">]))</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="n">hist</span><span class="p">(</span><span class="n">diffs</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">bcounts</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">);</span>
<span class="n">legend</span><span class="p">();</span>
<span class="n">title</span><span class="p">(</span><span class="s1">'delta similarity'</span><span class="p">);</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
<span class="n">hist</span><span class="p">(</span><span class="n">diffs</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">bcounts</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span><span class="n">log</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
<span class="n">legend</span><span class="p">();</span>
<span class="n">title</span><span class="p">(</span><span class="s1">'MFP2: delta similarity'</span><span class="p">);</span>
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xZx+OGHA7By5Uo+//nPM3bsWG699Vb+7d/+jSVLlnDooYcycuRIfv3rX3PppZcyf/58zjvvPM477zwAZs6cycCBAzulvsjMTjlxZxs9enTOmjWr2mVsMGon/bLJft2UQ6tUiSRJ6grz5s1b49sx1mctXXdEzM7M0c37urRDkiRJKsEgLUmSJJVgkJYkSZJKMEhLkiRJJRikJUmSpBIM0pIkSVIJvkdakiRJrZq3a8e+Cm/os/Na7fPiiy9y/PHH8/LLL7PRRhsxceJETjvtNCZPnsyPf/xjBgwYAMAFF1zAIYccAsBTTz3Fl7/8Zd566y022mgjfv/739OnT58Orb1Bq0E6Iq4BPgkszszdi7bJwL8AS4pu38jMXxXHvg5MAN4D/j0zf1207wVcC7wP+BVwWmZmRGwKXAfsBbwKHJWZdR10fZIkSeqhevXqxcUXX8yee+7J22+/zV577cWBBx4IwOmnn85ZZ53VpP/KlSs57rjjuP7669ljjz149dVX6d27d6fV15alHdcCY1to/35mjiw+DSF6N+BoYFgx5kcRsXHR/3JgIrBz8Wk45wTg9czcCfg+cFHJa5EkSdJ6pKamhj333BOAfv36MXToUF566aU19p85cyYjRoxgjz32AGDrrbdm4403XmP/9mo1SGfmA8BrbTzfOOAXmbk8M/8EzAc+EhE1wOaZ+XBWvkrxOuDTjcZMLbZvAsZERKzLRUiSJGn9VldXxxNPPMHee+8NwKWXXsqIESM48cQTef311wH44x//SERw0EEHseeee/Ltb3+7U2tqz8OGp0bEUxFxTURsWbRtC7zYqE990bZtsd28vcmYzFwJvAls3dIPjIiJETErImYtWbKkpS6SJElaz7zzzjscccQRXHLJJWy++eacfPLJPP/88zz55JPU1NRw5plnApWlHQ8++CA/+9nPePDBB7n11lu59957O62uskH6cmBHYCSwELi4aG9pJjnX0r62Mas3Zl6VmaMzc3TD4nJJkiStv1asWMERRxzBsccey2c+8xkAttlmGzbeeGM22mgj/uVf/oXHHnsMgMGDB/NP//RP9O/fn80224xDDjmExx9/vNNqKxWkM3NRZr6XmX8Hfgx8pDhUD2zXqOtgYEHRPriF9iZjIqIXsAVtX0oiSZKk9VRmMmHCBIYOHcoZZ5yxqn3hwoWrtm+99VZ23313AA466CCeeuop/vrXv7Jy5Up++9vfsttuu3VafaVefxcRNZnZcAWHA3OK7duBn0fE94BBVB4qfCwz34uItyNiH+BR4Hjgh43GjAceBj4L3Feso5YkSVI30ZbX1XW0hx56iOuvv57hw4czcuRIoPKquxtuuIEnn3ySiKC2tpYrr7wSgC233JIzzjiDD3/4w0QEhxxyCIceemin1deW19/dAOwP9I+IeuBcYP+IGEllCUYd8GWAzHwmIqYBc4GVwCmZ+V5xqpP5n9ff3VV8AK4Gro+I+VRmoo/uiAuTJElSz/bRj36UluZXG94Z3ZLjjjuO4447rjPLWqXVIJ2Zx7TQfPVa+p8PnN9C+yxg9xbalwGfa60OSZIkqTvxK8IlSZKkEgzSkiRJUgkGaUmSJKkEg7QkSZJUgkFakiRJKqHUe6QlSZK0YbnspPs69HynXHFAq31OPPFE7rzzTgYOHMicOZWvLfnqV7/KHXfcwSabbMKOO+7IT37yEz7wgQ+wYsUKvvSlL/H444+zcuVKjj/+eL7+9a93aM3NOSMtSZKkbumEE07g7rvvbtJ24IEHMmfOHJ566il22WUXLrzwQgCmT5/O8uXLefrpp5k9ezZXXnkldXV1nVqfQVqSJEnd0n777cdWW23VpO0Tn/gEvXpVFlXss88+1NfXAxARLF26lJUrV/K3v/2NTTbZhM0337xT6zNIS5IkqUe65pprOPjggwH47Gc/y/vf/35qamoYMmQIZ5111mohvKO5RlqSJEk9zvnnn0+vXr049thjAXjsscfYeOONWbBgAa+//jof+9jH+PjHP84OO+zQaTU4Iy1JkqQeZerUqdx555387Gc/IyIA+PnPf87YsWPp3bs3AwcOZN9992XWrFmdWodBWpIkST3G3XffzUUXXcTtt9/OZptttqp9yJAh3HfffWQmS5cu5ZFHHmHXXXft1Fpc2iFJkqRWteV1dR3tmGOO4f777+eVV15h8ODBfOtb3+LCCy9k+fLlHHjggUDlgcMrrriCU045hS9+8YvsvvvuZCZf/OIXGTFiRKfWZ5CWJElSt3TDDTes1jZhwoQW+/bt25fp06d3dklNuLRDkiRJKsEgLUmSJJVgkJYkSZJKMEhLkiRJJRikJUmSpBIM0pIkSVIJvv5OkiRJrbr4qE926PnOvPHONvV74403+NKXvsScOXOICK655hr+8R//EYDvfve7fPWrX2XJkiX079+/Q+trC4O0JEmSuq3TTjuNsWPHctNNN/Huu+/y17/+FYAXX3yRe+65hyFDhlStNpd2SJIkqVt66623eOCBB1Z9Ccsmm2zCBz7wAQBOP/10vv3tbxMRVavPIC1JkqRu6YUXXmDAgAF88YtfZNSoUXzpS19i6dKl3H777Wy77bbsscceVa3PpR2SJEnqllauXMnjjz/OD3/4Q/bee29OO+00Jk+ezAMPPMDMmTOrXZ4z0pIkSeqeBg8ezODBg9l7770B+OxnP8vjjz/On/70J/bYYw9qa2upr69nzz335OWXX+7y+gzSkiRJ6pY++MEPst122/GHP/wBgHvvvZc999yTxYsXU1dXR11dHYMHD+bxxx/ngx/8YJfX59IOSZIktaqtr6vraD/84Q859thjeffdd9lhhx34yU9+UpU6WmKQliRJUrc1cuRIZs2atcbjdXV1XVdMMy7tkCRJkkowSEuSJEkltBqkI+KaiFgcEXMatX0nIp6NiKci4taI+EDRXhsRf4uIJ4vPFY3G7BURT0fE/Ij4QRRvz46ITSPixqL90Yio7fjLlCRJ0rrKzGqX0KXW9XrbMiN9LTC2Wds9wO6ZOQL4I/D1Rseez8yRxeekRu2XAxOBnYtPwzknAK9n5k7A94GL1ukKJEmS1OH69OnDq6++usGE6czk1VdfpU+fPm0e0+rDhpn5QPNZ4sxs/AbsR4DPru0cEVEDbJ6ZDxf71wGfBu4CxgGTi643AZdGROSGctckSZK6ocGDB1NfX8+SJUuqXUqX6dOnD4MHD25z/454a8eJwI2N9rePiCeAt4BzMvN3wLZAfaM+9UUbxZ8vAmTmyoh4E9gaeKX5D4qIiVRmtRkyZEgHlC5JkqSW9O7dm+23377aZXRr7QrSEfG/gZXAz4qmhcCQzHw1IvYCZkTEMCBaGN4w47y2Y00bM68CrgIYPXq0M9YCYPjU4U32nx7/dJUqkSRJG5LSQToixgOfBMY0LMPIzOXA8mJ7dkQ8D+xCZQa68Tz5YGBBsV0PbAfUR0QvYAvgtbJ1SZIkSV2h1OvvImIscDZwWGb+tVH7gIjYuNjegcpDhS9k5kLg7YjYp3hbx/HAbcWw24HxxfZngftcHy1JkqTurtUZ6Yi4Adgf6B8R9cC5VN7SsSlwT/EWu0eKN3TsB/x/EbESeA84KTMbZpdPpvIGkPdRecjwrqL9auD6iJhPZSb66A65MkmSJKkTteWtHce00Hz1GvreDNy8hmOzgN1baF8GfK61OiRJkqTuxG82lCRJkkowSEuSJEklGKQlSZKkEgzSkiRJUgkGaUmSJKkEg7QkSZJUQru+IlyqislbNN3ffkh16pAkSRs0Z6QlSZKkEpyRVjnNZ4Unv1mdOiRJkqrEGWlJkiSpBIO0JEmSVIJBWpIkSSrBIC1JkiSVYJCWJEmSSjBIS5IkSSUYpCVJkqQSDNKSJElSCQZpSZIkqQSDtCRJklSCQVqSJEkqwSAtSZIklWCQliRJkkowSEuSJEklGKQlSZKkEgzSkiRJUgkGaUmSJKkEg7QkSZJUgkFakiRJKsEgLUmSJJVgkJYkSZJKaDVIR8Q1EbE4IuY0atsqIu6JiOeKP7dsdOzrETE/Iv4QEQc1at8rIp4ujv0gIqJo3zQibizaH42I2o69REmSJKnjtWVG+lpgbLO2ScC9mbkzcG+xT0TsBhwNDCvG/CgiNi7GXA5MBHYuPg3nnAC8npk7Ad8HLip7MZIkSVJXaTVIZ+YDwGvNmscBU4vtqcCnG7X/IjOXZ+afgPnARyKiBtg8Mx/OzASuazam4Vw3AWMaZqslSZKk7qrsGultMnMhQPHnwKJ9W+DFRv3qi7Zti+3m7U3GZOZK4E1g65Z+aERMjIhZETFryZIlJUuXJEmS2q+jHzZsaSY519K+tjGrN2ZelZmjM3P0gAEDSpYoSZIktV/ZIL2oWK5B8efior0e2K5Rv8HAgqJ9cAvtTcZERC9gC1ZfSiJJkiR1K2WD9O3A+GJ7PHBbo/ajizdxbE/locLHiuUfb0fEPsX65+ObjWk412eB+4p11JIkSVK31au1DhFxA7A/0D8i6oFzgSnAtIiYAPwF+BxAZj4TEdOAucBK4JTMfK841clU3gDyPuCu4gNwNXB9RMynMhN9dIdcmSRJktSJWg3SmXnMGg6NWUP/84HzW2ifBezeQvsyiiAuSZIk9RR+s6EkSZJUgkFakiRJKsEgLUmSJJVgkJYkSZJKMEhLkiRJJRikJUmSpBIM0pIkSVIJBmlJkiSpBIO0JEmSVIJBWpIkSSrBIC1JkiSVYJCWJEmSSjBIS5IkSSUYpCVJkqQSDNKSJElSCQZpSZIkqQSDtCRJklSCQVqSJEkqwSAtSZIklWCQliRJkkowSEuSJEklGKQlSZKkEgzSkiRJUgkGaUmSJKkEg7QkSZJUQq9qFyCtT+btOrTJ/tBn51WpEkmS1NmckZYkSZJKMEhLkiRJJRikJUmSpBIM0pIkSVIJBmlJkiSphNJBOiL+ISKebPR5KyK+EhGTI+KlRu2HNBrz9YiYHxF/iIiDGrXvFRFPF8d+EBHR3guTJEmSOlPpIJ2Zf8jMkZk5EtgL+Ctwa3H4+w3HMvNXABGxG3A0MAwYC/woIjYu+l8OTAR2Lj5jy9YlSZIkdYWOWtoxBng+M/+8lj7jgF9k5vLM/BMwH/hIRNQAm2fmw5mZwHXApzuoLkmSJKlTdFSQPhq4odH+qRHxVERcExFbFm3bAi826lNftG1bbDdvX01ETIyIWRExa8mSJR1UuiRJkrTu2h2kI2IT4DBgetF0ObAjMBJYCFzc0LWF4bmW9tUbM6/KzNGZOXrAgAHtqluSJElqj46YkT4YeDwzFwFk5qLMfC8z/w78GPhI0a8e2K7RuMHAgqJ9cAvtkiRJUrfVEUH6GBot6yjWPDc4HJhTbN8OHB0Rm0bE9lQeKnwsMxcCb0fEPsXbOo4HbuuAuiRJkqRO06s9gyNiM+BA4MuNmr8dESOpLM+oaziWmc9ExDRgLrASOCUz3yvGnAxcC7wPuKv4SJIkSd1Wu4J0Zv4V2LpZ2xfW0v984PwW2mcBu7enFkmSJKkr+c2GkiRJUgkGaUmSJKkEg7QkSZJUgkFakiRJKqFdDxtKXaF20i+b7Nf1qVIhkiRJjTgjLUmSJJVgkJYkSZJKcGmH1jvzdh3aZH/os/OqVIkkSVqfOSMtSZIklWCQliRJkkowSEuSJEklGKQlSZKkEgzSkiRJUgkGaUmSJKkEg7QkSZJUgkFakiRJKsEgLUmSJJVgkJYkSZJKMEhLkiRJJRikJUmSpBIM0pIkSVIJBmlJkiSphF7VLkDrh+FThzfZf3r801WqRJIkqWs4Iy1JkiSVYJCWJEmSSjBIS5IkSSW4Rlpqh+Zrw6dVqQ5JktT1nJGWJEmSSjBIS5IkSSUYpCVJkqQS2rVGOiLqgLeB94CVmTk6IrYCbgRqgTrgyMx8vej/dWBC0f/fM/PXRftewLXA+4BfAadlZranNmlDd9lJ9zXZP+WKA6pUiSRJ66eOmJH+58wcmZmji/1JwL2ZuTNwb7FPROwGHA0MA8YCP4qIjYsxlwMTgZ2Lz9gOqEuSJEnqNJ3x1o5xwP7F9lTgfuDsov0Xmbkc+FNEzAc+Usxqb56ZDwNExHXAp4G7OqE2SZ3MmXBJ0oaivUE6gZkRkcCVmXkVsE1mLgTIzIURMfyrccYAACAASURBVLDouy3wSKOx9UXbimK7ebukdTBv16FNG/a/rDqFSJK0gWhvkN43MxcUYfmeiHh2LX2jhbZcS/vqJ4iYSGUJCEOGDFnXWiVJkqQO06410pm5oPhzMXAr8BFgUUTUABR/Li661wPbNRo+GFhQtA9uob2ln3dVZo7OzNEDBgxoT+mSJElSu5QO0hHx/ojo17ANfAKYA9wOjC+6jQduK7ZvB46OiE0jYnsqDxU+ViwDeTsi9omIAI5vNEaSJEnqltqztGMb4NZK9qUX8PPMvDsifg9Mi4gJwF+AzwFk5jMRMQ2YC6wETsnM94pzncz/vP7uLnzQUJIkSd1c6SCdmS8Ae7TQ/iowZg1jzgfOb6F9FrB72VokSZKkruY3G0qSJEklGKQlSZKkEgzSkiRJUgkGaUmSJKkEg7QkSZJUgkFakiRJKsEgLUmSJJVgkJYkSZJKMEhLkiRJJRikJUmSpBIM0pIkSVIJvapdgLQ+u+yk+5rsn3LFAVWqRJIkdTRnpCVJkqQSnJGWtN7wbwAkSV3JGWlJkiSpBIO0JEmSVIJBWpIkSSrBIC1JkiSVYJCWJEmSSjBIS5IkSSX4+jtJ6gQXH/XJJvtn3nhnlSqRJHUWg7QkrWcM8ZLUNVzaIUmSJJVgkJYkSZJKMEhLkiRJJRikJUmSpBJ82FCS1CGaP+QIPugoaf3mjLQkSZJUgjPSktZbvgZOktSZnJGWJEmSSjBIS5IkSSWUDtIRsV1E/CYi5kXEMxFxWtE+OSJeiogni88hjcZ8PSLmR8QfIuKgRu17RcTTxbEfRES077IkSZKkztWeNdIrgTMz8/GI6AfMjoh7imPfz8zvNu4cEbsBRwPDgEHAf0bELpn5HnA5MBF4BPgVMBa4qx21SZIkSZ2q9Ix0Zi7MzMeL7beBecC2axkyDvhFZi7PzD8B84GPREQNsHlmPpyZCVwHfLpsXZIkSVJX6JC3dkRELTAKeBTYFzg1Io4HZlGZtX6dSsh+pNGw+qJtRbHdvL2lnzORysw1Q4YM6YjSJUnrAd/QIqka2v2wYUT0BW4GvpKZb1FZprEjMBJYCFzc0LWF4bmW9tUbM6/KzNGZOXrAgAHtLV2SJEkqrV0z0hHRm0qI/llm3gKQmYsaHf8x0DAtUA9s12j4YGBB0T64hXZJ6wG/7U6StL4qHaSLN2tcDczLzO81aq/JzIXF7uHAnGL7duDnEfE9Kg8b7gw8lpnvRcTbEbEPlaUhxwM/LFuXuod5uw5tsj/02XlVqkSSJKlztGdGel/gC8DTEfFk0fYN4JiIGElleUYd8GWAzHwmIqYBc6m88eOU4o0dACcD1wLvo/K2Dt/YIUmSpG6tdJDOzAdpeX3zr9Yy5nzg/BbaZwG7l61FkqrtspPuq3YJkqQu1iFv7ZC6s+YB55QrDqhSJeun5st42P+y6hQiSVIX8yvCJUmSpBIM0pIkSVIJBmlJkiSpBIO0JEmSVIIPG0qS1A5+Pbm04XJGWpIkSSrBIC1JkiSVYJCWJEmSSnCNtLSBcB2nJEkdyyAtrYvJWzTd335IdeqQJElV59IOSZIkqQSDtCRJklSCQVqSJEkqwSAtSZIklWCQliRJkkrwrR2SJPVQvtZSqi6DtNQDDZ86fLW2aVWoQ93DZSfdV+0SJGmD5NIOSZIkqQRnpKUu5F/DSpK0/nBGWpIkSSrBGWl1ieZrOE+54oAqVSJJktQxnJGWJEmSSjBIS5IkSSW4tEOSJK2T5g9Ogw9Pa8PkjLQkSZJUgjPSknqsebsObdqw/2XVKUSStEEySGuD47ucJUlSRzBIS5JK8avJJW3oXCMtSZIkleCMtKqiJzzxXTvpl6u11fWpQiGSpFVcnqfupNvMSEfE2Ij4Q0TMj4hJ1a5HkiRJWptuMSMdERsDlwEHAvXA7yPi9sycW93KepbhU4ev1jbtwpVN9oc+O6+rylFHmrxF0/3th1SnDlb//9m0KtVRbb4xpLpcn61qcDZczXWLIA18BJifmS8ARMQvgHGAQbqR5ksN6vp8vmmHNoSr5v/xWfb695rs+y8FSVo7Q7yqwRDfPUVmVrsGIuKzwNjM/FKx/wVg78w8tVm/icDEYvcfgD8A/YFXurBcdSzvX8/lvevZvH89l/euZ/P+9UwfyswBzRu7y4x0tNC2WsLPzKuAq5oMjJiVmaM7qzB1Lu9fz+W969m8fz2X965n8/6tX7rLw4b1wHaN9gcDC6pUiyRJktSq7hKkfw/sHBHbR8QmwNHA7VWuSZIkSVqjbrG0IzNXRsSpwK+BjYFrMvOZNg6/qvUu6sa8fz2X965n8/71XN67ns37tx7pFg8bSpIkST1Nd1naIUmSJPUoBmlJkiSphB4XpCNiq4i4JyKeK/7ccg39romIxRExp6trVFOtff17VPygOP5UROxZjTrVsjbcv10j4uGIWB4RZ1WjRq1ZG+7fscU/d09FxH9FxB7VqFOra8O9G1fctycjYlZEfLQadaplrd2/Rv0+HBHvFd+poR6mxwVpYBJwb2buDNxb7LfkWmBsVxWlljX6+veDgd2AYyJit2bdDgZ2Lj4Tgcu7tEitURvv32vAvwPf7eLy1Io23r8/Af+UmSOA8/BBqG6hjffuXmCPzBwJnAj8R9dWqTVp4/1r6HcRlZctqAfqiUF6HDC12J4KfLqlTpn5AJX/wKu6Vn39e2a+CzR8/Xtj44DrsuIR4AMRUdPVhapFrd6/zFycmb8HVlSjQK1VW+7ff2Xm68XuI1Te46/qa8u9eyf/540B76eFLzJT1bTlv30A/wbcDCzuyuLUcXpikN4mMxcCFH8OrHI9WrttgRcb7dcXbevaR9XhvenZ1vX+TQDu6tSK1FZtuncRcXhEPAv8ksqstLqHVu9fRGwLHA5c0YV1qYN1i/dINxcR/wl8sIVD/7ura1G7teXr39v0FfGqCu9Nz9bm+xcR/0wlSLvOtnto073LzFuBWyNiPypLcz7e2YWpTdpy/y4Bzs7M9yJa6q6eoFsG6cxc478IImJRRNRk5sLir//965DurS1f/+5XxHdf3puerU33LyJGUFlfe3BmvtpFtWnt1umfvcx8ICJ2jIj+mflKp1en1rTl/o0GflGE6P7AIRGxMjNndE2J6gg9cWnH7cD4Yns8cFsVa1Hr2vL177cDxxdv79gHeLNh+Y6qri33T91Xq/cvIoYAtwBfyMw/VqFGtawt926nKFJY8bajTQB/EeoeWr1/mbl9ZtZmZi1wE/Cvhuiep1vOSLdiCjAtIiYAfwE+BxARg4D/yMxDiv0bgP2B/hFRD5ybmVdXp+QN15q+/j0iTiqOXwH8CjgEmA/8FfhitepVU225fxHxQWAWsDnw94j4CrBbZr5VtcIFtPmfv/8DbA38qMhkKzNzdLVqVkUb790RVCYhVgB/A45q9PChqqiN90/rAb8iXJIkSSqhJy7tkCRJkqrOIC1JkiSVYJCWJEmSSjBIS5IkSSUYpCVJkqQSDNKSJElSCQZpSZIkqQSDtCRJklSCQVqSJEkqwSAtSZIklWCQliRJkkowSEuSJEklGKQlSZKkEgzSkiRJUgkGaUmSJKkEg7QkSZJUgkFakiRJKsEgLUmSJJVgkJYkSZJKMEhLkiRJJRikJUmSpBIM0pIkSVIJBmlJkiSpBIO0JEmSVIJBWpIkSSrBIC1JkiSVYJCWJEmSSjBIS1I3FhEZETu1od/+EVHfBfUcGxEzS479WET8odF+XUR8vB21vBMRO5QdL0ntZZCWtEEpwtu7EdG/WfuTRWitLfavLfq90+hzVKNz/K1oWxQRP4mIvsWx70bEcxHxdkQ8GxHHd/U1NqqxdEhdk8z8WWZ+ouTY32XmP3RgLX0z8wVYdb/+b0edW5LawiAtaUP0J+CYhp2IGA68r4V+3y7CWsPnxkbHPpWZfYE9gQ8D5xTtS4FPAVsA44H/PyL+V2dcxIYqInpVuwZJAoO0pA3T9UDjmeLxwHVlTpSZLwF3AbsX++dm5rOZ+ffMfBT4HfCPbT1fRHw1IhZGxIKIOLHZsU2LGe+/FDPhV0TEar8ARMT1wBDgjmLW/GtF+/SIeDki3oyIByJi2FrqOCEiXihm1v8UEcc2an+wUb+MiH9tNAt/XkTsGBEPR8RbETEtIjYp+q5x+UlEfKQY80Zx/Zc2jGv0c06JiOeA5xq17RQRE4Fjga8V13tH8b/jzc1+xg8j4pJWboEktZlBWtKG6BFg84gYGhEbA0cBPy1zoojYDjgEeKKFY++jMlv9TKO2pyLi82s411jgLOBAYGeg+dKMi4BdgJHATsC2wP9pfp7M/ALwF4pZ88z8dnHoruK8A4HHgZ+toY73Az8ADs7MfsD/Ap5sqW9hLLAXsA/wNeAqKsF2Oyq/YByz5qGrvAecDvSn8ovHGOBfm/X5NLA3sFvjxsy8qriWhr9B+BSV+zk2Ij5QXFMvKvf5+jbUIkltYpCWtKFqmJU+EHgWeKmFPmcVM6RvRMQrzY7NiIg3gAeB3wIXtDD+CuC/gV83NGTmiMz8+RpqOhL4SWbOycylwOSGAxERwL8Ap2fma5n5dvEzj279Ulf97Gsy8+3MXF6ce4+I2GIN3f8O7B4R78vMhZn5zBr6AVyUmW8VfeYAMzPzhcx8k0p4H9WG2mZn5iOZuTIz64ArgX9q1u3C4tr/1obzLQQeAD5XNI0FXsnM2a2NlaS2MkhL2lBdD3weOIE1L+v4bmZ+oPj0b3bs00X7hzLzX5uHu4j4DpXZ2CMzM9tY0yDgxUb7f260PQDYDJjdEO6Bu4v2VkXExhExJSKej4i3gLriUPProgjxRwEnAQsj4pcRsetaTr+o0fbfWtjv24b6domIO4ulJ29R+SWheW0vtjB0baYCxxXbx+FstKQOZpCWtEHKzD9TeejwEOCWjjx3RHwLOBj4RGa+tQ5DF1JZDtFgSKPtV6iE0mGNwv0WxQOPLWke3j8PjKOyXGQLoLah3BYHZ/46Mw8EaqjM2P94Ha6jjMuLn7NzZm4OfKOF2tb2C0lLx2YAIyJid+CTrGEpiySVZZCWtCGbABxQzMB2iIj4OpXQemBmvrqOw6cBJ0TEbhGxGXBuw4HM/DuVMPv9iBhY/KxtI+KgNZxrEdD4Hcv9gOXAq1RmtltaitJwDdtExGHFWunlwDtU1jB3pn7AW8A7xez3yes4vvn1kpnLgJuAnwOPZeZfOqJQSWpgkJa0wcrM5zNzVgef9gIqM8nPNXr/9DcaDkbEMw1vwGihnruAS4D7gPnFn42dXbQ/Uix/+E9gTe9lvhA4p1gGchaV5St/prIWfC6VBy7XZCPgTGAB8BqVtcrNH/zraGdR+QXkbSq/MNy49u6ruRrYrbjeGY3apwLDcVmHpE4QbV+6J0lSzxIRQ6gsGfngOi6zkaRWOSMtSVovRcRGwBnALwzRkjqD3w4lSVrvFOu7F1FZzjK2yuVIWk+5tEOSJEkqwaUdkiRJUgk9dmlH//79s7a2ttplSJIkaT03e/bsVzJztS/A6rFBura2llmzOvqtVZIkSVJTEfHnltpd2iFJkiSVYJCWJEmSSjBIS5IkSSX02DXSkiRJ6jwrVqygvr6eZcuWVbuULtOnTx8GDx5M796929TfIC1JkqTV1NfX069fP2pra4mIapfT6TKTV199lfr6erbffvs2jXFphyRJklazbNkytt566w0iRANEBFtvvfU6zcAbpCVJktSiDSVEN1jX6zVIS5IkSSW4RlqSJEmtqp30yw49X92UQ1vt8+KLL3L88cfz8ssvs9FGGzFx4kROO+00XnvtNY466ijq6uqora1l2rRpbLnllqvG/eUvf2G33XZj8uTJnHXWWQDccMMNXHDBBUQEgwYN4qc//Sn9+/dv1zX0uBnpiPhURFz15ptvVrsUSZIkdaJevXpx8cUXM2/ePB555BEuu+wy5s6dy5QpUxgzZgzPPfccY8aMYcqUKU3GnX766Rx88MGr9leuXMlpp53Gb37zG5566ilGjBjBpZde2u76elyQzsw7MnPiFltsUe1SJEmS1IlqamrYc889AejXrx9Dhw7lpZde4rbbbmP8+PEAjB8/nhkzZqwaM2PGDHbYYQeGDRu2qi0zyUyWLl1KZvLWW28xaNCgdtfX44K0JEmSNjx1dXU88cQT7L333ixatIiamhqgErYXL14MwNKlS7nooos499xzm4zt3bs3l19+OcOHD2fQoEHMnTuXCRMmtLsmg7TapHbSL5t8JEmSuso777zDEUccwSWXXMLmm2++xn7nnnsup59+On379m3SvmLFCi6//HKeeOIJFixYwIgRI7jwwgvbXZcPG0qSJKnbWrFiBUcccQTHHnssn/nMZwDYZpttWLhwITU1NSxcuJCBAwcC8Oijj3LTTTfxta99jTfeeIONNtqIPn36sPfeewOw4447AnDkkUeutq66DIO0JEmSuqXMZMKECQwdOpQzzjhjVfthhx3G1KlTmTRpElOnTmXcuHEA/O53v1vVZ/LkyfTt25dTTz2VBQsWMHfuXJYsWcKAAQO45557GDp0aLvrM0hLkiSpVW15XV1He+ihh7j++usZPnw4I0eOBOCCCy5g0qRJHHnkkVx99dUMGTKE6dOnr/U8gwYN4txzz2W//fajd+/efOhDH+Laa69td32Rme0+STWMHj06Z82aVe0yNhjN10VX4x8mSZLUdebNm9chs7Y9TUvXHRGzM3N0874+bChJkiSVYJCWJEmSSjBIS5IkSSUYpCVJkqQSDNKSJElSCb7+Tj3e8KnDm+w/Pf7pKlUiSZI2JAZp9TyTt2i6v/2Q6tQhSdKGpPl/f9t9vjdb7XLiiSdy5513MnDgQObMmQPAa6+9xlFHHUVdXR21tbVMmzaNLbfcknvuuYdJkybx7rvvsskmm/Cd73yHAw44oMn5DjvsMF544YVV52qvbrO0IyLeHxGzI+KT1a5FbTB5i6YfSZKkDnbCCSdw9913N2mbMmUKY8aM4bnnnmPMmDGrvuq7f//+3HHHHTz99NNMnTqVL3zhC03G3XLLLfTt27dD6+u0IB0R10TE4oiY06x9bET8ISLmR8SkRofOBqZ1Vj2SJEnqWfbbbz+22mqrJm233XYb48ePB2D8+PHMmDEDgFGjRjFo0CAAhg0bxrJly1i+fDkA77zzDt/73vc455xzOrS+zpyRvhYY27ghIjYGLgMOBnYDjomI3SLi48BcYFEn1iNJkqQebtGiRdTU1ABQU1PD4sWLV+tz8803M2rUKDbddFMAvvnNb3LmmWey2WabdWgtnbZGOjMfiIjaZs0fAeZn5gsAEfELYBzQF3g/lXD9t4j4VWb+vbNqkyRJ0vrpmWee4eyzz2bmzJkAPPnkk8yfP5/vf//71NXVdejP6uqHDbcFXmy0Xw/snZmnAkTECcArawrRETERmAgwZIgPmEmSJG1ottlmGxYuXEhNTQ0LFy5k4MCBq47V19dz+OGHc91117HjjjsC8PDDDzN79mxqa2tZuXIlixcvZv/99+f+++9vdy1d/bBhtNCWqzYyr83MO9c0ODOvyszRmTl6wIABnVKgJEmSuq/DDjuMqVOnAjB16lTGjRsHwBtvvMGhhx7KhRdeyL777ruq/8knn8yCBQuoq6vjwQcfZJdddumQEA1dPyNdD2zXaH8wsKCLa5AkSdK6asPr6jraMcccw/33388rr7zC4MGD+da3vsWkSZM48sgjufrqqxkyZAjTp08H4NJLL2X+/Pmcd955nHfeeQDMnDmzyYx1R+vqIP17YOeI2B54CTga+Py6nCAiPgV8aqedduqE8iRJktRd3HDDDS2233vvvau1nXPOOa2+laO2trbD3iENnfv6uxuAh4F/iIj6iJiQmSuBU4FfA/OAaZn5zLqcNzPvyMyJW2zhu4slSZJUPZ351o5j1tD+K+BXnfVzJUmSpK7Q474i3KUd6s7m7Tq0yf7QZ+dVqRJJktTZus1XhLeVSzskSZLUHfS4IC1JkiR1Bz0uSEfEpyLiqjff7PpXsEjS/2vv7oP1LOs7gX9/AjFTC0FNkJeQDRhbQglvRrGjg6wWCS+pRdhFCkuLYKSrLio44ixj03FaqpUdt5IW0xZFR5EXpUuQSlioMlodSCnF0FSXQnaJWIlUQwUFUq/9I4HmhISc3JznPM+dfD4zGc59nfs8+TLXJPme61zPdQPA03q3R7q1tizJsvnz57992FmYHLMv+vKY69VThxQEAHZi866cN6Gv9+3f+va47ps9e3Z233337LLLLtl1112zYsWKXHvttVm8eHFWrVqVO+64I/Pnz0+S3HLLLbnooovy5JNPZsqUKfmjP/qjvOENb5jQ3JvqXZGGbfGGPwDYsfz1X/91pk+f/sz1IYccki996Ut5xzveMea+6dOnZ9myZdl3332zcuXKHHfccfne9743sFyKNAAAvTJ37twtjh9xxBHPfPwrv/Ir+dnPfpYnnngiL3zhCweSo3d7pAEA2HlUVd70pjflla98ZZYuXTrur/viF7+YI444YmAlOunhirRzpAEAdh7f+MY3su++++bhhx/Osccem4MOOihHH330c37Nvffemw984ANZvnz5QLP1bkXaOdKjad6V88b8AgCYCPvuu2+SZK+99srJJ5+cO+644znvX7NmTU4++eR85jOfyctf/vKBZuvdijSMks2/abhmSDkAYEf02GOP5ec//3l23333PPbYY1m+fHk+9KEPbfX+H//4xznxxBNzySWX5LWvfe3A8ynSAABs03iPq5tIP/jBD3LyyScnSdavX5/f/M3fzIIFC3L99dfn3e9+d9auXZsTTzwxhx9+eG6++eZcdtllue+++/LhD384H/7wh5Mky5cvz1577TWQfIo07KCWnHfbmOt3Xj64czRH4fcFYMdz4IEH5u///u+fNX7yySc/U7A3dfHFF+fiiy+ejGhJelikvdkQtmzz87NzzJLhBAGAnYQ3GwIAQAe9K9IAADAKFGkAAOhAkQYAgA4UaQAA6MCpHTBAjoIDYEfxrNOhnqe5/7hqm/c8+OCDOeuss/LP//zPecELXpBFixbl/PPPz+LFi/Nnf/ZnmTFjRpLkD/7gD3LCCSckSe6555684x3vyKOPPpoXvOAFufPOOzN16tQJzf603hXp1tqyJMvmz5//9mFnAUaLb1wAdiy77rprLr300hx55JH513/917zyla/MsccemyR573vfmwsvvHDM/evXr8+ZZ56Zz372sznssMPyyCOPZLfddhtcvoG9MgAAPA/77LNP9tlnnyTJ7rvvnrlz5+Z73/veVu9fvnx5Dj300Bx22GFJkpe+9KUDzWePNAAAI2/16tX5u7/7uxx11FFJkssuuyyHHnpo3va2t+VHP/pRkuS73/1uqirHHXdcjjzyyHz0ox8daCZFGmAALj3tpDG/AOjuJz/5SU455ZR8/OMfzx577JHf+Z3fyT/90z/l7rvvzj777JMLLrggyYatHV//+tfzuc99Ll//+tdz/fXX59Zbbx1YLls7AHYwmxf3C66+cUhJAJ6/p556KqecckrOOOOMvOUtb0mSvOxlL3vm829/+9tz0kkb/t6bOXNmXv/612f69OlJkhNOOCF33XVX3vjGNw4kmxVpAABGUmst55xzTubOnZv3ve99z4x///vff+bj66+/PoccckiS5Ljjjss999yTxx9/POvXr8/Xvva1HHzwwQPLZ0UagAmxpS0sVsNhxzGe4+om2je+8Y189rOfzbx583L44Ycn2XDU3VVXXZW77747VZXZs2fnk5/8ZJLkxS9+cd73vvflVa96VaoqJ5xwQk488cSB5etdkXaONADAzuF1r3tdWmvPGn/6zOgtOfPMM3PmmWcOMtYzeleknSMNjJe9wgAMkj3SAADQgSINAAAdKNIAANBB7/ZIA8Dm7IcHhkGRBgbKkWgA7KgUaQZi1UFzx1wP4+xJAGDiLDnvtgl9vXde/oZt3vO2t70tN954Y/baa6+sXLkySfL+978/y5Yty5QpU/Lyl788n/rUp7Lnnnvmqaeeyrnnnpu77ror69evz1lnnZUPfvCDE5p5c/ZIA0yAJefdNuYXAM/fb//2b+crX/nKmLFjjz02K1euzD333JNf+qVfyiWXXJIkufbaa/PEE0/k29/+dv72b/82n/zkJ7N69eqB5rMizQ5v81Iznu+AGb/Nf/qQY5YMJwgAO5yjjz76WWX4TW960zMfv+Y1r8l1112XJKmqPPbYY1m/fn1++tOfZsqUKdljjz0Gms+KNAAAvXTFFVfk+OOPT5KceuqpedGLXpR99tkns2bNyoUXXpiXvOQlA/39e1ekq2phVS1dt27dsKMAADAkv//7v59dd901Z5xxRpLkjjvuyC677JKHHnooDzzwQC699NLcf//9A83QuyLdWlvWWls0bdq0YUcBAGAIrrzyytx444353Oc+l6pKknz+85/PggULsttuu2WvvfbKa1/72qxYsWKgOeyRBoDnwRnWMLm+8pWv5CMf+Ui+9rWv5Rd+4ReeGZ81a1Zuu+22nHnmmXn88cfzrW99K+95z3sGmkWRBgBgm4bxZv3TTz89X/3qV/PDH/4wM2fOzO/93u/lkksuyRNPPJFjjz02yYY3HF5++eV55zvfmbPPPjuHHHJIWms5++yzc+ihhw40nyINAMBIuuqqq541ds4552zx3l/8xV/MtddeO+hIY/RujzQAAIwCK9Kwk7CPEwAmlhVpAADowIo0bI/Fmx27eMCs4eQAAIbOijQAAHSgSAMAQAe2dgAAsE2bv2n9+Rrvm95//OMf59xzz83KlStTVbniiivyq7/6q0mSj33sY3n/+9+ftWvXZvr06ROabzwUaQAARtb555+fBQsW5LrrrsuTTz6Zxx9/PEny4IMP5pZbbsmsWcN7v5KtHQAAjKRHH300t99++zMPJ0t7bAAAESJJREFUYZkyZUr23HPPJMl73/vefPSjH01VDS2fIg0AwEi6//77M2PGjJx99tk54ogjcu655+axxx7LDTfckP322y+HHXbYUPPZ2gEAwEhav3597rrrrnziE5/IUUcdlfPPPz+LFy/O7bffnuXLlw873mgU6aqam+T8JNOT3Npa+9MhR4KRNu/Kec8au2YIOYDh8sRSdnQzZ87MzJkzc9RRRyVJTj311CxevDgPPPDAM6vRa9asyZFHHpk77rgje++996TmG9jWjqq6oqoerqqVm40vqKrvVNV9VXVRkrTWVrXWzkvyn5PMH1QmAAD6Y++9987++++f73znO0mSW2+9NUceeWQefvjhrF69OqtXr87MmTNz1113TXqJTga7Iv3pJJcl+czTA1W1S5IlSY5NsibJnVV1Q2vtH6rq15NctPFrABinJefdNuwIwE5gWD/x+MQnPpEzzjgjTz75ZA488MB86lOfGkqOLRlYkW6t3V5VszcbfnWS+1pr9ydJVX0hyZuT/ENr7YYkN1TVl5N8fkuvWVWLkixKMtSjTgAAmByHH354VqxYsdXPr169evLCbGay90jvl+TBTa7XJDmqqo5J8pYkL0xy09a+uLW2NMnSJJk/f34bXEwYDPsZAWDHMdlFeksH/bXW2leTfHVyowAAQHeTfY70miT7b3I9M8lD2/MCVbWwqpauW7duQoMBADBWazvXBoDt/f+d7CJ9Z5JXVNUBVTUlyVuT3LA9L9BaW9ZaWzRt2rSBBAQAIJk6dWoeeeSRnaZMt9byyCOPZOrUqeP+moFt7aiqq5Ick2R6Va1J8ruttb+oqncluTnJLkmuaK3dO6gMAAB0M3PmzKxZsyZr164ddpRJM3Xq1MycOXPc9w/y1I7TtzJ+U57jDYXbUlULkyycM2dO15dgCDY/nuudl79hSEkAgPHYbbfdcsABBww7xkib7K0dz5utHQAAjILeFWkAABgFijQAAHTQuyLt+DsAAEZB74q0PdIAAIyC3hVpAAAYBZP9iHAAoOcuPe2kZ41dcPWNQ0gCw9W7FWl7pAEAGAW9K9L2SAMAMAp6V6QBAGAU2CMN9Naqg+aOHThmyXCCALBTsiINAAAd9K5Ie7MhAACjoHdF2psNAQAYBb0r0gAAMAoUaQAA6MCpHex0Nn8il6dxAQBdWJEGAIAOerciXVULkyycM2fOsKMA7NSWnHfbsCMADFXvVqSd2gEAwCjoXZEGAIBRoEgDAEAHijQAAHSgSAMAQAeKNAAAdKBIAwBAB70r0lW1sKqWrlu3bthRAADYifWuSDtHGgCAUdC7JxuyY7j0tJOeNXbB1TcOIQkAfbL5vx/+7WCYFGnYitkXfflZY6unDiEIADCSere1AwAARoEiDQAAHSjSAADQgSINAAAdKNIAANCBIg0AAB0o0gAA0EHvirRHhAMAMAp6V6Q9IhwAgFHQuyINAACjQJEGAIAOFGkAAOhAkQYAgA4UaQAA6ECRBgCADhRpAADoQJEGAIAOFGkAAOhAkQYAgA52HXYAoF/mXTlvzPU1Q8oBAMOmSAMAjMOlp5005vqCq28cUhJGhSK9g1t10Nwx13P/cdWQkgAA7FgU6R3I5j9yT/zYHQZl829Sc8yS4QQBYGhGpkhX1W8kOTHJXkmWtNaWDzkSACNqyXm3DTsCwGCLdFVdkeSkJA+31g7ZZHxBkv+ZZJckf95a+8PW2l8m+cuqenGSjyVRpOFpi6eNvT5g1nByAADPGPTxd59OsmDTgaraJcmSJMcnOTjJ6VV18Ca3XLzx8wAAMLIGWqRba7cn+ZfNhl+d5L7W2v2ttSeTfCHJm2uDjyT5q9baXVt6vapaVFUrqmrF2rVrBxkdAACe0zAeyLJfkgc3uV6zcezdSX4tyalVdd6WvrC1trS1Nr+1Nn/GjBmDTwoAAFsxjDcb1hbGWmvtj5P88WSHAQCALoaxIr0myf6bXM9M8tB4v7iqFlbV0nXr1k14MAAAGK9hFOk7k7yiqg6oqilJ3prkhvF+cWttWWtt0bRp07Z9MwAADMhAi3RVXZXkm0l+uarWVNU5rbX1Sd6V5OYkq5Jc01q7d5A5AABgog10j3Rr7fStjN+U5KZB/t4AADBIw9ja8bzYIw0AwCgYmUeEj1drbVmSZfPnz3/7sLP00eaP1X3n5W8YUhIAgH7r3Yo0AACMgt4VaVs7AAAYBb0r0o6/AwBgFPSuSAMAwChQpAEAoANFGgAAOujd8XdVtTDJwjlz5gw7yvAt3myf+AGzhpMDAGAn1LsVaW82BABgFPSuSAMAwChQpAEAoANFGgAAOuhdkfZkQwAARkHvirQ3GwIAMAp6d/wdAMDO5tLTThpzfcHVNw4pCZvq3Yo0AACMAkUaAAA6UKQBAKCD3u2R3pkfET77oi+PuV49dUhBAADo34q0UzsAABgFvSvSAAAwCnq3tYOJ5TgdAIBurEgDAEAHijQAAHSgSAMAQAf2SAPAdlhy3m3DjgCMiN6tSFfVwqpaum7dumFHAQBgJ9a7Iu0caQAARkHvijQAAIwCRRoAADpQpAEAoANFGgAAOlCkAQCgA0UaAAA6UKQBAKADRRoAADpQpAEAoIPeFWmPCAcAYBT0rkh7RDgAAKOgd0UaAABGgSINAAAd7DrsAAAAjKZLTztpzPUFV984pCSjyYo0AAB0YEUa6I15V84bc33NkHIAQGJFGgAAOlGkAQCgA0UaAAA6sEcaoGdWHTR37MAxS4YTBGAnZ0UaAAA6UKQBAKADRRoAADpQpAEAoANFGgAAOhiJIl1VB1bVX1TVdcPOAgAA4zGwIl1VV1TVw1W1crPxBVX1naq6r6ouSpLW2v2ttXMGlQUAACbaIFekP51kwaYDVbVLkiVJjk9ycJLTq+rgAWYAAICBGFiRbq3dnuRfNht+dZL7Nq5AP5nkC0nePN7XrKpFVbWiqlasXbt2AtMCAMD2mew90vsleXCT6zVJ9quql1bV5UmOqKoPbu2LW2tLW2vzW2vzZ8yYMeisAACwVZP9iPDawlhrrT2S5LxJzgIAAJ1NdpFek2T/Ta5nJnloe16gqhYmWThnzpyJzAUAI2/JebcNOwKwicne2nFnkldU1QFVNSXJW5PcsD0v0Fpb1lpbNG3atIEEBACA8Rjk8XdXJflmkl+uqjVVdU5rbX2SdyW5OcmqJNe01u4dVAYAABiUgW3taK2dvpXxm5Lc1PV1be1gZzD7oi+PuV49dUhBAGBILj3tpDHXF1x945CSbN1IPNlwe9jaAQDAKOhdkQYAgFGgSAMAQAeTffzd82aPNEyyxZttozpg1nByAMCI6d2KtD3SAACMgt4VaQAAGAWKNAAAdNC7Il1VC6tq6bp164YdBQCAnVjvirQ90gAAjILeFWkAABgFijQAAHSgSAMAQAceyDIES867bcz1z370P8ZcX3D1jZMZB2BcVh00d+zAMUuGEwRgRPRuRdqbDQEAGAW9K9IAADAKFGkAAOhAkQYAgA4UaQAA6KB3RdojwgEAGAW9K9JO7QAAYBT0rkgDAMAoUKQBAKADRRoAADpQpAEAoANFGgAAOlCkAQCgg12HHWB7VdXCJAvnzJkz7CjjtuqguWMHjlkynCBAZ/OunDfm+poh5QBgdPRuRdo50gAAjILeFWkAABgFijQAAHSgSAMAQAeKNAAAdKBIAwBAB4o0AAB0oEgDAEAHijQAAHSgSAMAQAceEQ4APKcl59027Agwknq3Iu0R4QAAjILeFWkAABgFijQAAHSgSAMAQAeKNAAAdKBIAwBAB4o0AAB0oEgDAEAHijQAAHSgSAMAQAeKNAAAdKBIAwBAB4o0AAB0oEgDAEAHijQAAHSw67ADJElVvSjJnyR5MslXW2ufG3IkAAB4TgNbka6qK6rq4apaudn4gqr6TlXdV1UXbRx+S5LrWmtvT/Lrg8oEAAATZZBbOz6dZMGmA1W1S5IlSY5PcnCS06vq4CQzkzy48bZ/G2AmAACYEAPb2tFau72qZm82/Ook97XW7k+SqvpCkjcnWZMNZfruPEe5r6pFSRYlyaxZsyY+NDBaFk8be32AP/cAjI7JfrPhfvn3ledkQ4HeL8mXkpxSVX+aZNnWvri1trS1Nr+1Nn/GjBmDTQoAAM9hst9sWFsYa621x5KcPclZAACgs8lekV6TZP9NrmcmeWh7XqCqFlbV0nXr1k1oMAAA2B6TXaTvTPKKqjqgqqYkeWuSG7bnBVpry1pri6ZNm7btmwEAYEAGefzdVUm+meSXq2pNVZ3TWluf5F1Jbk6yKsk1rbV7B5UBAAAGZZCndpy+lfGbktzU9XWramGShXPmzOn6EgAA8Lz17hHhtnYAADAKelekAQBgFCjSAADQQe+KtOPvAAAYBb0r0vZIAwAwCnpXpAEAYBQo0gAA0EG11oadoZOqWpvk/yaZnuSHQ45Dd+avv8xdv5m//jJ3/Wb++uk/tNZmbD7Y2yL9tKpa0VqbP+wcdGP++svc9Zv56y9z12/mb8diawcAAHSgSAMAQAc7QpFeOuwAPC/mr7/MXb+Zv/4yd/1m/nYgvd8jDQAAw7AjrEgDAMCkU6QBAKCD3hXpqnpJVd1SVf9n439fvJX7rqiqh6tq5WRnZKyqWlBV36mq+6rqoi18vqrqjzd+/p6qOnIYOdmycczfQVX1zap6oqouHEZGtm4c83fGxj9391TV31TVYcPIybONY+7evHHe7q6qFVX1umHkZMu2NX+b3Peqqvq3qjp1MvMxMXpXpJNclOTW1torkty68XpLPp1kwWSFYsuqapckS5Icn+TgJKdX1cGb3XZ8klds/LUoyZ9Oaki2apzz9y9J/luSj01yPLZhnPP3QJLXt9YOTfLheCPUSBjn3N2a5LDW2uFJ3pbkzyc3JVszzvl7+r6PJLl5chMyUfpYpN+c5MqNH1+Z5De2dFNr7fZs+Aee4Xp1kvtaa/e31p5M8oVsmMNNvTnJZ9oG30qyZ1XtM9lB2aJtzl9r7eHW2p1JnhpGQJ7TeObvb1prP9p4+a0kMyc5I1s2nrn7Sfv3EwNelMTpAaNjPP/2Jcm7k3wxycOTGY6J08ci/bLW2veTZON/9xpyHp7bfkke3OR6zcax7b2H4TA3/ba983dOkr8aaCLGa1xzV1UnV9U/JvlyNqxKMxq2OX9VtV+Sk5NcPom5mGC7DjvAllTV/06y9xY+9d8nOwvPW21hbPNVk/Hcw3CYm34b9/xV1X/MhiJtn+1oGNfctdauT3J9VR2dDVtzfm3QwRiX8czfx5N8oLX2b1Vbup0+GMki3Vrb6l8EVfWDqtqntfb9jT/+9+OQ0bYmyf6bXM9M8lCHexgOc9Nv45q/qjo0G/bXHt9ae2SSsvHctuvPXmvt9qp6eVVNb639cODp2JbxzN/8JF/YWKKnJzmhqta31v5yciIyEfq4teOGJL+18ePfSvK/hpiFbbszySuq6oCqmpLkrdkwh5u6IclZG0/veE2SdU9v32HoxjN/jK5tzl9VzUrypST/pbX23SFkZMvGM3dzamML23ja0ZQkvhEaDducv9baAa212a212UmuS/Jflej+GckV6W34wyTXVNU5Sf5fkv+UJFW1b5I/b62dsPH6qiTHJJleVWuS/G5r7S+GE3nn1VpbX1XvyoZ3JO+S5IrW2r1Vdd7Gz1+e5KYkJyS5L8njSc4eVl7GGs/8VdXeSVYk2SPJz6vqPUkObq09OrTgJBn3n78PJXlpkj/Z2MnWt9bmDyszG4xz7k7JhkWIp5L8NMlpm7z5kCEa5/yxA/CIcAAA6KCPWzsAAGDoFGkAAOhAkQYAgA4UaQAA6ECRBgCADhRpAADoQJEGAIAO/j/ABYJAJs0WVAAAAABJRU5ErkJggg==
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<p>Clear (and generally unsurprising) conclusion from this: folding the fingerprints tends to increase computed similarity values. Going all the way down to 64bits does so dramatically.</p>
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<p>It's also worth looking at how much the folding changes the ranking of similarities. This is where I may get into trouble with people who are better at stats than I am, but I think the right metric for this is <a href="https://en.wikipedia.org/wiki/Spearman%27s_rank_correlation_coefficient">Spearman's rank correlation coefficient</a>:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
<span class="n">bcounts</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2048</span><span class="p">,</span><span class="mi">1024</span><span class="p">,</span><span class="mi">512</span><span class="p">,</span><span class="mi">256</span><span class="p">,</span><span class="mi">128</span><span class="p">,</span><span class="mi">64</span><span class="p">)</span>
<span class="k">for</span> <span class="n">bcount</span> <span class="ow">in</span> <span class="n">bcounts</span><span class="p">:</span>
<span class="n">r</span><span class="p">,</span><span class="n">p</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">spearmanr</span><span class="p">(</span><span class="n">sims</span><span class="p">[</span><span class="n">bcount</span><span class="p">],</span><span class="n">sims</span><span class="p">[</span><span class="mi">4096</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">bcount</span><span class="si">}</span><span class="s2"> bits: r=</span><span class="si">{</span><span class="n">r</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> p=</span><span class="si">{</span><span class="n">p</span><span class="si">:</span><span class="s2">.3g</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>2048 bits: r=0.997 p=0
1024 bits: r=0.994 p=0
512 bits: r=0.985 p=0
256 bits: r=0.965 p=0
128 bits: r=0.919 p=0
64 bits: r=0.811 p=0
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<p>It's nice to see how high these are. Even the super-short 64 bit fingerprints maintain the ranking of these pairs reasonably well.</p>
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<p>In <a href="https://rdkit.blogspot.com/2020/06/number-of-bits-set-compared-between.html">another blog post</a> I showed that the Morgan2 fingerprint sets comparatively few bits compared to some of the other fingerprints available in the RDKit: compare ~50 bits per molecule with MFP2 with the ~1000 bits per molecule set by the RDKit fingerprint.</p>
<p>I assume that the higher bit densities of the RDKit fingerprint will cause similarity values to increase more rapidly as the fingerprint is folded.</p>
<p>Let's test that assumption:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rdksims</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">row</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">rows</span><span class="p">):</span>
<span class="n">m1</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
<span class="n">m2</span> <span class="o">=</span> <span class="n">row</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
<span class="k">for</span> <span class="n">bitsize</span> <span class="ow">in</span> <span class="p">(</span><span class="mi">64</span><span class="p">,</span> <span class="mi">128</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">512</span><span class="p">,</span><span class="mi">1024</span><span class="p">,</span><span class="mi">2048</span><span class="p">,</span><span class="mi">4096</span><span class="p">):</span>
<span class="n">fp1</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">RDKFingerprint</span><span class="p">(</span><span class="n">m1</span><span class="p">,</span><span class="n">fpSize</span><span class="o">=</span><span class="n">bitsize</span><span class="p">)</span>
<span class="n">fp2</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">RDKFingerprint</span><span class="p">(</span><span class="n">m2</span><span class="p">,</span><span class="n">fpSize</span><span class="o">=</span><span class="n">bitsize</span><span class="p">)</span>
<span class="n">rdksims</span><span class="p">[</span><span class="n">bitsize</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">DataStructs</span><span class="o">.</span><span class="n">TanimotoSimilarity</span><span class="p">(</span><span class="n">fp1</span><span class="p">,</span><span class="n">fp2</span><span class="p">))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="p">(</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">%</span><span class="k">5000</span>: print("Done:",i+1)
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<pre>Done: 5000
Done: 10000
Done: 15000
Done: 20000
Done: 25000
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<div class="prompt input_prompt">In [76]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">figsize</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span>
<span class="n">bcounts</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2048</span><span class="p">,</span><span class="mi">1024</span><span class="p">,</span><span class="mi">512</span><span class="p">,</span><span class="mi">256</span><span class="p">,</span><span class="mi">128</span><span class="p">,</span><span class="mi">64</span><span class="p">)</span>
<span class="n">diffs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">bcount</span> <span class="ow">in</span> <span class="n">bcounts</span><span class="p">:</span>
<span class="n">diffs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">rdksims</span><span class="p">[</span><span class="n">bcount</span><span class="p">])</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">rdksims</span><span class="p">[</span><span class="mi">4096</span><span class="p">]))</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span>
<span class="n">hist</span><span class="p">(</span><span class="n">diffs</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">bcounts</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">);</span>
<span class="n">legend</span><span class="p">();</span>
<span class="n">title</span><span class="p">(</span><span class="s1">'delta similarity'</span><span class="p">);</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">)</span>
<span class="n">hist</span><span class="p">(</span><span class="n">diffs</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">bcounts</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span><span class="n">log</span><span class="o">=</span><span class="kc">True</span><span class="p">);</span>
<span class="n">legend</span><span class="p">();</span>
<span class="n">title</span><span class="p">(</span><span class="s1">'RDKit: delta similarity'</span><span class="p">);</span>
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<p>The Spearman coefficients here show how quickly things degrade:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">scipy</span> <span class="kn">import</span> <span class="n">stats</span>
<span class="n">bcounts</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2048</span><span class="p">,</span><span class="mi">1024</span><span class="p">,</span><span class="mi">512</span><span class="p">,</span><span class="mi">256</span><span class="p">,</span><span class="mi">128</span><span class="p">,</span><span class="mi">64</span><span class="p">)</span>
<span class="k">for</span> <span class="n">bcount</span> <span class="ow">in</span> <span class="n">bcounts</span><span class="p">:</span>
<span class="n">r</span><span class="p">,</span><span class="n">p</span> <span class="o">=</span> <span class="n">stats</span><span class="o">.</span><span class="n">spearmanr</span><span class="p">(</span><span class="n">rdksims</span><span class="p">[</span><span class="n">bcount</span><span class="p">],</span><span class="n">rdksims</span><span class="p">[</span><span class="mi">4096</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">bcount</span><span class="si">}</span><span class="s2"> bits: r=</span><span class="si">{</span><span class="n">r</span><span class="si">:</span><span class="s2">.3f</span><span class="si">}</span><span class="s2"> p=</span><span class="si">{</span><span class="n">p</span><span class="si">:</span><span class="s2">.3g</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>2048 bits: r=0.986 p=0
1024 bits: r=0.911 p=0
512 bits: r=0.735 p=0
256 bits: r=0.506 p=0
128 bits: r=0.287 p=0
64 bits: r=0.103 p=1.39e-59
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<p>Clearly the RDKit fingerprints generated with the default parameters are not the ideal candidates to be folded into super-short fingerprints.</p>
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<h1 id="Impact-on-similarity-searching">Impact on similarity searching<a class="anchor-link" href="#Impact-on-similarity-searching">¶</a></h1>
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<p>Let's move on to looking at the impact shorter fingerprints have on similarity searching, specifically the number of results retrieved for queries using different similarity cutoffs.</p>
<p>Our test database for this is the SDF from ChEMBL27. I downloaded this directly from the <a href="ftp://ftp.ebi.ac.uk/pub/databases/chembl/ChEMBLdb/latest/">ChEMBL website</a>.</p>
<p>Read in that SDF and generate MFP2 fingerprints:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">pickle</span><span class="o">,</span> <span class="nn">time</span><span class="o">,</span> <span class="nn">gzip</span>
<span class="n">gz</span> <span class="o">=</span> <span class="n">gzip</span><span class="o">.</span><span class="n">GzipFile</span><span class="p">(</span><span class="s1">'/home/glandrum/Downloads/chembl_27.sdf.gz'</span><span class="p">)</span>
<span class="n">suppl</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">ForwardSDMolSupplier</span><span class="p">(</span><span class="n">gz</span><span class="p">)</span>
<span class="n">RDLogger</span><span class="o">.</span><span class="n">DisableLog</span><span class="p">(</span><span class="s2">"rdApp.warning"</span><span class="p">)</span>
<span class="n">t1</span><span class="o">=</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">data</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">mol</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">suppl</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="p">((</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">%</span><span class="k">50000</span>):
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Processed </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2"> molecules in </span><span class="si">{</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">t1</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> seconds"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">mol</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">mol</span><span class="o">.</span><span class="n">GetNumAtoms</span><span class="p">()</span><span class="o">></span><span class="mi">70</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">fp</span> <span class="o">=</span> <span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">GetMorganFingerprintAsBitVect</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">nBits</span><span class="o">=</span><span class="mi">4096</span><span class="p">)</span>
<span class="n">smi</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolToSmiles</span><span class="p">(</span><span class="n">mol</span><span class="p">)</span>
<span class="n">data</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">mol</span><span class="o">.</span><span class="n">GetProp</span><span class="p">(</span><span class="s1">'chembl_id'</span><span class="p">),</span><span class="n">smi</span><span class="p">,</span><span class="n">fp</span><span class="p">))</span>
<span class="n">t2</span><span class="o">=</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Processed </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="si">}</span><span class="s2"> molecules in </span><span class="si">{</span><span class="p">(</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> seconds"</span><span class="p">)</span>
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<pre>Processed 50000 molecules in 15.7 seconds
Processed 100000 molecules in 30.9 seconds
Processed 150000 molecules in 45.8 seconds
Processed 200000 molecules in 61.8 seconds
Processed 250000 molecules in 79.1 seconds
Processed 300000 molecules in 95.5 seconds
Processed 350000 molecules in 113.1 seconds
Processed 400000 molecules in 132.1 seconds
Processed 450000 molecules in 150.2 seconds
Processed 500000 molecules in 167.8 seconds
Processed 550000 molecules in 185.2 seconds
Processed 600000 molecules in 203.7 seconds
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<pre>RDKit ERROR: [08:36:27] Explicit valence for atom # 0 N, 4, is greater than permitted
RDKit ERROR: [08:36:27] ERROR: Could not sanitize molecule ending on line 44530308
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<pre>Processed 650000 molecules in 222.4 seconds
Processed 700000 molecules in 243.6 seconds
Processed 750000 molecules in 259.4 seconds
Processed 800000 molecules in 273.9 seconds
Processed 850000 molecules in 288.2 seconds
Processed 900000 molecules in 302.9 seconds
Processed 950000 molecules in 317.4 seconds
Processed 1000000 molecules in 333.0 seconds
Processed 1050000 molecules in 349.7 seconds
Processed 1100000 molecules in 366.1 seconds
Processed 1150000 molecules in 384.8 seconds
Processed 1200000 molecules in 401.7 seconds
Processed 1250000 molecules in 419.5 seconds
Processed 1300000 molecules in 438.1 seconds
Processed 1350000 molecules in 458.1 seconds
Processed 1400000 molecules in 475.6 seconds
Processed 1450000 molecules in 492.3 seconds
Processed 1500000 molecules in 508.3 seconds
Processed 1550000 molecules in 522.2 seconds
Processed 1600000 molecules in 538.5 seconds
Processed 1650000 molecules in 556.1 seconds
Processed 1700000 molecules in 574.4 seconds
Processed 1750000 molecules in 593.8 seconds
Processed 1800000 molecules in 613.1 seconds
Processed 1850000 molecules in 631.7 seconds
Processed 1900000 molecules in 650.2 seconds
Processed 1905747 molecules in 665.7 seconds
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<p>Save those to disk so that we don't have to do that work again</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">data</span><span class="p">,</span><span class="nb">open</span><span class="p">(</span><span class="s1">'../data/chembl27_fps.pkl'</span><span class="p">,</span><span class="s1">'wb+'</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">data</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">'../data/chembl27_fps.pkl'</span><span class="p">,</span><span class="s1">'rb'</span><span class="p">))</span>
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<p>Our queries are the set of "very active" compounds (compounds with subnanomolar measured Ki values) I <a href="https://rdkit.blogspot.com/2020/05/binary-molecules-and-cartridge.html">previously collected</a> from ChEMBL26.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">gz</span> <span class="o">=</span> <span class="n">gzip</span><span class="o">.</span><span class="n">GzipFile</span><span class="p">(</span><span class="s1">'../data/chembl26_very_active.sdf.gz'</span><span class="p">)</span>
<span class="n">suppl</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">ForwardSDMolSupplier</span><span class="p">(</span><span class="n">gz</span><span class="p">)</span>
<span class="n">t1</span><span class="o">=</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">queries</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">mol</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">suppl</span><span class="p">):</span>
<span class="k">if</span> <span class="ow">not</span> <span class="p">((</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span><span class="o">%</span><span class="k">50000</span>):
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Processed </span><span class="si">{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2"> molecules in </span><span class="si">{</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">t1</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> seconds"</span><span class="p">)</span>
<span class="k">if</span> <span class="n">mol</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">mol</span><span class="o">.</span><span class="n">GetNumAtoms</span><span class="p">()</span><span class="o">></span><span class="mi">70</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">fp</span> <span class="o">=</span> <span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">GetMorganFingerprintAsBitVect</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">nBits</span><span class="o">=</span><span class="mi">4096</span><span class="p">)</span>
<span class="n">smi</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolToSmiles</span><span class="p">(</span><span class="n">mol</span><span class="p">)</span>
<span class="n">queries</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">mol</span><span class="o">.</span><span class="n">GetProp</span><span class="p">(</span><span class="s1">'compound_chembl_id'</span><span class="p">),</span><span class="n">smi</span><span class="p">,</span><span class="n">fp</span><span class="p">))</span>
<span class="n">t2</span><span class="o">=</span><span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"Processed </span><span class="si">{</span><span class="nb">len</span><span class="p">(</span><span class="n">queries</span><span class="p">)</span><span class="si">}</span><span class="s2"> molecules in </span><span class="si">{</span><span class="p">(</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span><span class="p">)</span><span class="si">:</span><span class="s2">.1f</span><span class="si">}</span><span class="s2"> seconds"</span><span class="p">)</span>
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<pre>Processed 34348 molecules in 14.5 seconds
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<p>Now collect the data. We'll use 1000 randomly selected queries against the first million pubchem compounds.</p>
<p>We try a variety of different fingerprint sizes along with different similarity thresholds. For each threshold/size combination we keep track of the number of neighbors found for each query as well as the number of missing neighbors (neighbors found at that threshold with a 4096 bit fingerprint that were not found with the folded fingerprint).</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">random</span>
<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mh">0xf00d</span><span class="p">)</span>
<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">queries</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dsize</span> <span class="o">=</span> <span class="mi">1000000</span>
<span class="n">nQueries</span> <span class="o">=</span> <span class="mi">1000</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="k">def</span> <span class="nf">fold_fp</span><span class="p">(</span><span class="n">fp</span><span class="p">,</span><span class="n">sz</span><span class="p">):</span>
<span class="k">return</span> <span class="n">DataStructs</span><span class="o">.</span><span class="n">FoldFingerprint</span><span class="p">(</span><span class="n">fp</span><span class="p">,</span><span class="n">fp</span><span class="o">.</span><span class="n">GetNumBits</span><span class="p">()</span><span class="o">//</span><span class="n">sz</span><span class="p">)</span>
<span class="n">thresholds</span> <span class="o">=</span> <span class="p">[</span><span class="mf">0.5</span><span class="p">,</span><span class="mf">0.6</span><span class="p">,</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.9</span><span class="p">]</span>
<span class="n">sizes</span> <span class="o">=</span> <span class="p">[</span><span class="mi">64</span><span class="p">,</span><span class="mi">128</span><span class="p">,</span><span class="mi">256</span><span class="p">,</span><span class="mi">512</span><span class="p">,</span><span class="mi">1024</span><span class="p">,</span><span class="mi">2048</span><span class="p">]</span>
<span class="n">results</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span><span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">))</span>
<span class="n">missed</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="k">lambda</span><span class="p">:</span><span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">))</span>
<span class="n">dbfps</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[:</span><span class="n">dsize</span><span class="p">]]</span>
<span class="k">for</span> <span class="n">sz</span> <span class="ow">in</span> <span class="n">sizes</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Doing fp len </span><span class="si">{</span><span class="n">sz</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
<span class="n">tfps</span> <span class="o">=</span> <span class="p">[]</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\t</span><span class="s1"> folding'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">cid</span><span class="p">,</span><span class="n">smi</span><span class="p">,</span><span class="n">fp</span> <span class="ow">in</span> <span class="n">data</span><span class="p">[:</span><span class="n">dsize</span><span class="p">]:</span>
<span class="n">tfps</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">fold_fp</span><span class="p">(</span><span class="n">fp</span><span class="p">,</span><span class="n">sz</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\t</span><span class="s1"> running queries'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">qcid</span><span class="p">,</span><span class="n">qsmi</span><span class="p">,</span><span class="n">qfp</span> <span class="ow">in</span> <span class="n">queries</span><span class="p">[:</span><span class="n">nQueries</span><span class="p">]:</span>
<span class="n">nqfp</span> <span class="o">=</span> <span class="n">fold_fp</span><span class="p">(</span><span class="n">qfp</span><span class="p">,</span><span class="n">sz</span><span class="p">)</span>
<span class="n">sims</span> <span class="o">=</span> <span class="n">DataStructs</span><span class="o">.</span><span class="n">BulkTanimotoSimilarity</span><span class="p">(</span><span class="n">nqfp</span><span class="p">,</span><span class="n">tfps</span><span class="p">)</span>
<span class="n">osims</span> <span class="o">=</span> <span class="n">DataStructs</span><span class="o">.</span><span class="n">BulkTanimotoSimilarity</span><span class="p">(</span><span class="n">qfp</span><span class="p">,</span><span class="n">dbfps</span><span class="p">)</span>
<span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="n">thresholds</span><span class="p">:</span>
<span class="n">oindices</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">i</span> <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="n">x</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">osims</span><span class="p">)</span> <span class="k">if</span> <span class="n">x</span><span class="o">>=</span><span class="n">thresh</span><span class="p">)</span>
<span class="k">if</span> <span class="n">sz</span><span class="o">==</span><span class="n">sizes</span><span class="p">[</span><span class="mi">0</span><span class="p">]:</span>
<span class="n">results</span><span class="p">[</span><span class="mi">4096</span><span class="p">][</span><span class="n">thresh</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">oindices</span><span class="p">))</span>
<span class="n">indices</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">i</span> <span class="k">for</span> <span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="n">x</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sims</span><span class="p">)</span> <span class="k">if</span> <span class="n">x</span><span class="o">>=</span><span class="n">thresh</span><span class="p">)</span>
<span class="n">cnt</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">indices</span><span class="p">)</span>
<span class="n">results</span><span class="p">[</span><span class="n">sz</span><span class="p">][</span><span class="n">thresh</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cnt</span><span class="p">)</span>
<span class="n">missed</span><span class="p">[</span><span class="n">sz</span><span class="p">][</span><span class="n">thresh</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">oindices</span><span class="o">.</span><span class="n">difference</span><span class="p">(</span><span class="n">indices</span><span class="p">)))</span>
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<pre>Doing fp len 64
folding
running queries
Doing fp len 128
folding
running queries
Doing fp len 256
folding
running queries
Doing fp len 512
folding
running queries
Doing fp len 1024
folding
running queries
Doing fp len 2048
folding
running queries
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<p>that took a while to run, so save the results</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dres</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">results</span><span class="p">:</span>
<span class="n">dres</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
<span class="n">dmissed</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">missed</span><span class="p">:</span>
<span class="n">dmissed</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="n">missed</span><span class="p">[</span><span class="n">k</span><span class="p">])</span>
<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">((</span><span class="n">dres</span><span class="p">,</span><span class="n">dmissed</span><span class="p">),</span><span class="nb">open</span><span class="p">(</span><span class="s1">'../data/size_and_neighbors_results.mfp2.pkl'</span><span class="p">,</span><span class="s1">'wb+'</span><span class="p">))</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">results</span><span class="p">,</span><span class="n">missed</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="nb">open</span><span class="p">(</span><span class="s1">'../data/size_and_neighbors_results.mfp2.pkl'</span><span class="p">,</span><span class="s1">'rb'</span><span class="p">))</span>
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<h2 id="Comparing-the-the-number-of-neighbors-retrieved">Comparing the the number of neighbors retrieved<a class="anchor-link" href="#Comparing-the-the-number-of-neighbors-retrieved">¶</a></h2>
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<p>To get a sense of what the data look like, pick a couple of threshold values and do a direct comparison of the number of neighbors found at the other bit counts with the number found at 4096 bits.</p>
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<div class="prompt input_prompt">In [58]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">thresh</span> <span class="o">=</span> <span class="mf">0.9</span>
<span class="n">bcounts</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2048</span><span class="p">,</span><span class="mi">1024</span><span class="p">,</span><span class="mi">512</span><span class="p">,</span><span class="mi">256</span><span class="p">,</span><span class="mi">128</span><span class="p">,</span><span class="mi">64</span><span class="p">)</span>
<span class="n">figsize</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">15</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">yv</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">bcounts</span><span class="p">):</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">xv</span> <span class="o">=</span> <span class="mi">4096</span>
<span class="n">mv</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">xv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])</span>
<span class="n">hexbin</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">xv</span><span class="p">][</span><span class="n">thresh</span><span class="p">],</span><span class="n">results</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="s1">'log'</span><span class="p">,</span><span class="n">cmap</span><span class="o">=</span><span class="s1">'Blues'</span><span class="p">)</span>
<span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="n">mv</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="n">mv</span><span class="p">]);</span>
<span class="n">xlabel</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">xv</span><span class="si">}</span><span class="s1"> bits'</span><span class="p">)</span>
<span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">yv</span><span class="si">}</span><span class="s1"> bits'</span><span class="p">);</span>
<span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'number of neighbors, thresh=</span><span class="si">{</span><span class="n">thresh</span><span class="si">:</span><span class="s1">0.1f</span><span class="si">}</span><span class="s1">'</span><span class="p">);</span>
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
"
>
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<p>Do the same thing with a threshold of 0.6</p>
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<div class="input">
<div class="prompt input_prompt">In [59]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">thresh</span> <span class="o">=</span> <span class="mf">0.6</span>
<span class="n">bcounts</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2048</span><span class="p">,</span><span class="mi">1024</span><span class="p">,</span><span class="mi">512</span><span class="p">,</span><span class="mi">256</span><span class="p">,</span><span class="mi">128</span><span class="p">,</span><span class="mi">64</span><span class="p">)</span>
<span class="n">figsize</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">15</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">yv</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">bcounts</span><span class="p">):</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">xv</span> <span class="o">=</span> <span class="mi">4096</span>
<span class="n">mv</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">xv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])</span>
<span class="n">hexbin</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">xv</span><span class="p">][</span><span class="n">thresh</span><span class="p">],</span><span class="n">results</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="s1">'log'</span><span class="p">,</span><span class="n">cmap</span><span class="o">=</span><span class="s1">'Blues'</span><span class="p">)</span>
<span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="n">mv</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="n">mv</span><span class="p">]);</span>
<span class="n">xlabel</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">xv</span><span class="si">}</span><span class="s1"> bits'</span><span class="p">)</span>
<span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">yv</span><span class="si">}</span><span class="s1"> bits'</span><span class="p">);</span>
<span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'number of neighbors, thresh=</span><span class="si">{</span><span class="n">thresh</span><span class="si">:</span><span class="s1">0.1f</span><span class="si">}</span><span class="s1">'</span><span class="p">);</span>
</pre></div>
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<p>We can see here that the more folded fingerprints tend to yield more neighors at a given threshold. This makese sense given what we saw above, which is that similarity values tend to be higher as fingerprints are folded.</p>
<p>It's also clear, though not super surprising, that the 64 bit fingerprint isn't going to be useful with low similarity thresholds: we just get way too many extra hits.</p>
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<h2 id="How-many-hits-do-we-miss?">How many hits do we miss?<a class="anchor-link" href="#How-many-hits-do-we-miss?">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">thresh</span> <span class="o">=</span> <span class="mf">0.8</span>
<span class="n">bcounts</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2048</span><span class="p">,</span><span class="mi">1024</span><span class="p">,</span><span class="mi">512</span><span class="p">,</span><span class="mi">256</span><span class="p">,</span><span class="mi">128</span><span class="p">,</span><span class="mi">64</span><span class="p">)</span>
<span class="n">hist</span><span class="p">([</span><span class="n">missed</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="mf">0.8</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">bcounts</span><span class="p">],</span><span class="n">log</span><span class="o">=</span><span class="s1">'true'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">bcounts</span><span class="p">]);</span>
<span class="n">legend</span><span class="p">();</span>
<span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'number of neighbors missed, thresh=</span><span class="si">{</span><span class="n">thresh</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">'</span><span class="p">);</span>
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<div class="prompt input_prompt">In [29]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">thresh</span> <span class="o">=</span> <span class="mf">0.7</span>
<span class="n">bcounts</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2048</span><span class="p">,</span><span class="mi">1024</span><span class="p">,</span><span class="mi">512</span><span class="p">,</span><span class="mi">256</span><span class="p">,</span><span class="mi">128</span><span class="p">,</span><span class="mi">64</span><span class="p">)</span>
<span class="n">hist</span><span class="p">([</span><span class="n">missed</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="n">thresh</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">bcounts</span><span class="p">],</span><span class="n">log</span><span class="o">=</span><span class="s1">'true'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">bcounts</span><span class="p">]);</span>
<span class="n">legend</span><span class="p">();</span>
<span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'number of neighbors missed, thresh=</span><span class="si">{</span><span class="n">thresh</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">'</span><span class="p">);</span>
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<div class="prompt input_prompt">In [30]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">figsize</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">4</span><span class="p">)</span>
<span class="n">thresh</span> <span class="o">=</span> <span class="mf">0.6</span>
<span class="n">bcounts</span> <span class="o">=</span> <span class="p">(</span><span class="mi">2048</span><span class="p">,</span><span class="mi">1024</span><span class="p">,</span><span class="mi">512</span><span class="p">,</span><span class="mi">256</span><span class="p">,</span><span class="mi">128</span><span class="p">,</span><span class="mi">64</span><span class="p">)</span>
<span class="n">hist</span><span class="p">([</span><span class="n">missed</span><span class="p">[</span><span class="n">x</span><span class="p">][</span><span class="n">thresh</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">bcounts</span><span class="p">],</span><span class="n">log</span><span class="o">=</span><span class="s1">'true'</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">bcounts</span><span class="p">]);</span>
<span class="n">legend</span><span class="p">();</span>
<span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'number of neighbors missed, thresh=</span><span class="si">{</span><span class="n">thresh</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">'</span><span class="p">);</span>
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<p>Look at result counts where hits were missed</p>
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<div class="prompt input_prompt">In [31]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">thresh</span> <span class="o">=</span> <span class="mf">0.9</span>
<span class="n">figsize</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">yv</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">bcounts</span><span class="p">):</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">xv</span> <span class="o">=</span> <span class="mi">4096</span>
<span class="n">tresx</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">xv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])</span> <span class="k">if</span> <span class="n">missed</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">][</span><span class="n">i</span><span class="p">]]</span>
<span class="n">tresy</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])</span> <span class="k">if</span> <span class="n">missed</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">][</span><span class="n">i</span><span class="p">]]</span>
<span class="n">tresr</span> <span class="o">=</span> <span class="p">[</span><span class="mi">20</span><span class="o">*</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">missed</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])</span> <span class="k">if</span> <span class="n">x</span><span class="p">]</span>
<span class="n">mv</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">tresx</span><span class="p">)</span>
<span class="n">scatter</span><span class="p">(</span><span class="n">tresx</span><span class="p">,</span><span class="n">tresy</span><span class="p">,</span><span class="n">tresr</span><span class="p">)</span>
<span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="n">mv</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="n">mv</span><span class="p">]);</span>
<span class="c1">#xlabel(f'{xv} bits')</span>
<span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">yv</span><span class="si">}</span><span class="s1"> bits'</span><span class="p">);</span>
<span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'number of neighbors, thresh=</span><span class="si">{</span><span class="n">thresh</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">'</span><span class="p">);</span>
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<div class="prompt input_prompt">In [32]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">thresh</span> <span class="o">=</span> <span class="mf">0.7</span>
<span class="n">figsize</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">yv</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">bcounts</span><span class="p">):</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">xv</span> <span class="o">=</span> <span class="mi">4096</span>
<span class="n">tresx</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">xv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])</span> <span class="k">if</span> <span class="n">missed</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">][</span><span class="n">i</span><span class="p">]]</span>
<span class="n">tresy</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])</span> <span class="k">if</span> <span class="n">missed</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">][</span><span class="n">i</span><span class="p">]]</span>
<span class="n">tresr</span> <span class="o">=</span> <span class="p">[</span><span class="mi">20</span><span class="o">*</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">missed</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])</span> <span class="k">if</span> <span class="n">x</span><span class="p">]</span>
<span class="n">mv</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">tresx</span><span class="p">)</span>
<span class="n">scatter</span><span class="p">(</span><span class="n">tresx</span><span class="p">,</span><span class="n">tresy</span><span class="p">,</span><span class="n">tresr</span><span class="p">)</span>
<span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="n">mv</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="n">mv</span><span class="p">]);</span>
<span class="c1">#xlabel(f'{xv} bits')</span>
<span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">yv</span><span class="si">}</span><span class="s1"> bits'</span><span class="p">);</span>
<span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'number of neighbors, thresh=</span><span class="si">{</span><span class="n">thresh</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">'</span><span class="p">);</span>
</pre></div>
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<div class="prompt input_prompt">In [39]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">thresh</span> <span class="o">=</span> <span class="mf">0.6</span>
<span class="n">figsize</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">yv</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">bcounts</span><span class="p">):</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">xv</span> <span class="o">=</span> <span class="mi">4096</span>
<span class="n">tresx</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">xv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])</span> <span class="k">if</span> <span class="n">missed</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">][</span><span class="n">i</span><span class="p">]]</span>
<span class="n">tresy</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])</span> <span class="k">if</span> <span class="n">missed</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">][</span><span class="n">i</span><span class="p">]]</span>
<span class="n">tresr</span> <span class="o">=</span> <span class="p">[</span><span class="mi">20</span><span class="o">*</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">missed</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])</span> <span class="k">if</span> <span class="n">x</span><span class="p">]</span>
<span class="n">mv</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">tresx</span><span class="p">)</span>
<span class="n">sc</span> <span class="o">=</span> <span class="n">scatter</span><span class="p">(</span><span class="n">tresx</span><span class="p">,</span><span class="n">tresy</span><span class="p">,</span><span class="n">tresr</span><span class="p">)</span>
<span class="n">plot</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span><span class="n">mv</span><span class="p">],[</span><span class="mi">0</span><span class="p">,</span><span class="n">mv</span><span class="p">]);</span>
<span class="c1">#xlabel(f'{xv} bits')</span>
<span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">yv</span><span class="si">}</span><span class="s1"> bits'</span><span class="p">);</span>
<span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'number of neighbors, thresh=</span><span class="si">{</span><span class="n">thresh</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">'</span><span class="p">);</span>
</pre></div>
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<h2 id="Efficiency,-look-at-the-number-of-extra-hits">Efficiency, look at the number of extra hits<a class="anchor-link" href="#Efficiency,-look-at-the-number-of-extra-hits">¶</a></h2>
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<div class="prompt input_prompt">In [49]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">thresh</span> <span class="o">=</span> <span class="mf">0.9</span>
<span class="n">figsize</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">yv</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">bcounts</span><span class="p">):</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">xv</span> <span class="o">=</span> <span class="mi">4096</span>
<span class="n">delt</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">-</span><span class="n">results</span><span class="p">[</span><span class="n">xv</span><span class="p">][</span><span class="n">thresh</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])]</span>
<span class="n">tresx</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">xv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])</span> <span class="k">if</span> <span class="n">delt</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">>=</span><span class="mi">0</span><span class="p">]</span>
<span class="n">tresy</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">delt</span><span class="p">)</span> <span class="k">if</span> <span class="n">x</span><span class="o">>=</span><span class="mi">0</span><span class="p">]</span>
<span class="n">mv</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">tresx</span><span class="p">)</span>
<span class="n">hexbin</span><span class="p">(</span><span class="n">tresx</span><span class="p">,</span><span class="n">tresy</span><span class="p">,</span><span class="n">bins</span><span class="o">=</span><span class="s1">'log'</span><span class="p">,</span><span class="n">cmap</span><span class="o">=</span><span class="s1">'Blues'</span><span class="p">)</span>
<span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s1">'extra hits'</span><span class="p">);</span>
<span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'extra hits with </span><span class="si">{</span><span class="n">yv</span><span class="si">}</span><span class="s1"> bits, thresh=</span><span class="si">{</span><span class="n">thresh</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">'</span><span class="p">);</span>
</pre></div>
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<div class="prompt input_prompt">In [48]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">thresh</span> <span class="o">=</span> <span class="mf">0.7</span>
<span class="n">figsize</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">yv</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">bcounts</span><span class="p">):</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">xv</span> <span class="o">=</span> <span class="mi">4096</span>
<span class="n">delt</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">-</span><span class="n">results</span><span class="p">[</span><span class="n">xv</span><span class="p">][</span><span class="n">thresh</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])]</span>
<span class="n">tresx</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">xv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])</span> <span class="k">if</span> <span class="n">delt</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">>=</span><span class="mi">0</span><span class="p">]</span>
<span class="n">tresy</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">delt</span><span class="p">)</span> <span class="k">if</span> <span class="n">x</span><span class="o">>=</span><span class="mi">0</span><span class="p">]</span>
<span class="n">mv</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">tresx</span><span class="p">)</span>
<span class="n">hexbin</span><span class="p">(</span><span class="n">tresx</span><span class="p">,</span><span class="n">tresy</span><span class="p">,</span><span class="n">bins</span><span class="o">=</span><span class="s1">'log'</span><span class="p">,</span><span class="n">cmap</span><span class="o">=</span><span class="s1">'Blues'</span><span class="p">)</span>
<span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s1">'extra hits'</span><span class="p">);</span>
<span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'extra hits with </span><span class="si">{</span><span class="n">yv</span><span class="si">}</span><span class="s1"> bits, thresh=</span><span class="si">{</span><span class="n">thresh</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">'</span><span class="p">);</span>
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<div class="prompt input_prompt">In [47]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">thresh</span> <span class="o">=</span> <span class="mf">0.6</span>
<span class="n">figsize</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">12</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">yv</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">bcounts</span><span class="p">):</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">xv</span> <span class="o">=</span> <span class="mi">4096</span>
<span class="n">delt</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">-</span><span class="n">results</span><span class="p">[</span><span class="n">xv</span><span class="p">][</span><span class="n">thresh</span><span class="p">][</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">yv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])]</span>
<span class="n">tresx</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="p">[</span><span class="n">xv</span><span class="p">][</span><span class="n">thresh</span><span class="p">])</span> <span class="k">if</span> <span class="n">delt</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">>=</span><span class="mi">0</span><span class="p">]</span>
<span class="n">tresy</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">delt</span><span class="p">)</span> <span class="k">if</span> <span class="n">x</span><span class="o">>=</span><span class="mi">0</span><span class="p">]</span>
<span class="n">mv</span> <span class="o">=</span> <span class="nb">max</span><span class="p">(</span><span class="n">tresx</span><span class="p">)</span>
<span class="n">hexbin</span><span class="p">(</span><span class="n">tresx</span><span class="p">,</span><span class="n">tresy</span><span class="p">,</span><span class="n">bins</span><span class="o">=</span><span class="s1">'log'</span><span class="p">,</span><span class="n">cmap</span><span class="o">=</span><span class="s1">'Blues'</span><span class="p">)</span>
<span class="n">ylabel</span><span class="p">(</span><span class="sa">f</span><span class="s1">'extra hits'</span><span class="p">);</span>
<span class="n">title</span><span class="p">(</span><span class="sa">f</span><span class="s1">'extra hits with </span><span class="si">{</span><span class="n">yv</span><span class="si">}</span><span class="s1"> bits, thresh=</span><span class="si">{</span><span class="n">thresh</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">'</span><span class="p">);</span>
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<p>This makes it even more obvious that the 64bit fingerprints are just too short. 128 bits seems to be a good balance.</p>
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<h1 id="Performance-impact-of-FP-size">Performance impact of FP size<a class="anchor-link" href="#Performance-impact-of-FP-size">¶</a></h1>
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<h2 id="In-PostgreSQL">In PostgreSQL<a class="anchor-link" href="#In-PostgreSQL">¶</a></h2><p>Here we're using slightly different datasets: the database is the full ChEMBL26 (instead of the first million molecules in ChEMBL27) and the queries are 1000 molecules randomly selected from ChEMBL26.</p>
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<p>Start by setting up the tables with the different fingerprint sizes:</p>
<pre><code>chembl_26=# set rdkit.morgan_fp_size=2048;
SET
Time: 0.335 ms
chembl_26=# select molregno,morganbv_fp(m) as mfp2 into rdk.fps2048 from rdk.mols;
SELECT 1940732
Time: 115226.927 ms (01:55.227)
chembl_26=# set rdkit.morgan_fp_size=128;
SET
Time: 0.140 ms
chembl_26=# select molregno,morganbv_fp(m) as mfp2 into rdk.fps128 from rdk.mols;
SELECT 1940732
Time: 105524.382 ms (01:45.524)
chembl_26=# \dt+ rdk.fps2048;
List of relations
Schema | Name | Type | Owner | Size | Description
--------+---------+-------+----------+--------+-------------
rdk | fps2048 | table | glandrum | 562 MB |
(1 row)
chembl_26=# \dt+ rdk.fps128;
List of relations
Schema | Name | Type | Owner | Size | Description
--------+--------+-------+----------+--------+-------------
rdk | fps128 | table | glandrum | 112 MB |
(1 row)
chembl_26=# create index fps128_mfp2_idx on rdk.fps128 using gist(mfp2);
CREATE INDEX
Time: 25325.528 ms (00:25.326)
chembl_26=# set rdkit.morgan_fp_size=2048;
SET
Time: 0.154 ms
chembl_26=# create index fps2048_mfp2_idx on rdk.fps2048 using gist(mfp2);
CREATE INDEX
Time: 195260.817 ms (03:15.261)</code></pre>
<p>Now create the tables with 1000 randomly chosen queries:</p>
<pre><code>chembl_26=# select * into table rdk.tmpqs2048 from rdk.fps2048 tablesample system(0.1) limit 1000;
SELECT 1000
Time: 14.196 ms
chembl_26=# select fps.* into table rdk.tmpqs128 from rdk.fps128 fps join rdk.tmpqs2048 using (molregno);
SELECT 1000
Time: 134.327 ms</code></pre>
<p>Run the 2048 bit and 128 bit queries:</p>
<pre><code>chembl_26=# create or replace function get_count2048(qfp2 bfp) returns table(cnt bigint) as
$$
select count(*) from rdk.fps2048 tbl where tbl.mfp2%qfp2;
$$ language sql stable;
CREATE FUNCTION
Time: 67.197 ms
chembl_26=# set rdkit.tanimoto_threshold=0.9;
SET
Time: 0.132 ms
chembl_26=# select min(cnt),max(cnt),avg(cnt) from (select get_count2048(mfp2) cnt from rdk.tmpqs2048) tmp;
min | max | avg
-----+-----+--------------------
1 | 49 | 1.5690000000000000
(1 row)
Time: 33572.026 ms (00:33.572)
chembl_26=# set rdkit.tanimoto_threshold=0.7;
SET
Time: 0.419 ms
chembl_26=# select min(cnt),max(cnt),avg(cnt) from (select get_count2048(mfp2) cnt from rdk.tmpqs2048) tmp;
min | max | avg
-----+-----+--------------------
1 | 737 | 9.3190000000000000
(1 row)
Time: 99049.256 ms (01:39.049)
chembl_26=# set rdkit.tanimoto_threshold=0.6;
SET
Time: 0.393 ms
chembl_26=# select min(cnt),max(cnt),avg(cnt) from (select get_count2048(mfp2) cnt from rdk.tmpqs2048) tmp;
min | max | avg
-----+------+---------------------
1 | 1274 | 28.0480000000000000
(1 row)
Time: 182964.348 ms (03:02.964)
chembl_26=# create or replace function get_count128(qfp2 bfp) returns table(cnt bigint) as
$$
select count(*) from rdk.fps128 tbl where tbl.mfp2%qfp2;
$$ language sql stable;
CREATE FUNCTION
Time: 67.593 ms
chembl_26=# set rdkit.tanimoto_threshold=0.9;
SET
Time: 0.389 ms
chembl_26=# select min(cnt),max(cnt),avg(cnt) from (select get_count128(mfp2) cnt from rdk.tmpqs128) tmp;
min | max | avg
-----+-----+--------------------
1 | 255 | 2.1610000000000000
(1 row)
Time: 44760.287 ms (00:44.760)
chembl_26=# set rdkit.tanimoto_threshold=0.7;
SET
Time: 0.145 ms
chembl_26=# select min(cnt),max(cnt),avg(cnt) from (select get_count128(mfp2) cnt from rdk.tmpqs128) tmp;
min | max | avg
-----+------+---------------------
1 | 2046 | 31.8120000000000000
(1 row)
Time: 107402.701 ms (01:47.403)
chembl_26=# set rdkit.tanimoto_threshold=0.6;
SET
Time: 0.112 ms
chembl_26=# select min(cnt),max(cnt),avg(cnt) from (select get_count128(mfp2) cnt from rdk.tmpqs128) tmp;
min | max | avg
-----+------+----------------------
1 | 8956 | 170.5990000000000000
(1 row)
Time: 126819.459 ms (02:06.819)</code></pre>
<p>Finally check what happens if we turn off the index and just do a sequential scan:</p>
<pre><code>chembl_26=# set enable_bitmapscan=off;
SET
Time: 0.353 ms
chembl_26=# set enable_indexscan=off;
SET
Time: 0.351 ms
chembl_26=# select min(cnt),max(cnt),avg(cnt) from (select get_count128(mfp2) cnt from rdk.tmpqs128) tmp;
min | max | avg
-----+-------+---------------------
1 | 12201 | 84.1850000000000000
(1 row)
Time: 1120514.600 ms (18:40.515)</code></pre>
<p>Ouch, don't want to do that!</p>
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<h3 id="Summarize-that">Summarize that<a class="anchor-link" href="#Summarize-that">¶</a></h3><p>Here's a summary of the runtime (in seconds) to run the 1000 queries for different thresholds and number of bits:</p>
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<thead><tr>
<th>Threshold</th>
<th>2048 bits</th>
<th>128 bits</th>
</tr>
</thead>
<tbody>
<tr>
<td>0.9</td>
<td>33.6</td>
<td>44.8</td>
</tr>
<tr>
<td>0.7</td>
<td>99.0</td>
<td>107.4</td>
</tr>
<tr>
<td>0.6</td>
<td>183.0</td>
<td>126.8</td>
</tr>
</tbody>
</table>
<p>What's interesting here is that the 128 bit fingerprint only becomes faster than the 2048 bit fingerprint when the similarity threshold is low. I haven't dug into this (yet?), but I guess the less computationally intensive searches with the shorter fingerprints are being outweighed by less-accurate index results,</p>
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<h2 id="Using-FPSim2">Using FPSim2<a class="anchor-link" href="#Using-FPSim2">¶</a></h2><p><a href="https://github.com/chembl/FPSim2">FPSim2</a> is an interesting project from Eloy and the ChEMBL team to allow fast similarity searching with either a CPU or a GPU. I haven't really tried it out before, so I figured I would do it as part of this post.</p>
<p>After installing FPSim2 I created db files for both 2048 and 128 bit Morgan2 fingerprints for the ChEMBL27 compounds by following the instructions in the <a href="https://chembl.github.io/FPSim2/source/user_guide/create_fp_db.html">FPSim2 docs</a>.</p>
<p>Now let's do some searches:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">time</span>
<span class="kn">from</span> <span class="nn">FPSim2</span> <span class="kn">import</span> <span class="n">FPSim2Engine</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">queries</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">split</span><span class="p">()</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'../data/chembl26_1000simqueries.txt'</span><span class="p">)</span><span class="o">.</span><span class="n">readlines</span><span class="p">()]</span>
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<h3 id="On-disk-queries">On-disk queries<a class="anchor-link" href="#On-disk-queries">¶</a></h3><p>Start by doing on-disk queries. We wouldn't normally do this unless the dataset was huge, but it's worth establishing a baseline:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.6</span><span class="p">):</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">fpe</span> <span class="o">=</span> <span class="n">FPSim2Engine</span><span class="p">(</span><span class="s1">'../data/chembl27_mfp2.2048.h5'</span><span class="p">,</span><span class="n">in_memory_fps</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">for</span> <span class="n">mrn</span><span class="p">,</span><span class="n">smi</span> <span class="ow">in</span> <span class="n">queries</span><span class="p">:</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">fpe</span><span class="o">.</span><span class="n">on_disk_similarity</span><span class="p">(</span><span class="n">smi</span><span class="p">,</span><span class="n">thresh</span><span class="p">,</span><span class="n">n_workers</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">cnts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">))</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"threshold=</span><span class="si">{</span><span class="n">thresh</span><span class="si">}</span><span class="s2"> time=</span><span class="si">{</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> min=</span><span class="si">{</span><span class="nb">min</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2"> max=</span><span class="si">{</span><span class="nb">max</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2"> avg=</span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>threshold=0.9 time=85.29 min=1 max=49 avg=1.57
threshold=0.8 time=163.08 min=1 max=368 avg=3.336
threshold=0.7 time=231.87 min=1 max=737 avg=9.332
threshold=0.6 time=284.70 min=1 max=1274 avg=28.08
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.6</span><span class="p">):</span>
<span class="n">cnts128</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">fpe</span> <span class="o">=</span> <span class="n">FPSim2Engine</span><span class="p">(</span><span class="s1">'../data/chembl27_mfp2.128.h5'</span><span class="p">,</span><span class="n">in_memory_fps</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="k">for</span> <span class="n">mrn</span><span class="p">,</span><span class="n">smi</span> <span class="ow">in</span> <span class="n">queries</span><span class="p">:</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">fpe</span><span class="o">.</span><span class="n">on_disk_similarity</span><span class="p">(</span><span class="n">smi</span><span class="p">,</span><span class="n">thresh</span><span class="p">,</span><span class="n">n_workers</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="n">cnts128</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">))</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"threshold=</span><span class="si">{</span><span class="n">thresh</span><span class="si">}</span><span class="s2"> time=</span><span class="si">{</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> min=</span><span class="si">{</span><span class="nb">min</span><span class="p">(</span><span class="n">cnts128</span><span class="p">)</span><span class="si">}</span><span class="s2"> max=</span><span class="si">{</span><span class="nb">max</span><span class="p">(</span><span class="n">cnts128</span><span class="p">)</span><span class="si">}</span><span class="s2"> avg=</span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">cnts128</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>threshold=0.9 time=8.83 min=1 max=255 avg=2.162
threshold=0.8 time=15.07 min=1 max=931 avg=8.353
threshold=0.7 time=19.90 min=1 max=2046 avg=31.849
threshold=0.6 time=23.61 min=1 max=8963 avg=170.709
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<p>The 128 bit times here aren't that terrible, but the 2048 bit queries are slow (slower than PostgreSQL is, for example).</p>
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<h3 id="In-memory-queries">In-memory queries<a class="anchor-link" href="#In-memory-queries">¶</a></h3><p>This time load the fingerprints into memory and then search them. I imagine that this is a more normal mode for working with FPSim2. The results are, of course, be significantly faster:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.6</span><span class="p">):</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">fpe</span> <span class="o">=</span> <span class="n">FPSim2Engine</span><span class="p">(</span><span class="s1">'../data/chembl27_mfp2.2048.h5'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">mrn</span><span class="p">,</span><span class="n">smi</span> <span class="ow">in</span> <span class="n">queries</span><span class="p">:</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">fpe</span><span class="o">.</span><span class="n">similarity</span><span class="p">(</span><span class="n">smi</span><span class="p">,</span><span class="n">thresh</span><span class="p">)</span>
<span class="n">cnts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">))</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"threshold=</span><span class="si">{</span><span class="n">thresh</span><span class="si">}</span><span class="s2"> time=</span><span class="si">{</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> min=</span><span class="si">{</span><span class="nb">min</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2"> max=</span><span class="si">{</span><span class="nb">max</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2"> avg=</span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>threshold=0.9 time=12.12 min=1 max=49 avg=1.57
threshold=0.8 time=22.32 min=1 max=368 avg=3.336
threshold=0.7 time=31.22 min=1 max=737 avg=9.332
threshold=0.6 time=38.45 min=1 max=1274 avg=28.08
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.6</span><span class="p">):</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">fpe</span> <span class="o">=</span> <span class="n">FPSim2Engine</span><span class="p">(</span><span class="s1">'../data/chembl27_mfp2.128.h5'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">mrn</span><span class="p">,</span><span class="n">smi</span> <span class="ow">in</span> <span class="n">queries</span><span class="p">:</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">fpe</span><span class="o">.</span><span class="n">similarity</span><span class="p">(</span><span class="n">smi</span><span class="p">,</span><span class="n">thresh</span><span class="p">)</span>
<span class="n">cnts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">))</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"threshold=</span><span class="si">{</span><span class="n">thresh</span><span class="si">}</span><span class="s2"> time=</span><span class="si">{</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> min=</span><span class="si">{</span><span class="nb">min</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2"> max=</span><span class="si">{</span><span class="nb">max</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2"> avg=</span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>threshold=0.9 time=2.51 min=1 max=255 avg=2.162
threshold=0.8 time=4.21 min=1 max=931 avg=8.353
threshold=0.7 time=5.68 min=1 max=2046 avg=31.849
threshold=0.6 time=7.02 min=1 max=8963 avg=170.709
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<p>The 128 bit fingerprints are still quite a bit faster than then 2048 bit fingerprints. We're really seeing the benefit of not having to do as much work when calculating similarities here.</p>
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<h3 id="Multi-threaded-queries">Multi-threaded queries<a class="anchor-link" href="#Multi-threaded-queries">¶</a></h3><p>FPSim2 lets us do multi-threaded queries when working with fingerprints in-memory. Try that out:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.6</span><span class="p">):</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">fpe</span> <span class="o">=</span> <span class="n">FPSim2Engine</span><span class="p">(</span><span class="s1">'../data/chembl27_mfp2.2048.h5'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">mrn</span><span class="p">,</span><span class="n">smi</span> <span class="ow">in</span> <span class="n">queries</span><span class="p">:</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">fpe</span><span class="o">.</span><span class="n">similarity</span><span class="p">(</span><span class="n">smi</span><span class="p">,</span><span class="n">thresh</span><span class="p">,</span> <span class="n">n_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">cnts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">))</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"threshold=</span><span class="si">{</span><span class="n">thresh</span><span class="si">}</span><span class="s2"> time=</span><span class="si">{</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> min=</span><span class="si">{</span><span class="nb">min</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2"> max=</span><span class="si">{</span><span class="nb">max</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2"> avg=</span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>threshold=0.9 time=6.50 min=1 max=49 avg=1.57
threshold=0.8 time=10.64 min=1 max=368 avg=3.336
threshold=0.7 time=14.18 min=1 max=737 avg=9.332
threshold=0.6 time=17.17 min=1 max=1274 avg=28.08
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.6</span><span class="p">):</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">fpe</span> <span class="o">=</span> <span class="n">FPSim2Engine</span><span class="p">(</span><span class="s1">'../data/chembl27_mfp2.2048.h5'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">mrn</span><span class="p">,</span><span class="n">smi</span> <span class="ow">in</span> <span class="n">queries</span><span class="p">:</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">fpe</span><span class="o">.</span><span class="n">similarity</span><span class="p">(</span><span class="n">smi</span><span class="p">,</span><span class="n">thresh</span><span class="p">,</span> <span class="n">n_workers</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">cnts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">))</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"threshold=</span><span class="si">{</span><span class="n">thresh</span><span class="si">}</span><span class="s2"> time=</span><span class="si">{</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> min=</span><span class="si">{</span><span class="nb">min</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2"> max=</span><span class="si">{</span><span class="nb">max</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2"> avg=</span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>threshold=0.9 time=8.15 min=1 max=49 avg=1.57
threshold=0.8 time=14.03 min=1 max=368 avg=3.336
threshold=0.7 time=18.73 min=1 max=737 avg=9.332
threshold=0.6 time=22.91 min=1 max=1274 avg=28.08
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.6</span><span class="p">):</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">fpe</span> <span class="o">=</span> <span class="n">FPSim2Engine</span><span class="p">(</span><span class="s1">'../data/chembl27_mfp2.128.h5'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">mrn</span><span class="p">,</span><span class="n">smi</span> <span class="ow">in</span> <span class="n">queries</span><span class="p">:</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">fpe</span><span class="o">.</span><span class="n">similarity</span><span class="p">(</span><span class="n">smi</span><span class="p">,</span><span class="n">thresh</span><span class="p">,</span><span class="n">n_workers</span><span class="o">=</span><span class="mi">4</span><span class="p">)</span>
<span class="n">cnts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">))</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"threshold=</span><span class="si">{</span><span class="n">thresh</span><span class="si">}</span><span class="s2"> time=</span><span class="si">{</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> min=</span><span class="si">{</span><span class="nb">min</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2"> max=</span><span class="si">{</span><span class="nb">max</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2"> avg=</span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>threshold=0.9 time=2.08 min=1 max=255 avg=2.162
threshold=0.8 time=2.54 min=1 max=931 avg=8.353
threshold=0.7 time=3.10 min=1 max=2046 avg=31.849
threshold=0.6 time=3.40 min=1 max=8963 avg=170.709
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">thresh</span> <span class="ow">in</span> <span class="p">(</span><span class="mf">0.9</span><span class="p">,</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.6</span><span class="p">):</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">fpe</span> <span class="o">=</span> <span class="n">FPSim2Engine</span><span class="p">(</span><span class="s1">'../data/chembl27_mfp2.128.h5'</span><span class="p">)</span>
<span class="k">for</span> <span class="n">mrn</span><span class="p">,</span><span class="n">smi</span> <span class="ow">in</span> <span class="n">queries</span><span class="p">:</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">fpe</span><span class="o">.</span><span class="n">similarity</span><span class="p">(</span><span class="n">smi</span><span class="p">,</span><span class="n">thresh</span><span class="p">,</span><span class="n">n_workers</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
<span class="n">cnts</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">))</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"threshold=</span><span class="si">{</span><span class="n">thresh</span><span class="si">}</span><span class="s2"> time=</span><span class="si">{</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span><span class="si">:</span><span class="s2">.2f</span><span class="si">}</span><span class="s2"> min=</span><span class="si">{</span><span class="nb">min</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2"> max=</span><span class="si">{</span><span class="nb">max</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2"> avg=</span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">average</span><span class="p">(</span><span class="n">cnts</span><span class="p">)</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
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<pre>threshold=0.9 time=2.18 min=1 max=255 avg=2.162
threshold=0.8 time=4.01 min=1 max=931 avg=8.353
threshold=0.7 time=4.54 min=1 max=2046 avg=31.849
threshold=0.6 time=4.90 min=1 max=8963 avg=170.709
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<p>Summarize the in-memory results</p>
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<thead><tr>
<th>Threshold</th>
<th>Num threads</th>
<th>2048 bits</th>
<th>128 bits</th>
</tr>
</thead>
<tbody>
<tr>
<td>0.9</td>
<td>1</td>
<td>12.1</td>
<td>2.5</td>
</tr>
<tr>
<td>0.9</td>
<td>2</td>
<td>8.2</td>
<td>2.2</td>
</tr>
<tr>
<td>0.7</td>
<td>1</td>
<td>31.2</td>
<td>5.7</td>
</tr>
<tr>
<td>0.7</td>
<td>2</td>
<td>18.7</td>
<td>4.5</td>
</tr>
<tr>
<td>0.6</td>
<td>1</td>
<td>38.4</td>
<td>7.0</td>
</tr>
<tr>
<td>0.6</td>
<td>2</td>
<td>22.9</td>
<td>4.9</td>
</tr>
</tbody>
</table>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com2tag:blogger.com,1999:blog-684443317148892945.post-16325399373430353432020-07-10T05:00:00.000-07:002020-07-10T05:00:17.464-07:00Adding molecular metadata to PNGs<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>I really like the idea of having an image of a molecule that I can re-construct the molecule from. It's been possible to do this with SVGs from the RDKit for a while by adding chemical metadata. There's a desciption of that <a href="https://github.com/rdkit/UGM_2018/blob/master/Notebooks/Landrum_Whats_New.ipynb">here</a> that shows both adding the metadata and constructing the molecule from it.</p>
<p>An aside: Since you can now add SVGs to powerpoint documents, I was super excited about the idea of being able to have molecule images in my Powerpoint decks together with the metadata to reconstruct those molecules. Unforunately, as of this writing at least, Powerpoint strips out all the metadata that it doesn't recognize, so the chemistry information all ends up being removed. Sad...</p>
<p>Anyway, back to the point of this blog post: I wanted to do the same thing with the PNGs that the RDKit can generate, but kept getting stuck on how to do this from the C++ side. Today I realized that it's quite easy to do it in Python and that this would possibly be useful to most RDKit users. Thus this post.</p>
<p>If anyone knows of a decent C++ snippet or library with an BSD-compatible license (this rules out <a href="https://www.exiv2.org/">exiv2</a>, which otherwise looks like the perfect thing) that shows how to add metadata to a PNG, please do let me know.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">Chem</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">Draw</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">IPythonConsole</span>
<span class="n">IPythonConsole</span><span class="o">.</span><span class="n">molSize</span> <span class="o">=</span> <span class="mi">350</span><span class="p">,</span><span class="mi">300</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="kn">import</span> <span class="n">Image</span>
<span class="kn">from</span> <span class="nn">io</span> <span class="kn">import</span> <span class="n">BytesIO</span>
<span class="kn">import</span> <span class="nn">rdkit</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rdkit</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
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<pre>2020.03.4
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">colchicine</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'COc1cc2c(c(OC)c1OC)-c1ccc(OC)c(=O)cc1[C@@H](NC(C)=O)CC2'</span><span class="p">)</span>
<span class="n">colchicine</span>
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<img 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>
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<p>A quick reminder on what the metadata in the SVG looks like:</p>
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<div class="input">
<div class="prompt input_prompt">In [3]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">dm</span> <span class="o">=</span> <span class="n">Draw</span><span class="o">.</span><span class="n">PrepareMolForDrawing</span><span class="p">(</span><span class="n">colchicine</span><span class="p">)</span>
<span class="n">d2d</span> <span class="o">=</span> <span class="n">Draw</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">450</span><span class="p">,</span><span class="mi">400</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecule</span><span class="p">(</span><span class="n">dm</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">AddMoleculeMetadata</span><span class="p">(</span><span class="n">dm</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">svg</span> <span class="o">=</span> <span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">()</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s1">'/tmp/blah.svg'</span><span class="p">,</span><span class="s1">'w+'</span><span class="p">)</span> <span class="k">as</span> <span class="n">outf</span><span class="p">:</span>
<span class="n">outf</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">svg</span><span class="p">)</span>
</pre></div>
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<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [4]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>grep rdkit /tmp/blah.svg
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<pre> xmlns:rdkit='http://www.rdkit.org/xml'
<rdkit:mol xmlns:rdkit = "http://www.rdkit.org/xml" version="0.9">
<rdkit:atom idx="1" atom-smiles="[CH3]" drawing-x="429.545" drawing-y="166.585" x="6.46024" y="1.03002" z="0" />
<rdkit:atom idx="2" atom-smiles="[O]" drawing-x="390.087" drawing-y="133.821" x="5.30621" y="1.98825" z="0" />
<rdkit:atom idx="3" atom-smiles="[c]" drawing-x="341.983" drawing-y="151.611" x="3.89934" y="1.46795" z="0" />
<rdkit:atom idx="4" atom-smiles="[cH]" drawing-x="302.525" drawing-y="118.847" x="2.74531" y="2.42618" z="0" />
<rdkit:atom idx="5" atom-smiles="[c]" drawing-x="254.421" drawing-y="136.637" x="1.33844" y="1.90588" z="0" />
<rdkit:atom idx="6" atom-smiles="[c]" drawing-x="245.776" drawing-y="187.191" x="1.0856" y="0.427343" z="0" />
<rdkit:atom idx="7" atom-smiles="[c]" drawing-x="285.235" drawing-y="219.955" x="2.23963" y="-0.530891" z="0" />
<rdkit:atom idx="8" atom-smiles="[O]" drawing-x="276.59" drawing-y="270.509" x="1.98679" y="-2.00943" z="0" />
<rdkit:atom idx="9" atom-smiles="[CH3]" drawing-x="316.048" drawing-y="303.273" x="3.14082" y="-2.96766" z="0" />
<rdkit:atom idx="10" atom-smiles="[c]" drawing-x="333.338" drawing-y="202.165" x="3.6465" y="-0.0105878" z="0" />
<rdkit:atom idx="11" atom-smiles="[O]" drawing-x="372.797" drawing-y="234.929" x="4.80053" y="-0.968822" z="0" />
<rdkit:atom idx="12" atom-smiles="[CH3]" drawing-x="364.152" drawing-y="285.483" x="4.54769" y="-2.44736" z="0" />
<rdkit:atom idx="13" atom-smiles="[c]" drawing-x="200.861" drawing-y="211.952" x="-0.228013" y="-0.296833" z="0" />
<rdkit:atom idx="14" atom-smiles="[cH]" drawing-x="215.007" drawing-y="261.251" x="0.1857" y="-1.73865" z="0" />
<rdkit:atom idx="15" atom-smiles="[cH]" drawing-x="185.283" drawing-y="303.047" x="-0.683614" y="-2.96106" z="0" />
<rdkit:atom idx="16" atom-smiles="[c]" drawing-x="134.073" drawing-y="305.868" x="-2.18134" y="-3.04357" z="0" />
<rdkit:atom idx="17" atom-smiles="[O]" drawing-x="114.396" drawing-y="353.231" x="-2.75685" y="-4.42878" z="0" />
<rdkit:atom idx="18" atom-smiles="[CH3]" drawing-x="63.5393" drawing-y="359.871" x="-4.24422" y="-4.62298" z="0" />
<rdkit:atom idx="19" atom-smiles="[c]" drawing-x="99.9385" drawing-y="267.59" x="-3.17967" y="-1.92404" z="0" />
<rdkit:atom idx="20" atom-smiles="[O]" drawing-x="50.64" drawing-y="281.735" x="-4.62149" y="-2.33775" z="0" />
<rdkit:atom idx="21" atom-smiles="[cH]" drawing-x="108.584" drawing-y="217.035" x="-2.92683" y="-0.445502" z="0" />
<rdkit:atom idx="22" atom-smiles="[c]" drawing-x="153.498" drawing-y="192.275" x="-1.61322" y="0.278673" z="0" />
<rdkit:atom idx="23" atom-smiles="[C@@H]" drawing-x="139.353" drawing-y="142.976" x="-2.02693" y="1.72049" z="0" />
<rdkit:atom idx="24" atom-smiles="[NH]" drawing-x="88.7988" drawing-y="134.331" x="-3.50547" y="1.97333" z="0" />
<rdkit:atom idx="25" atom-smiles="[C]" drawing-x="71.0086" drawing-y="86.2273" x="-4.02577" y="3.3802" z="0" />
<rdkit:atom idx="26" atom-smiles="[CH3]" drawing-x="20.4545" drawing-y="77.5822" x="-5.50431" y="3.63304" z="0" />
<rdkit:atom idx="27" atom-smiles="[O]" drawing-x="103.772" drawing-y="46.7688" x="-3.06754" y="4.53423" z="0" />
<rdkit:atom idx="28" atom-smiles="[CH2]" drawing-x="169.076" drawing-y="101.179" x="-1.15762" y="2.9429" z="0" />
<rdkit:atom idx="29" atom-smiles="[CH2]" drawing-x="220.287" drawing-y="98.3583" x="0.340111" y="3.02541" z="0" />
<rdkit:bond idx="1" begin-atom-idx="1" end-atom-idx="2" bond-smiles="-" />
<rdkit:bond idx="2" begin-atom-idx="2" end-atom-idx="3" bond-smiles="-" />
<rdkit:bond idx="3" begin-atom-idx="3" end-atom-idx="4" bond-smiles="=" />
<rdkit:bond idx="4" begin-atom-idx="4" end-atom-idx="5" bond-smiles="-" />
<rdkit:bond idx="5" begin-atom-idx="5" end-atom-idx="6" bond-smiles="=" />
<rdkit:bond idx="6" begin-atom-idx="6" end-atom-idx="7" bond-smiles="-" />
<rdkit:bond idx="7" begin-atom-idx="7" end-atom-idx="8" bond-smiles="-" />
<rdkit:bond idx="8" begin-atom-idx="8" end-atom-idx="9" bond-smiles="-" />
<rdkit:bond idx="9" begin-atom-idx="7" end-atom-idx="10" bond-smiles="=" />
<rdkit:bond idx="10" begin-atom-idx="10" end-atom-idx="11" bond-smiles="-" />
<rdkit:bond idx="11" begin-atom-idx="11" end-atom-idx="12" bond-smiles="-" />
<rdkit:bond idx="12" begin-atom-idx="6" end-atom-idx="13" bond-smiles="-" />
<rdkit:bond idx="13" begin-atom-idx="13" end-atom-idx="14" bond-smiles="=" />
<rdkit:bond idx="14" begin-atom-idx="14" end-atom-idx="15" bond-smiles="-" />
<rdkit:bond idx="15" begin-atom-idx="15" end-atom-idx="16" bond-smiles="=" />
<rdkit:bond idx="16" begin-atom-idx="16" end-atom-idx="17" bond-smiles="-" />
<rdkit:bond idx="17" begin-atom-idx="17" end-atom-idx="18" bond-smiles="-" />
<rdkit:bond idx="18" begin-atom-idx="16" end-atom-idx="19" bond-smiles="-" />
<rdkit:bond idx="19" begin-atom-idx="19" end-atom-idx="20" bond-smiles="=" />
<rdkit:bond idx="20" begin-atom-idx="19" end-atom-idx="21" bond-smiles="-" />
<rdkit:bond idx="21" begin-atom-idx="21" end-atom-idx="22" bond-smiles="=" />
<rdkit:bond idx="22" begin-atom-idx="22" end-atom-idx="23" bond-smiles="-" />
<rdkit:bond idx="23" begin-atom-idx="23" end-atom-idx="24" bond-smiles="-" />
<rdkit:bond idx="24" begin-atom-idx="24" end-atom-idx="25" bond-smiles="-" />
<rdkit:bond idx="25" begin-atom-idx="25" end-atom-idx="26" bond-smiles="-" />
<rdkit:bond idx="26" begin-atom-idx="25" end-atom-idx="27" bond-smiles="=" />
<rdkit:bond idx="27" begin-atom-idx="23" end-atom-idx="28" bond-smiles="-" />
<rdkit:bond idx="28" begin-atom-idx="28" end-atom-idx="29" bond-smiles="-" />
<rdkit:bond idx="29" begin-atom-idx="10" end-atom-idx="3" bond-smiles="-" />
<rdkit:bond idx="30" begin-atom-idx="22" end-atom-idx="13" bond-smiles="-" />
<rdkit:bond idx="31" begin-atom-idx="29" end-atom-idx="5" bond-smiles="-" />
</rdkit:mol></metadata>
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<p>Ok, let's move onto PNGs.
Start by getting a PIL Image object with the molecule drawing. There's a convenience function for this:</p>
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<div class="prompt input_prompt">In [5]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">img</span> <span class="o">=</span> <span class="n">Draw</span><span class="o">.</span><span class="n">MolToImage</span><span class="p">(</span><span class="n">dm</span><span class="p">,</span><span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">450</span><span class="p">,</span><span class="mi">400</span><span class="p">))</span>
<span class="n">img</span>
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<img src="data:image/png;base64,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<p>Now use the PngImagePlugin which is part of pillow to add CXSMILES as the metadata and write the PNG out to a file:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">PIL.PngImagePlugin</span> <span class="kn">import</span> <span class="n">PngInfo</span>
<span class="n">metadata</span> <span class="o">=</span> <span class="n">PngInfo</span><span class="p">()</span>
<span class="n">metadata</span><span class="o">.</span><span class="n">add_text</span><span class="p">(</span><span class="s2">"RDKit_SMILES"</span><span class="p">,</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolToCXSmiles</span><span class="p">(</span><span class="n">dm</span><span class="p">))</span>
<span class="n">img</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s2">"/tmp/blah.png"</span><span class="p">,</span><span class="nb">format</span><span class="o">=</span><span class="s2">"PNG"</span><span class="p">,</span><span class="n">pnginfo</span><span class="o">=</span><span class="n">metadata</span><span class="p">)</span>
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<p>Confirm that we can access the metadata when we read the file back in:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">nimg</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">'/tmp/blah.png'</span><span class="p">)</span>
<span class="n">nimg</span><span class="o">.</span><span class="n">text</span>
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<pre>{'RDKit_SMILES': 'COc1cc2c(-c3ccc(OC)c(=O)cc3[C@@H](NC(C)=O)CC2)c(OC)c1OC |(6.46024,1.03002,;5.30621,1.98825,;3.89934,1.46795,;2.74531,2.42618,;1.33844,1.90588,;1.0856,0.427343,;-0.228013,-0.296833,;0.1857,-1.73865,;-0.683614,-2.96106,;-2.18134,-3.04357,;-2.75685,-4.42878,;-4.24422,-4.62298,;-3.17967,-1.92404,;-4.62149,-2.33775,;-2.92683,-0.445502,;-1.61322,0.278673,;-2.02693,1.72049,;-3.50547,1.97333,;-4.02577,3.3802,;-5.50431,3.63304,;-3.06754,4.53423,;-1.15762,2.9429,;0.340111,3.02541,;2.23963,-0.530891,;1.98679,-2.00943,;3.14082,-2.96766,;3.6465,-0.0105878,;4.80053,-0.968822,;4.54769,-2.44736,)|'}</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tmol</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="n">nimg</span><span class="o">.</span><span class="n">text</span><span class="p">[</span><span class="s1">'RDKit_SMILES'</span><span class="p">])</span>
<span class="n">tmol</span>
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<img 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<p>And demonstrate that this isn't just something the PIL tools can read... show the metadata using imagemagick:</p>
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<div class="prompt input_prompt">In [9]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>identify -verbose /tmp/blah.png <span class="p">|</span> grep RDK
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<pre> RDKit_SMILES: COc1cc2c(-c3ccc(OC)c(=O)cc3[C@@H](NC(C)=O)CC2)c(OC)c1OC |(6.46024,1.03002,;5.30621,1.98825,;3.89934,1.46795,;2.74531,2.42618,;1.33844,1.90588,;1.0856,0.427343,;-0.228013,-0.296833,;0.1857,-1.73865,;-0.683614,-2.96106,;-2.18134,-3.04357,;-2.75685,-4.42878,;-4.24422,-4.62298,;-3.17967,-1.92404,;-4.62149,-2.33775,;-2.92683,-0.445502,;-1.61322,0.278673,;-2.02693,1.72049,;-3.50547,1.97333,;-4.02577,3.3802,;-5.50431,3.63304,;-3.06754,4.53423,;-1.15762,2.9429,;0.340111,3.02541,;2.23963,-0.530891,;1.98679,-2.00943,;3.14082,-2.96766,;3.6465,-0.0105878,;4.80053,-0.968822,;4.54769,-2.44736,)|
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<p>Since people inevitably want to put structures into Powerpoint, make sure that we can safely add these and not lose the metadata. I will use the <a href="https://python-pptx.readthedocs.io/en/latest/index.html">python-pptx library</a> to demo this.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">pptx</span> <span class="kn">import</span> <span class="n">Presentation</span>
<span class="kn">from</span> <span class="nn">pptx.util</span> <span class="kn">import</span> <span class="n">Mm</span>
<span class="n">bio</span> <span class="o">=</span> <span class="n">BytesIO</span><span class="p">()</span>
<span class="n">img</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">bio</span><span class="p">,</span><span class="nb">format</span><span class="o">=</span><span class="s2">"PNG"</span><span class="p">,</span><span class="n">pnginfo</span><span class="o">=</span><span class="n">metadata</span><span class="p">)</span>
<span class="n">prs</span> <span class="o">=</span> <span class="n">Presentation</span><span class="p">()</span>
<span class="n">blank_slide_layout</span> <span class="o">=</span> <span class="n">prs</span><span class="o">.</span><span class="n">slide_layouts</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span>
<span class="n">slide</span> <span class="o">=</span> <span class="n">prs</span><span class="o">.</span><span class="n">slides</span><span class="o">.</span><span class="n">add_slide</span><span class="p">(</span><span class="n">blank_slide_layout</span><span class="p">)</span>
<span class="n">left</span> <span class="o">=</span> <span class="n">top</span> <span class="o">=</span> <span class="n">Mm</span><span class="p">(</span><span class="mi">50</span><span class="p">)</span>
<span class="n">pic</span> <span class="o">=</span> <span class="n">slide</span><span class="o">.</span><span class="n">shapes</span><span class="o">.</span><span class="n">add_picture</span><span class="p">(</span><span class="n">bio</span><span class="p">,</span> <span class="n">left</span><span class="p">,</span> <span class="n">top</span><span class="p">,</span> <span class="n">height</span><span class="o">=</span><span class="n">Mm</span><span class="p">(</span><span class="mi">60</span><span class="p">))</span>
<span class="n">prs</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="s1">'/tmp/test.pptx'</span><span class="p">)</span>
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<p>Make sure we don't lose the metadata:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">nprs</span> <span class="o">=</span> <span class="n">Presentation</span><span class="p">(</span><span class="s1">'/tmp/test.pptx'</span><span class="p">)</span>
<span class="n">slide</span> <span class="o">=</span> <span class="n">nprs</span><span class="o">.</span><span class="n">slides</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">pic</span> <span class="o">=</span> <span class="n">slide</span><span class="o">.</span><span class="n">shapes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">bio</span> <span class="o">=</span> <span class="n">BytesIO</span><span class="p">(</span><span class="n">pic</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">blob</span><span class="p">)</span>
<span class="n">nimg</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">bio</span><span class="p">)</span>
<span class="n">nimg</span><span class="o">.</span><span class="n">text</span>
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<pre>{'RDKit_SMILES': 'COc1cc2c(-c3ccc(OC)c(=O)cc3[C@@H](NC(C)=O)CC2)c(OC)c1OC |(6.46024,1.03002,;5.30621,1.98825,;3.89934,1.46795,;2.74531,2.42618,;1.33844,1.90588,;1.0856,0.427343,;-0.228013,-0.296833,;0.1857,-1.73865,;-0.683614,-2.96106,;-2.18134,-3.04357,;-2.75685,-4.42878,;-4.24422,-4.62298,;-3.17967,-1.92404,;-4.62149,-2.33775,;-2.92683,-0.445502,;-1.61322,0.278673,;-2.02693,1.72049,;-3.50547,1.97333,;-4.02577,3.3802,;-5.50431,3.63304,;-3.06754,4.53423,;-1.15762,2.9429,;0.340111,3.02541,;2.23963,-0.530891,;1.98679,-2.00943,;3.14082,-2.96766,;3.6465,-0.0105878,;4.80053,-0.968822,;4.54769,-2.44736,)|'}</pre>
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<p>And to be super sure, I opened the file in PPT in Office365, added some text, and then re-downloaded it</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">nprs</span> <span class="o">=</span> <span class="n">Presentation</span><span class="p">(</span><span class="s1">'/home/glandrum/Downloads/test.pptx'</span><span class="p">)</span>
<span class="n">slide</span> <span class="o">=</span> <span class="n">nprs</span><span class="o">.</span><span class="n">slides</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">pic</span> <span class="o">=</span> <span class="n">slide</span><span class="o">.</span><span class="n">shapes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="n">bio</span> <span class="o">=</span> <span class="n">BytesIO</span><span class="p">(</span><span class="n">pic</span><span class="o">.</span><span class="n">image</span><span class="o">.</span><span class="n">blob</span><span class="p">)</span>
<span class="n">nimg</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">bio</span><span class="p">)</span>
<span class="n">nimg</span><span class="o">.</span><span class="n">text</span>
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<pre>{'RDKit_SMILES': 'COc1cc2c(-c3ccc(OC)c(=O)cc3[C@@H](NC(C)=O)CC2)c(OC)c1OC |(-4.30669,0.156621,;-3.68129,-0.623579,;-2.68309,-0.470179,;-2.34369,0.441021,;-1.32889,0.576821,;-0.68409,-0.187979,;0.27171,-0.186179,;0.52551,-1.16158,;1.40091,-1.58258,;2.32971,-1.17158,;3.10331,-1.78918,;4.03411,-1.42338,;2.52231,-0.182779,;3.50711,0.0420207,;1.92331,0.619821,;0.93211,0.590821,;0.67991,1.56562,;1.46991,2.19622,;1.32211,3.18522,;0.39191,3.55222,;2.10511,3.80762,;-0.21529,2.02862,;-1.08509,1.57442,;-1.05729,-1.09018,;-0.42849,-1.88038,;-0.79549,-2.81058,;-2.06789,-1.25978,;-2.42669,-2.18238,;-3.41509,-2.33478,)|'}</pre>
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<p>Looks good!</p>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com0tag:blogger.com,1999:blog-684443317148892945.post-47121567957987309022020-06-05T00:54:00.000-07:002020-06-05T00:54:13.740-07:00Number of bits set compared between fingerprints<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<h1 id="Looking-at-the-number-of-bits-set-by-different-fingerprints">Looking at the number of bits set by different fingerprints<a class="anchor-link" href="#Looking-at-the-number-of-bits-set-by-different-fingerprints">¶</a></h1><p>I've done a number of posts looking at Morgan fingerprint statistics before, including:</p>
<ul>
<li><a href="https://rdkit.blogspot.ch/2014/02/colliding-bits.html">The number of collisions in Morgan fingerprints</a>. </li>
<li><a href="https://rdkit.blogspot.ch/2016/02/morgan-fingerprint-bit-statistics.html">Morgan fingerprint stats</a></li>
<li><a href="https://rdkit.blogspot.com/2016/02/colliding-bits-iii.html">Collisions in Morgan fingerprints revisited</a></li>
</ul>
<p>I have done similar analysis for other fingerprint types, but it looks like I didn't post that (at least I can't find it if I did). It's useful to do this because, as we'll see, the different fingerprint types have <em>very</em> different numbers of bits set for typical molecules.</p>
<p>Here's the summary of the mean and standard deviation of the number of bits set, from an analysis of 5 million molecules with less than 50 heavy atoms extracted from ZINC:</p>
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<thead><tr>
<th>Fingerprint</th>
<th>Type</th>
<th>Mean num_bits</th>
<th>SD num_bits</th>
</tr>
</thead>
<tbody>
<tr>
<td>Morgan1</td>
<td>sparse</td>
<td>29.4</td>
<td>5.6</td>
</tr>
<tr>
<td>Morgan2</td>
<td>sparse</td>
<td>48.7</td>
<td>9.6</td>
</tr>
<tr>
<td>Morgan3</td>
<td>sparse</td>
<td>66.8</td>
<td>13.8</td>
</tr>
<tr>
<td>FeatMorgan1</td>
<td>sparse</td>
<td>20.1</td>
<td>3.9</td>
</tr>
<tr>
<td>FeatMorgan2</td>
<td>sparse</td>
<td>38.1</td>
<td>7.7</td>
</tr>
<tr>
<td>FeatMorgan3</td>
<td>sparse</td>
<td>56.0</td>
<td>11.8</td>
</tr>
<tr>
<td>RDKit5</td>
<td>bitvect</td>
<td>363</td>
<td>122</td>
</tr>
<tr>
<td>RDKit6</td>
<td>bitvect</td>
<td>621</td>
<td>233</td>
</tr>
<tr>
<td>RDKit7</td>
<td>bitvect</td>
<td>993</td>
<td>406</td>
</tr>
<tr>
<td>pattern</td>
<td>bitvect</td>
<td>446</td>
<td>122</td>
</tr>
<tr>
<td>avalon</td>
<td>bitvect</td>
<td>280</td>
<td>130</td>
</tr>
<tr>
<td>atom pairs</td>
<td>sparse</td>
<td>167</td>
<td>56</td>
</tr>
<tr>
<td>TT</td>
<td>sparse</td>
<td>33.4</td>
<td>9.8</td>
</tr>
<tr>
<td>atom pairs</td>
<td>bitvect</td>
<td>267</td>
<td>90</td>
</tr>
<tr>
<td>TT</td>
<td>bitvect</td>
<td>47.2</td>
<td>12.0</td>
</tr>
</tbody>
</table>
<p>The bit vector fingerprints were all 4096 bits long.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">Chem</span><span class="p">,</span><span class="n">DataStructs</span>
<span class="kn">import</span> <span class="nn">time</span><span class="o">,</span><span class="nn">random</span><span class="o">,</span><span class="nn">gzip</span><span class="o">,</span><span class="nn">pickle</span><span class="o">,</span><span class="nn">copy</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">Counter</span><span class="p">,</span><span class="n">defaultdict</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">Draw</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">rdMolDescriptors</span>
<span class="kn">from</span> <span class="nn">rdkit.Avalon</span> <span class="kn">import</span> <span class="n">pyAvalonTools</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">IPythonConsole</span>
<span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">DataStructs</span>
<span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">rdBase</span>
<span class="o">%</span><span class="k">pylab</span> inline
<span class="nb">print</span><span class="p">(</span><span class="n">rdBase</span><span class="o">.</span><span class="n">rdkitVersion</span><span class="p">)</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="nb">print</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">asctime</span><span class="p">())</span>
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<pre>Populating the interactive namespace from numpy and matplotlib
2020.03.2
Thu May 28 05:31:00 2020
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<pre>/home/glandrum/miniconda3/envs/py37_rdkit/lib/python3.7/site-packages/IPython/core/magics/pylab.py:160: UserWarning: pylab import has clobbered these variables: ['random', 'copy']
`%matplotlib` prevents importing * from pylab and numpy
"\n`%matplotlib` prevents importing * from pylab and numpy"
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">try</span><span class="p">:</span>
<span class="kn">import</span> <span class="nn">ipyparallel</span> <span class="k">as</span> <span class="nn">ipp</span>
<span class="n">rc</span> <span class="o">=</span> <span class="n">ipp</span><span class="o">.</span><span class="n">Client</span><span class="p">()</span>
<span class="n">dview</span> <span class="o">=</span> <span class="n">rc</span><span class="p">[:]</span>
<span class="n">dview</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'from rdkit import Chem'</span><span class="p">)</span>
<span class="n">dview</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'from rdkit import Descriptors'</span><span class="p">)</span>
<span class="n">dview</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'from rdkit.Chem import rdMolDescriptors'</span><span class="p">)</span>
<span class="n">dview</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'from rdkit.Avalon import pyAvalonTools'</span><span class="p">)</span>
<span class="k">except</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"could not use ipyparallel"</span><span class="p">)</span>
<span class="n">dview</span> <span class="o">=</span> <span class="kc">None</span>
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<p>For test data I'll use the same 16 million ZINC compounds I used in the <a href="https://rdkit.blogspot.ch/2016/02/morgan-fingerprint-bit-statistics.html">bit statistics post</a>.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">filen</span><span class="o">=</span><span class="s1">'/scratch/RDKit_git/LocalData/Zinc/zinc_all_clean.pkl.gz'</span>
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<p>Loop over the molecules, skip anything with more than 50 atoms, and build fingerprints for all the others.</p>
<p>The fingerprints I generate for this analysis are:</p>
<ul>
<li>Sparse Morgan with radii 1, 2, and 3</li>
<li>Sparse FeatureMorgan with radii 1, 2, and 3</li>
<li>RDKit BitVect with maxPath 5, 6, and 7</li>
<li>Pattern BitVect</li>
<li>Avalon BitVect</li>
<li>Sparse Atom Pairs</li>
<li>Sparse Topological Torsions</li>
<li>Atom Pair BitVect</li>
<li>Topological Torsion BitVect</li>
</ul>
<p>All of the BitVect fingerprints are 4096 bits long</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">copy</span>
<span class="n">historyf</span> <span class="o">=</span> <span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">'../data/fp_bit_counts.history.pkl.gz'</span><span class="p">,</span><span class="s1">'wb+'</span><span class="p">)</span>
<span class="n">counts</span><span class="o">=</span><span class="n">defaultdict</span><span class="p">(</span><span class="n">Counter</span><span class="p">)</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="k">with</span> <span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">filen</span><span class="p">,</span><span class="s1">'rb'</span><span class="p">)</span> <span class="k">as</span> <span class="n">inf</span><span class="p">:</span>
<span class="n">i</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">ms</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">while</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">m</span><span class="p">,</span><span class="n">nm</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">inf</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">EOFError</span><span class="p">:</span>
<span class="k">break</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">m</span> <span class="ow">or</span> <span class="n">m</span><span class="o">.</span><span class="n">GetNumHeavyAtoms</span><span class="p">()</span><span class="o">></span><span class="mi">50</span><span class="p">:</span> <span class="k">continue</span>
<span class="n">ms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">i</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">ms</span><span class="p">)</span><span class="o">>=</span><span class="mi">10000</span><span class="p">:</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">:</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="n">dview</span><span class="o">.</span><span class="n">map_sync</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span><span class="n">v</span><span class="o">=</span><span class="n">v</span><span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">GetMorganFingerprint</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">v</span><span class="p">)</span><span class="o">.</span><span class="n">GetNonzeroElements</span><span class="p">()),</span>
<span class="n">ms</span><span class="p">)</span>
<span class="k">for</span> <span class="n">obc</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">:</span>
<span class="n">counts</span><span class="p">[(</span><span class="s1">'Morgan'</span><span class="p">,</span><span class="n">v</span><span class="p">)][</span><span class="n">obc</span><span class="p">]</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">:</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="n">dview</span><span class="o">.</span><span class="n">map_sync</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span><span class="n">v</span><span class="o">=</span><span class="n">v</span><span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">GetMorganFingerprint</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">v</span><span class="p">,</span><span class="n">useFeatures</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span><span class="o">.</span><span class="n">GetNonzeroElements</span><span class="p">()),</span>
<span class="n">ms</span><span class="p">)</span>
<span class="k">for</span> <span class="n">obc</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">:</span>
<span class="n">counts</span><span class="p">[(</span><span class="s1">'FeatMorgan'</span><span class="p">,</span><span class="n">v</span><span class="p">)][</span><span class="n">obc</span><span class="p">]</span><span class="o">+=</span><span class="mi">1</span>
<span class="k">for</span> <span class="n">v</span> <span class="ow">in</span> <span class="mi">5</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">7</span><span class="p">:</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="n">dview</span><span class="o">.</span><span class="n">map_sync</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">,</span><span class="n">v</span><span class="o">=</span><span class="n">v</span><span class="p">:</span><span class="n">Chem</span><span class="o">.</span><span class="n">RDKFingerprint</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">maxPath</span><span class="o">=</span><span class="n">v</span><span class="p">,</span><span class="n">fpSize</span><span class="o">=</span><span class="mi">4096</span><span class="p">)</span><span class="o">.</span><span class="n">GetNumOnBits</span><span class="p">(),</span>
<span class="n">ms</span><span class="p">)</span>
<span class="k">for</span> <span class="n">obc</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">:</span>
<span class="n">counts</span><span class="p">[(</span><span class="s1">'RDKit'</span><span class="p">,</span><span class="n">v</span><span class="p">)][</span><span class="n">obc</span><span class="p">]</span><span class="o">+=</span><span class="mi">1</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="n">dview</span><span class="o">.</span><span class="n">map_sync</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="n">Chem</span><span class="o">.</span><span class="n">PatternFingerprint</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">fpSize</span><span class="o">=</span><span class="mi">4096</span><span class="p">)</span><span class="o">.</span><span class="n">GetNumOnBits</span><span class="p">(),</span>
<span class="n">ms</span><span class="p">)</span>
<span class="k">for</span> <span class="n">obc</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">:</span>
<span class="n">counts</span><span class="p">[(</span><span class="s1">'pattern'</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)][</span><span class="n">obc</span><span class="p">]</span><span class="o">+=</span><span class="mi">1</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="n">dview</span><span class="o">.</span><span class="n">map_sync</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="n">pyAvalonTools</span><span class="o">.</span><span class="n">GetAvalonFP</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">nBits</span><span class="o">=</span><span class="mi">4096</span><span class="p">)</span><span class="o">.</span><span class="n">GetNumOnBits</span><span class="p">(),</span>
<span class="n">ms</span><span class="p">)</span>
<span class="k">for</span> <span class="n">obc</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">:</span>
<span class="n">counts</span><span class="p">[(</span><span class="s1">'avalon'</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)][</span><span class="n">obc</span><span class="p">]</span><span class="o">+=</span><span class="mi">1</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="n">dview</span><span class="o">.</span><span class="n">map_sync</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">GetAtomPairFingerprint</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">GetNonzeroElements</span><span class="p">()),</span>
<span class="n">ms</span><span class="p">)</span>
<span class="k">for</span> <span class="n">obc</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">:</span>
<span class="n">counts</span><span class="p">[(</span><span class="s1">'ap-counts'</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)][</span><span class="n">obc</span><span class="p">]</span><span class="o">+=</span><span class="mi">1</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="n">dview</span><span class="o">.</span><span class="n">map_sync</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="nb">len</span><span class="p">(</span><span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">GetTopologicalTorsionFingerprint</span><span class="p">(</span><span class="n">x</span><span class="p">)</span><span class="o">.</span><span class="n">GetNonzeroElements</span><span class="p">()),</span>
<span class="n">ms</span><span class="p">)</span>
<span class="k">for</span> <span class="n">obc</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">:</span>
<span class="n">counts</span><span class="p">[(</span><span class="s1">'tt-counts'</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)][</span><span class="n">obc</span><span class="p">]</span><span class="o">+=</span><span class="mi">1</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="n">dview</span><span class="o">.</span><span class="n">map_sync</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">GetHashedAtomPairFingerprintAsBitVect</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">nBits</span><span class="o">=</span><span class="mi">4096</span><span class="p">)</span><span class="o">.</span><span class="n">GetNumOnBits</span><span class="p">(),</span>
<span class="n">ms</span><span class="p">)</span>
<span class="k">for</span> <span class="n">obc</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">:</span>
<span class="n">counts</span><span class="p">[(</span><span class="s1">'ap-bv'</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)][</span><span class="n">obc</span><span class="p">]</span><span class="o">+=</span><span class="mi">1</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="n">dview</span><span class="o">.</span><span class="n">map_sync</span><span class="p">(</span><span class="k">lambda</span> <span class="n">x</span><span class="p">:</span><span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">GetHashedTopologicalTorsionFingerprintAsBitVect</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">nBits</span><span class="o">=</span><span class="mi">4096</span><span class="p">)</span><span class="o">.</span><span class="n">GetNumOnBits</span><span class="p">(),</span>
<span class="n">ms</span><span class="p">)</span>
<span class="k">for</span> <span class="n">obc</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">:</span>
<span class="n">counts</span><span class="p">[(</span><span class="s1">'tt-bv'</span><span class="p">,</span><span class="o">-</span><span class="mi">1</span><span class="p">)][</span><span class="n">obc</span><span class="p">]</span><span class="o">+=</span><span class="mi">1</span>
<span class="n">ms</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">i</span><span class="o">%</span><span class="k">50000</span>:
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"Done </span><span class="si">%d</span><span class="s2"> in </span><span class="si">%.2f</span><span class="s2"> sec"</span><span class="o">%</span><span class="p">(</span><span class="n">i</span><span class="p">,</span><span class="n">t2</span><span class="o">-</span><span class="n">t1</span><span class="p">))</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">i</span><span class="o">%</span><span class="k">500000</span>:
<span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">counts</span><span class="p">),</span><span class="n">historyf</span><span class="p">)</span>
<span class="k">if</span> <span class="n">i</span><span class="o">>=</span><span class="mi">5000000</span><span class="p">:</span>
<span class="k">break</span>
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<pre>Done 50000 in 33.84 sec
Done 100000 in 68.21 sec
Done 150000 in 102.05 sec
Done 200000 in 144.86 sec
Done 250000 in 191.21 sec
Done 300000 in 237.45 sec
Done 350000 in 284.70 sec
Done 400000 in 332.91 sec
Done 450000 in 370.83 sec
Done 500000 in 418.55 sec
Done 550000 in 470.13 sec
Done 600000 in 510.53 sec
Done 650000 in 558.36 sec
Done 700000 in 604.75 sec
Done 750000 in 649.47 sec
Done 800000 in 695.77 sec
Done 850000 in 739.01 sec
Done 900000 in 783.58 sec
Done 950000 in 827.22 sec
Done 1000000 in 870.85 sec
Done 1050000 in 914.38 sec
Done 1100000 in 957.65 sec
Done 1150000 in 1001.25 sec
Done 1200000 in 1045.27 sec
Done 1250000 in 1086.67 sec
Done 1300000 in 1126.09 sec
Done 1350000 in 1171.17 sec
Done 1400000 in 1215.29 sec
Done 1450000 in 1256.11 sec
Done 1500000 in 1300.58 sec
Done 1550000 in 1341.92 sec
Done 1600000 in 1382.68 sec
Done 1650000 in 1423.52 sec
Done 1700000 in 1464.51 sec
Done 1750000 in 1512.74 sec
Done 1800000 in 1559.71 sec
Done 1850000 in 1611.70 sec
Done 1900000 in 1661.66 sec
Done 1950000 in 1712.89 sec
Done 2000000 in 1764.45 sec
Done 2050000 in 1820.24 sec
Done 2100000 in 1874.09 sec
Done 2150000 in 1926.30 sec
Done 2200000 in 1977.40 sec
Done 2250000 in 2025.98 sec
Done 2300000 in 2071.92 sec
Done 2350000 in 2118.75 sec
Done 2400000 in 2165.23 sec
Done 2450000 in 2212.75 sec
Done 2500000 in 2261.27 sec
Done 2550000 in 2309.62 sec
Done 2600000 in 2355.43 sec
Done 2650000 in 2403.02 sec
Done 2700000 in 2450.79 sec
Done 2750000 in 2497.79 sec
Done 2800000 in 2544.90 sec
Done 2850000 in 2591.88 sec
Done 2900000 in 2640.37 sec
Done 2950000 in 2692.71 sec
Done 3000000 in 2739.17 sec
Done 3050000 in 2787.73 sec
Done 3100000 in 2832.19 sec
Done 3150000 in 2882.86 sec
Done 3200000 in 2931.07 sec
Done 3250000 in 2977.42 sec
Done 3300000 in 3023.65 sec
Done 3350000 in 3070.12 sec
Done 3400000 in 3115.96 sec
Done 3450000 in 3160.61 sec
Done 3500000 in 3205.97 sec
Done 3550000 in 3250.91 sec
Done 3600000 in 3296.68 sec
Done 3650000 in 3343.29 sec
Done 3700000 in 3387.35 sec
Done 3750000 in 3432.67 sec
Done 3800000 in 3477.16 sec
Done 3850000 in 3524.60 sec
Done 3900000 in 3575.61 sec
Done 3950000 in 3624.95 sec
Done 4000000 in 3672.21 sec
Done 4050000 in 3720.89 sec
Done 4100000 in 3768.69 sec
Done 4150000 in 3810.06 sec
Done 4200000 in 3855.77 sec
Done 4250000 in 3900.81 sec
Done 4300000 in 3946.37 sec
Done 4350000 in 3990.56 sec
Done 4400000 in 4035.36 sec
Done 4450000 in 4076.46 sec
Done 4500000 in 4118.95 sec
Done 4550000 in 4161.02 sec
Done 4600000 in 4202.55 sec
Done 4650000 in 4243.38 sec
Done 4700000 in 4284.74 sec
Done 4750000 in 4328.13 sec
Done 4800000 in 4370.41 sec
Done 4850000 in 4419.91 sec
Done 4900000 in 4469.38 sec
Done 4950000 in 4519.92 sec
Done 5000000 in 4564.51 sec
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<div class="prompt input_prompt">In [5]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="nb">dict</span><span class="p">(</span><span class="n">counts</span><span class="p">),</span><span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">'../data/fp_bit_counts.pkl.gz'</span><span class="p">,</span><span class="s1">'wb+'</span><span class="p">))</span>
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<p>Now plot the distributions of the number of bits set</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">morgan_ks</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">if</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span><span class="s1">'Morgan'</span><span class="p">]</span>
<span class="n">featmorgan_ks</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">if</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span><span class="s1">'FeatMorgan'</span><span class="p">]</span>
<span class="n">rdkit_ks</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">if</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'RDKit'</span><span class="p">]</span>
<span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">15</span><span class="p">))</span>
<span class="n">pidx</span><span class="o">=</span><span class="mi">1</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">pidx</span><span class="p">)</span>
<span class="k">for</span> <span class="n">n</span><span class="p">,</span><span class="n">r</span> <span class="ow">in</span> <span class="n">morgan_ks</span><span class="p">:</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">counts</span><span class="p">[(</span><span class="n">n</span><span class="p">,</span><span class="n">r</span><span class="p">)]</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
<span class="n">plot</span><span class="p">([</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">],[</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">],</span><span class="n">label</span><span class="o">=</span>
<span class="sa">f</span><span class="s2">"r=</span><span class="si">{</span><span class="n">r</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">title</span><span class="p">(</span><span class="s2">"Morgan"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"num bits set"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"count"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">legend</span><span class="p">()</span>
<span class="n">pidx</span><span class="o">=</span><span class="mi">2</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">pidx</span><span class="p">)</span>
<span class="k">for</span> <span class="n">n</span><span class="p">,</span><span class="n">r</span> <span class="ow">in</span> <span class="n">featmorgan_ks</span><span class="p">:</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">counts</span><span class="p">[(</span><span class="n">n</span><span class="p">,</span><span class="n">r</span><span class="p">)]</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
<span class="n">plot</span><span class="p">([</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">],[</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">],</span><span class="n">label</span><span class="o">=</span>
<span class="sa">f</span><span class="s2">"r=</span><span class="si">{</span><span class="n">r</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">title</span><span class="p">(</span><span class="s2">"FeatMorgan"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"num bits set"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"count"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">legend</span><span class="p">()</span>
<span class="n">pidx</span><span class="o">=</span><span class="mi">3</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">pidx</span><span class="p">)</span>
<span class="k">for</span> <span class="n">n</span><span class="p">,</span><span class="n">r</span> <span class="ow">in</span> <span class="n">rdkit_ks</span><span class="p">:</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">counts</span><span class="p">[(</span><span class="n">n</span><span class="p">,</span><span class="n">r</span><span class="p">)]</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
<span class="n">plot</span><span class="p">([</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">],[</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">],</span><span class="n">label</span><span class="o">=</span>
<span class="sa">f</span><span class="s2">"r=</span><span class="si">{</span><span class="n">r</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">title</span><span class="p">(</span><span class="s2">"RDKit"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"num bits set"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"count"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">legend</span><span class="p">()</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
<span class="k">if</span> <span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'Morgan'</span><span class="p">,</span><span class="s1">'FeatMorgan'</span><span class="p">,</span><span class="s1">'RDKit'</span><span class="p">):</span>
<span class="k">continue</span>
<span class="n">pidx</span><span class="o">+=</span><span class="mi">1</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">pidx</span><span class="p">)</span>
<span class="n">cnts</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">counts</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">items</span><span class="p">())</span>
<span class="n">plot</span><span class="p">([</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">],[</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">cnts</span><span class="p">])</span>
<span class="n">_</span><span class="o">=</span><span class="n">title</span><span class="p">(</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">_</span><span class="o">=</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"num bits set"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"count"</span><span class="p">)</span>
</pre></div>
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<p>The avalon FP curve has an interesting shape</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">cnts</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">accum</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">denom</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">cnt</span><span class="p">,</span><span class="n">num</span> <span class="ow">in</span> <span class="n">cnts</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">accum</span> <span class="o">+=</span> <span class="n">cnt</span><span class="o">*</span><span class="n">num</span>
<span class="n">denom</span> <span class="o">+=</span> <span class="n">num</span>
<span class="n">mean</span> <span class="o">=</span> <span class="n">accum</span><span class="o">/</span><span class="n">denom</span>
<span class="n">dev</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">cnt</span><span class="p">,</span><span class="n">num</span> <span class="ow">in</span> <span class="n">cnts</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">dev</span> <span class="o">+=</span> <span class="n">num</span><span class="o">*</span><span class="p">(</span><span class="n">cnt</span><span class="o">-</span><span class="n">mean</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
<span class="n">dev</span> <span class="o">/=</span> <span class="p">(</span><span class="n">denom</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">dev</span> <span class="o">=</span> <span class="n">dev</span><span class="o">**</span><span class="mf">0.5</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">!=-</span><span class="mi">1</span><span class="p">:</span>
<span class="n">label</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">label</span><span class="p">,</span><span class="s1">'</span><span class="se">\t</span><span class="si">%.1f</span><span class="s1">'</span><span class="o">%</span><span class="k">mean</span>,'%.1f'%dev)
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<pre>Morgan1 29.4 5.6
Morgan2 48.7 9.6
Morgan3 66.8 13.8
FeatMorgan1 20.1 3.9
FeatMorgan2 38.1 7.7
FeatMorgan3 56.0 11.8
RDKit5 363.3 122.5
RDKit6 621.7 233.2
RDKit7 993.6 406.3
pattern 445.5 122.5
avalon 279.8 129.9
ap-counts 166.6 56.3
tt-counts 33.4 9.8
ap-bv 267.3 90.0
tt-bv 47.2 12.0
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<h1 id="Convergence">Convergence<a class="anchor-link" href="#Convergence">¶</a></h1><p>I did 5 million examples, which took a while (about 1.5 hours with 6 worker processes on my PC). Could I have analyzed less and gotten to the same results? Did the means converge? If so, how quickly?</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">historyf</span> <span class="o">=</span> <span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">'../data/fp_bit_counts.history.pkl.gz'</span><span class="p">,</span><span class="s1">'rb'</span><span class="p">)</span>
<span class="n">means</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="n">devs</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="n">nmols</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">while</span> <span class="mi">1</span><span class="p">:</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">lcounts</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">historyf</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">EOFError</span><span class="p">:</span>
<span class="k">break</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">cnts</span> <span class="ow">in</span> <span class="n">lcounts</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">accum</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">denom</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">cnt</span><span class="p">,</span><span class="n">num</span> <span class="ow">in</span> <span class="n">cnts</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">accum</span> <span class="o">+=</span> <span class="n">cnt</span><span class="o">*</span><span class="n">num</span>
<span class="n">denom</span> <span class="o">+=</span> <span class="n">num</span>
<span class="n">mean</span> <span class="o">=</span> <span class="n">accum</span><span class="o">/</span><span class="n">denom</span>
<span class="n">dev</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">cnt</span><span class="p">,</span><span class="n">num</span> <span class="ow">in</span> <span class="n">cnts</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">dev</span> <span class="o">+=</span> <span class="n">num</span><span class="o">*</span><span class="p">(</span><span class="n">cnt</span><span class="o">-</span><span class="n">mean</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
<span class="n">dev</span> <span class="o">/=</span> <span class="p">(</span><span class="n">denom</span><span class="o">-</span><span class="mi">1</span><span class="p">)</span>
<span class="n">dev</span> <span class="o">=</span> <span class="n">dev</span><span class="o">**</span><span class="mf">0.5</span>
<span class="k">if</span> <span class="n">denom</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">nmols</span><span class="p">:</span>
<span class="n">nmols</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">denom</span><span class="p">)</span>
<span class="n">means</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mean</span><span class="p">)</span>
<span class="n">devs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">dev</span><span class="p">)</span>
<span class="n">label</span> <span class="o">=</span> <span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">if</span> <span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">!=-</span><span class="mi">1</span><span class="p">:</span>
<span class="n">label</span> <span class="o">+=</span> <span class="nb">str</span><span class="p">(</span><span class="n">k</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">denom</span><span class="p">,</span><span class="n">label</span><span class="p">,</span><span class="s1">'</span><span class="se">\t</span><span class="si">%.1f</span><span class="s1">'</span><span class="o">%</span><span class="k">mean</span>,'%.1f'%dev)
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<pre>500000 Morgan1 26.0 6.2
500000 Morgan2 42.8 10.7
500000 Morgan3 58.7 15.5
500000 FeatMorgan1 18.2 4.3
500000 FeatMorgan2 33.8 8.5
500000 FeatMorgan3 49.5 13.2
500000 RDKit5 324.6 133.9
500000 RDKit6 560.8 256.2
500000 RDKit7 902.9 445.7
500000 pattern 408.8 133.9
500000 avalon 241.8 133.8
500000 ap-counts 133.3 57.6
500000 tt-counts 28.6 10.2
500000 ap-bv 219.5 93.6
500000 tt-bv 41.9 12.9
1000000 Morgan1 27.1 6.1
1000000 Morgan2 44.6 10.5
1000000 Morgan3 61.2 15.2
1000000 FeatMorgan1 18.9 4.2
1000000 FeatMorgan2 35.2 8.4
1000000 FeatMorgan3 51.6 13.0
1000000 RDKit5 340.7 133.9
1000000 RDKit6 588.9 257.4
1000000 RDKit7 948.5 449.9
1000000 pattern 425.2 136.0
1000000 avalon 257.7 136.7
1000000 ap-counts 143.7 57.7
1000000 tt-counts 30.1 10.1
1000000 ap-bv 234.4 92.8
1000000 tt-bv 43.6 12.9
1500000 Morgan1 27.3 5.8
1500000 Morgan2 45.0 9.9
1500000 Morgan3 61.7 14.3
1500000 FeatMorgan1 19.0 4.1
1500000 FeatMorgan2 35.5 8.0
1500000 FeatMorgan3 52.0 12.3
1500000 RDKit5 340.3 127.8
1500000 RDKit6 587.1 246.2
1500000 RDKit7 944.8 432.0
1500000 pattern 424.0 129.4
1500000 avalon 260.5 133.7
1500000 ap-counts 145.1 54.8
1500000 tt-counts 30.5 9.8
1500000 ap-bv 234.9 87.3
1500000 tt-bv 43.7 12.3
2000000 Morgan1 28.0 5.7
2000000 Morgan2 46.2 9.8
2000000 Morgan3 63.4 14.1
2000000 FeatMorgan1 19.4 4.0
2000000 FeatMorgan2 36.3 7.9
2000000 FeatMorgan3 53.3 12.1
2000000 RDKit5 350.7 126.6
2000000 RDKit6 603.5 243.1
2000000 RDKit7 969.0 425.8
2000000 pattern 433.3 128.0
2000000 avalon 269.5 133.1
2000000 ap-counts 152.4 55.5
2000000 tt-counts 31.5 9.8
2000000 ap-bv 245.8 88.2
2000000 tt-bv 45.0 12.2
2500000 Morgan1 28.7 5.8
2500000 Morgan2 47.5 9.8
2500000 Morgan3 65.3 14.2
2500000 FeatMorgan1 19.7 4.0
2500000 FeatMorgan2 37.2 7.9
2500000 FeatMorgan3 54.7 12.1
2500000 RDKit5 361.5 126.3
2500000 RDKit6 621.2 241.1
2500000 RDKit7 996.0 420.5
2500000 pattern 443.2 126.4
2500000 avalon 278.4 132.6
2500000 ap-counts 160.1 56.9
2500000 tt-counts 32.6 9.9
2500000 ap-bv 257.9 90.2
2500000 tt-bv 46.3 12.2
3000000 Morgan1 29.1 5.7
3000000 Morgan2 48.1 9.8
3000000 Morgan3 66.1 14.1
3000000 FeatMorgan1 19.9 3.9
3000000 FeatMorgan2 37.6 7.8
3000000 FeatMorgan3 55.3 12.0
3000000 RDKit5 364.5 124.5
3000000 RDKit6 625.3 237.2
3000000 RDKit7 1001.4 413.2
3000000 pattern 446.5 124.1
3000000 avalon 280.5 131.5
3000000 ap-counts 163.7 57.0
3000000 tt-counts 33.1 9.8
3000000 ap-bv 263.5 90.5
3000000 tt-bv 46.9 12.1
3500000 Morgan1 29.2 5.7
3500000 Morgan2 48.3 9.7
3500000 Morgan3 66.4 14.0
3500000 FeatMorgan1 19.9 3.9
3500000 FeatMorgan2 37.7 7.8
3500000 FeatMorgan3 55.6 11.9
3500000 RDKit5 365.3 123.8
3500000 RDKit6 626.7 236.0
3500000 RDKit7 1003.7 411.3
3500000 pattern 448.4 123.3
3500000 avalon 280.3 131.1
3500000 ap-counts 165.1 56.7
3500000 tt-counts 33.3 9.8
3500000 ap-bv 265.9 90.1
3500000 tt-bv 47.2 12.1
4000000 Morgan1 29.4 5.7
4000000 Morgan2 48.6 9.8
4000000 Morgan3 66.7 14.1
4000000 FeatMorgan1 20.0 3.9
4000000 FeatMorgan2 38.0 7.8
4000000 FeatMorgan3 55.9 12.0
4000000 RDKit5 365.2 124.1
4000000 RDKit6 627.1 236.6
4000000 RDKit7 1005.0 412.4
4000000 pattern 448.6 124.0
4000000 avalon 281.4 131.3
4000000 ap-counts 165.7 56.9
4000000 tt-counts 33.4 9.9
4000000 ap-bv 266.8 90.6
4000000 tt-bv 47.3 12.2
4500000 Morgan1 29.4 5.6
4500000 Morgan2 48.7 9.6
4500000 Morgan3 66.8 13.9
4500000 FeatMorgan1 20.1 3.9
4500000 FeatMorgan2 38.0 7.7
4500000 FeatMorgan3 55.9 11.8
4500000 RDKit5 364.3 123.1
4500000 RDKit6 624.4 234.6
4500000 RDKit7 999.1 408.8
4500000 pattern 447.0 122.7
4500000 avalon 280.7 130.6
4500000 ap-counts 166.3 56.4
4500000 tt-counts 33.4 9.8
4500000 ap-bv 267.3 89.9
4500000 tt-bv 47.3 12.1
5000000 Morgan1 29.4 5.6
5000000 Morgan2 48.7 9.6
5000000 Morgan3 66.8 13.8
5000000 FeatMorgan1 20.1 3.9
5000000 FeatMorgan2 38.1 7.7
5000000 FeatMorgan3 56.0 11.8
5000000 RDKit5 363.3 122.5
5000000 RDKit6 621.7 233.2
5000000 RDKit7 993.6 406.3
5000000 pattern 445.5 122.5
5000000 avalon 279.8 129.9
5000000 ap-counts 166.6 56.3
5000000 tt-counts 33.4 9.8
5000000 ap-bv 267.3 90.0
5000000 tt-bv 47.2 12.0
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<p>Let's look at those graphically:</p>
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<div class="prompt input_prompt">In [31]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">morgan_ks</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">if</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span><span class="s1">'Morgan'</span><span class="p">]</span>
<span class="n">featmorgan_ks</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">if</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span><span class="s1">'FeatMorgan'</span><span class="p">]</span>
<span class="n">rdkit_ks</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">if</span> <span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">'RDKit'</span><span class="p">]</span>
<span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">15</span><span class="p">,</span><span class="mi">15</span><span class="p">))</span>
<span class="n">nmols2</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">/</span><span class="mi">1000000</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">nmols</span><span class="p">]</span>
<span class="n">pidx</span><span class="o">=</span><span class="mi">1</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">pidx</span><span class="p">)</span>
<span class="k">for</span> <span class="n">n</span><span class="p">,</span><span class="n">r</span> <span class="ow">in</span> <span class="n">morgan_ks</span><span class="p">:</span>
<span class="n">lmeans</span> <span class="o">=</span> <span class="n">means</span><span class="p">[(</span><span class="n">n</span><span class="p">,</span><span class="n">r</span><span class="p">)]</span>
<span class="n">ldevs</span> <span class="o">=</span> <span class="n">devs</span><span class="p">[(</span><span class="n">n</span><span class="p">,</span><span class="n">r</span><span class="p">)]</span>
<span class="n">errorbar</span><span class="p">(</span><span class="n">nmols2</span><span class="p">,</span><span class="n">lmeans</span><span class="p">,</span><span class="n">yerr</span><span class="o">=</span><span class="n">ldevs</span><span class="p">,</span><span class="n">capsize</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">title</span><span class="p">(</span><span class="s2">"Morgan"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"num mols (millions)"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"count"</span><span class="p">)</span>
<span class="c1">#_=legend()</span>
<span class="n">pidx</span><span class="o">=</span><span class="mi">2</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">pidx</span><span class="p">)</span>
<span class="k">for</span> <span class="n">n</span><span class="p">,</span><span class="n">r</span> <span class="ow">in</span> <span class="n">featmorgan_ks</span><span class="p">:</span>
<span class="n">lmeans</span> <span class="o">=</span> <span class="n">means</span><span class="p">[(</span><span class="n">n</span><span class="p">,</span><span class="n">r</span><span class="p">)]</span>
<span class="n">ldevs</span> <span class="o">=</span> <span class="n">devs</span><span class="p">[(</span><span class="n">n</span><span class="p">,</span><span class="n">r</span><span class="p">)]</span>
<span class="n">errorbar</span><span class="p">(</span><span class="n">nmols2</span><span class="p">,</span><span class="n">lmeans</span><span class="p">,</span><span class="n">yerr</span><span class="o">=</span><span class="n">ldevs</span><span class="p">,</span><span class="n">capsize</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">title</span><span class="p">(</span><span class="s2">"FeatMorgan"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"num mols (millions)"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"count"</span><span class="p">)</span>
<span class="c1">#_=legend()</span>
<span class="n">pidx</span><span class="o">=</span><span class="mi">3</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">pidx</span><span class="p">)</span>
<span class="k">for</span> <span class="n">n</span><span class="p">,</span><span class="n">r</span> <span class="ow">in</span> <span class="n">rdkit_ks</span><span class="p">:</span>
<span class="n">lmeans</span> <span class="o">=</span> <span class="n">means</span><span class="p">[(</span><span class="n">n</span><span class="p">,</span><span class="n">r</span><span class="p">)]</span>
<span class="n">ldevs</span> <span class="o">=</span> <span class="n">devs</span><span class="p">[(</span><span class="n">n</span><span class="p">,</span><span class="n">r</span><span class="p">)]</span>
<span class="n">errorbar</span><span class="p">(</span><span class="n">nmols2</span><span class="p">,</span><span class="n">lmeans</span><span class="p">,</span><span class="n">yerr</span><span class="o">=</span><span class="n">ldevs</span><span class="p">,</span><span class="n">capsize</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">title</span><span class="p">(</span><span class="s2">"RDKit"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"num mols (millions)"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"count"</span><span class="p">)</span>
<span class="c1">#_=legend()</span>
<span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="n">counts</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
<span class="k">if</span> <span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'Morgan'</span><span class="p">,</span><span class="s1">'FeatMorgan'</span><span class="p">,</span><span class="s1">'RDKit'</span><span class="p">):</span>
<span class="k">continue</span>
<span class="n">pidx</span><span class="o">+=</span><span class="mi">1</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="n">pidx</span><span class="p">)</span>
<span class="n">lmeans</span> <span class="o">=</span> <span class="n">means</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
<span class="n">ldevs</span> <span class="o">=</span> <span class="n">devs</span><span class="p">[</span><span class="n">k</span><span class="p">]</span>
<span class="n">errorbar</span><span class="p">(</span><span class="n">nmols2</span><span class="p">,</span><span class="n">lmeans</span><span class="p">,</span><span class="n">yerr</span><span class="o">=</span><span class="n">ldevs</span><span class="p">,</span><span class="n">capsize</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">title</span><span class="p">(</span><span class="n">k</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
<span class="n">_</span><span class="o">=</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"num mols (millions)"</span><span class="p">)</span>
<span class="n">_</span><span class="o">=</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"count"</span><span class="p">)</span>
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<p>Looks like we would have been fine with 3 million molecules.</p>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com0tag:blogger.com,1999:blog-684443317148892945.post-26696698068498067472020-05-08T01:11:00.000-07:002020-05-08T01:11:18.984-07:00Binary molecules and the cartridge<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>I had a couple of online conversations this week about working with the PostgreSQL cartridge from Python and sending molecules back and forth between Python and the database. Here's a quick blogpost on that topic.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">Chem</span>
<span class="kn">import</span> <span class="nn">gzip</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="kn">import</span> <span class="nn">psycopg2</span>
<span class="kn">import</span> <span class="nn">json</span>
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<p>The dataset we'll use here is a set of molecules+activity data from ChEMBL26. We take all the measured Ki values that are less than 1nM.</p>
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# here's how I constructed the dataset
conn2 = psycopg2.connect("dbname=chembl_26 host=localhost")
curs2 = conn2.cursor()
curs2.execute('''select cid1.chembl_id as compound_chembl_id,cid2.chembl_id as assay_chembl_id,
target_dictionary.chembl_id as target_chembl_id,target_dictionary.pref_name as pref_name,
standard_relation,standard_value,standard_units,standard_type,molfile
from activities acts
join assays using (assay_id)
join compound_structures using (molregno)
join chembl_id_lookup cid1 on (molregno=entity_id and entity_type='COMPOUND')
join chembl_id_lookup cid2 on (assay_id=cid2.entity_id and cid2.entity_type='ASSAY')
join target_dictionary using (tid)
where standard_type='Ki' and standard_units='nM' and standard_value is not null and
standard_relation='=' and standard_value<1''')
data = curs2.fetchall()
import gzip
cnames = [x.name for x in curs2.description]
w = Chem.SDWriter(gzip.open('/home/glandrum/RDKit_blog/data/chembl26_very_active.sdf.gz','wt+'))
for row in data:
m = Chem.MolFromMolBlock(row[-1])
for i in range(len(cnames)-1):
m.SetProp(cnames[i],str(row[i]))
w.write(m)
w=None
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<p>Let's start by assembling a benchmarking set of 30K molblocks:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">molblocks</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">nms</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">with</span> <span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">'../data/chembl26_very_active.sdf.gz'</span><span class="p">,</span><span class="s1">'r'</span><span class="p">)</span> <span class="k">as</span> <span class="n">inf</span><span class="p">:</span>
<span class="n">suppl</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">ForwardSDMolSupplier</span><span class="p">(</span><span class="n">inf</span><span class="p">)</span>
<span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">molblocks</span><span class="p">)</span><span class="o"><</span><span class="mi">30000</span><span class="p">:</span>
<span class="n">m</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">suppl</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">m</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">nms</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">GetProp</span><span class="p">(</span><span class="s1">'compound_chembl_id'</span><span class="p">))</span>
<span class="n">molblocks</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolToMolBlock</span><span class="p">(</span><span class="n">m</span><span class="p">))</span>
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<p>How long does it take to parse all the molblocks on the python side?</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">ms</span> <span class="o">=</span> <span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromMolBlock</span><span class="p">(</span><span class="n">mb</span><span class="p">)</span> <span class="k">for</span> <span class="n">mb</span> <span class="ow">in</span> <span class="n">molblocks</span><span class="p">]</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" that took {t2-t1 :.2f} seconds"</span><span class="p">)</span>
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<pre> that took 6.88 seconds
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<p>What about to do the same work in the database?</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">conn</span> <span class="o">=</span> <span class="n">psycopg2</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s2">"dbname=demodb host=localhost"</span><span class="p">)</span>
<span class="n">curs</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span>
<span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'drop table if exists molbs'</span><span class="p">)</span>
<span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'drop table if exists mols'</span><span class="p">)</span>
<span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'create table molbs (chembl_id text,molb text)'</span><span class="p">)</span>
<span class="n">curs</span><span class="o">.</span><span class="n">executemany</span><span class="p">(</span><span class="s1">'insert into molbs values (</span><span class="si">%s</span><span class="s1">,</span><span class="si">%s</span><span class="s1">)'</span><span class="p">,[(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">nms</span><span class="p">,</span><span class="n">molblocks</span><span class="p">)])</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'select chembl_id,mol_from_ctab(molb::cstring) m into mols from molbs'</span><span class="p">)</span>
<span class="n">conn</span><span class="o">.</span><span class="n">commit</span><span class="p">()</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" that took {t2-t1 :.2f} seconds"</span><span class="p">)</span>
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<pre> that took 7.83 seconds
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<p>Notice that we also had to transfer the mol blocks to the database. I didn't include that in the timing results because we're just looking at processing time.</p>
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<h2 id="Sending-binary-molecules-to-the-database">Sending binary molecules to the database<a class="anchor-link" href="#Sending-binary-molecules-to-the-database">¶</a></h2><p>It seems silly to do the work of processing the mol blocks in the database a second time. Fortunately, we can add the molecules to the database in RDKit's binary form:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">conn</span> <span class="o">=</span> <span class="n">psycopg2</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s2">"dbname=demodb host=localhost"</span><span class="p">)</span>
<span class="n">curs</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span>
<span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'drop table if exists mols'</span><span class="p">)</span>
<span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'create table mols (chembl_id text,m mol)'</span><span class="p">)</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">curs</span><span class="o">.</span><span class="n">executemany</span><span class="p">(</span><span class="s1">'insert into mols values (</span><span class="si">%s</span><span class="s1">,mol_from_pkl(</span><span class="si">%s</span><span class="s1">))'</span><span class="p">,[(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="o">.</span><span class="n">ToBinary</span><span class="p">(),)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">nms</span><span class="p">,</span><span class="n">ms</span><span class="p">)])</span>
<span class="n">conn</span><span class="o">.</span><span class="n">commit</span><span class="p">()</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" that took {t2-t1 :.2f} seconds"</span><span class="p">)</span>
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<pre> that took 4.47 seconds
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<h2 id="Retrieving-binary-molecules-from-the-database">Retrieving binary molecules from the database<a class="anchor-link" href="#Retrieving-binary-molecules-from-the-database">¶</a></h2><p>What about going the other way: we have binary molecules in the database and want to pull them back into Python to work with them?</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">conn</span> <span class="o">=</span> <span class="n">psycopg2</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s2">"dbname=demodb host=localhost"</span><span class="p">)</span>
<span class="n">curs</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'select chembl_id,mol_to_pkl(m) from mols'</span><span class="p">)</span>
<span class="n">tms</span> <span class="o">=</span> <span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">Mol</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">tobytes</span><span class="p">())</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">curs</span><span class="o">.</span><span class="n">fetchall</span><span class="p">()]</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" that took {t2-t1 :.2f} seconds"</span><span class="p">)</span>
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<pre> that took 2.24 seconds
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<p>We can, of course, do searches in the database and then pull just the molecules from the search results into Python:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">conn</span> <span class="o">=</span> <span class="n">psycopg2</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s2">"dbname=demodb host=localhost"</span><span class="p">)</span>
<span class="n">curs</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span>
<span class="n">t1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'select chembl_id,mol_to_pkl(m) from mols where m@>mol_from_smarts(</span><span class="si">%s</span><span class="s1">)'</span><span class="p">,(</span><span class="s1">'c1ncn[o,n]1'</span><span class="p">,))</span>
<span class="n">tms</span> <span class="o">=</span> <span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">Mol</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">tobytes</span><span class="p">())</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">curs</span><span class="o">.</span><span class="n">fetchall</span><span class="p">()]</span>
<span class="n">t2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
<span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">" that took {t2-t1 :.2f} seconds and returned {len(tms)} results"</span><span class="p">)</span>
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<pre> that took 0.55 seconds and returned 993 results
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<h2 id="Example-1:-adding-descriptors-to-the-database">Example 1: adding descriptors to the database<a class="anchor-link" href="#Example-1:-adding-descriptors-to-the-database">¶</a></h2><p>The cartridge can calculate a <a href="https://rdkit.org/docs/Cartridge.html#descriptors">number of molecular descriptors</a> directly, but there are more available in Python. Let's calculate some of those and add them to the database.</p>
<p>We'll pull the binary molecules from the database, calculate BCUT2D descriptors for them, and then add the descriptors to a new database table.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">Descriptors</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">rdMolDescriptors</span>
<span class="n">nBCuts</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">BCUT2D</span><span class="p">(</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'c1ccccc1'</span><span class="p">)))</span>
<span class="n">descrdefn</span> <span class="o">=</span> <span class="s1">','</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="sa">f</span><span class="s1">'bcut_{i+1} float'</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">nBCuts</span><span class="p">))</span>
<span class="n">descrholder</span> <span class="o">=</span> <span class="s1">','</span><span class="o">.</span><span class="n">join</span><span class="p">([</span><span class="s1">'</span><span class="si">%s</span><span class="s1">'</span><span class="p">]</span><span class="o">*</span><span class="n">nBCuts</span><span class="p">)</span>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">conn</span> <span class="o">=</span> <span class="n">psycopg2</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s2">"dbname=demodb host=localhost"</span><span class="p">)</span>
<span class="n">curs</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span>
<span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'drop table if exists bcuts'</span><span class="p">)</span>
<span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="sa">f</span><span class="s1">'create table bcuts (chembl_id text,</span><span class="si">{descrdefn}</span><span class="s1">)'</span><span class="p">)</span>
<span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'select chembl_id,mol_to_pkl(m) from mols'</span><span class="p">)</span>
<span class="n">bcut_data</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">row</span> <span class="ow">in</span> <span class="n">curs</span><span class="o">.</span><span class="n">fetchall</span><span class="p">():</span>
<span class="n">trow</span> <span class="o">=</span> <span class="p">[</span><span class="n">row</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
<span class="n">mol</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">Mol</span><span class="p">(</span><span class="n">row</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">tobytes</span><span class="p">())</span>
<span class="k">try</span><span class="p">:</span>
<span class="n">descrs</span> <span class="o">=</span> <span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">BCUT2D</span><span class="p">(</span><span class="n">mol</span><span class="p">)</span>
<span class="k">except</span> <span class="ne">ValueError</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">trow</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">descrs</span><span class="p">)</span>
<span class="n">bcut_data</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">trow</span><span class="p">)</span>
<span class="n">cmd</span> <span class="o">=</span> <span class="sa">f</span><span class="s1">'insert into bcuts values (</span><span class="si">%s</span><span class="s1">,</span><span class="si">{descrholder}</span><span class="s1">)'</span>
<span class="n">curs</span><span class="o">.</span><span class="n">executemany</span><span class="p">(</span><span class="n">cmd</span><span class="p">,</span><span class="n">bcut_data</span><span class="p">)</span>
<span class="n">conn</span><span class="o">.</span><span class="n">commit</span><span class="p">()</span>
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<p>Since the bcuts descriptors use partial charges and we don't have parameters for all atom types, some molecules don't have values:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'select count(*) from bcuts'</span><span class="p">)</span>
<span class="n">curs</span><span class="o">.</span><span class="n">fetchone</span><span class="p">()</span>
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<pre>(29937,)</pre>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'select count(*) from mols'</span><span class="p">)</span>
<span class="n">curs</span><span class="o">.</span><span class="n">fetchone</span><span class="p">()</span>
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<pre>(30000,)</pre>
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<h2 id="Example-2:-Loading-SDF-data-into-the-database">Example 2: Loading SDF data into the database<a class="anchor-link" href="#Example-2:-Loading-SDF-data-into-the-database">¶</a></h2><p>We loaded the molecules from an SDF, but ignored the data fields in that SDF. Now let's load those into the database too.</p>
<p>We'll take advantage of PostgreSQL's jsonb type to store the properties on each molecule in a dictionary-like object.</p>
<p>Let's start by loading the data:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">conn</span> <span class="o">=</span> <span class="n">psycopg2</span><span class="o">.</span><span class="n">connect</span><span class="p">(</span><span class="s2">"dbname=demodb host=localhost"</span><span class="p">)</span>
<span class="n">curs</span> <span class="o">=</span> <span class="n">conn</span><span class="o">.</span><span class="n">cursor</span><span class="p">()</span>
<span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'drop table if exists mols'</span><span class="p">)</span>
<span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s1">'create table mols (chembl_id text,m mol,sdf_data jsonb)'</span><span class="p">)</span>
<span class="n">rows</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">with</span> <span class="n">gzip</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="s1">'../data/chembl26_very_active.sdf.gz'</span><span class="p">,</span><span class="s1">'r'</span><span class="p">)</span> <span class="k">as</span> <span class="n">inf</span><span class="p">:</span>
<span class="n">suppl</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">ForwardSDMolSupplier</span><span class="p">(</span><span class="n">inf</span><span class="p">)</span>
<span class="k">while</span> <span class="nb">len</span><span class="p">(</span><span class="n">rows</span><span class="p">)</span><span class="o"><</span><span class="mi">30000</span><span class="p">:</span>
<span class="n">m</span> <span class="o">=</span> <span class="nb">next</span><span class="p">(</span><span class="n">suppl</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">m</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">nm</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">GetProp</span><span class="p">(</span><span class="s1">'compound_chembl_id'</span><span class="p">)</span>
<span class="n">props</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">GetPropsAsDict</span><span class="p">()</span>
<span class="n">rows</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">nm</span><span class="p">,</span><span class="n">m</span><span class="o">.</span><span class="n">ToBinary</span><span class="p">(),</span><span class="n">json</span><span class="o">.</span><span class="n">dumps</span><span class="p">(</span><span class="n">props</span><span class="p">)))</span>
<span class="n">curs</span><span class="o">.</span><span class="n">executemany</span><span class="p">(</span><span class="s1">'insert into mols values (</span><span class="si">%s</span><span class="s1">,mol_from_pkl(</span><span class="si">%s</span><span class="s1">),</span><span class="si">%s</span><span class="s1">)'</span><span class="p">,</span><span class="n">rows</span><span class="p">)</span>
<span class="n">conn</span><span class="o">.</span><span class="n">commit</span><span class="p">()</span>
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<p>Demonstrate how to do a string query:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s2">"select count(*) from mols where sdf_data->>'pref_name' = 'Human immunodeficiency virus type 1 protease'"</span><span class="p">)</span>
<span class="n">curs</span><span class="o">.</span><span class="n">fetchone</span><span class="p">()</span>
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<pre>(892,)</pre>
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<p>And a query on a floating point value, here we're counting the number of rows where the Ki value is less than 10 picomolar.</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s2">"select count(*) from mols where (sdf_data->>'standard_value')::float < 0.01"</span><span class="p">)</span>
<span class="n">curs</span><span class="o">.</span><span class="n">fetchone</span><span class="p">()</span>
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<pre>(788,)</pre>
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<p>Get all the rows with measurements aginst HIV protease where Ki < 1 picomolar</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s2">"select chembl_id,m from mols where sdf_data->>'pref_name' = 'Human immunodeficiency virus type 1 protease'</span><span class="se">\</span>
<span class="s2">and (sdf_data->>'standard_value')::float < 0.01"</span><span class="p">)</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">curs</span><span class="o">.</span><span class="n">fetchall</span><span class="p">()</span>
<span class="n">d</span><span class="p">[:</span><span class="mi">2</span><span class="p">]</span>
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<pre>[('CHEMBL443030',
'COc1cc(CN2C(=O)N(Cc3ccc(O)c(OC)c3)N(Cc3ccccc3)C[C@@H](O)[C@H]2Cc2ccccc2)ccc1O |(3.8692,2.234,;3.0465,2.2956,;2.5817,1.614,;1.759,1.6756,;1.2943,0.994,;0.4716,1.0556,;0.0069,0.374,;-0.8089,0.4969,;-1.0521,1.2853,;-1.4137,-0.0642,;-2.1817,0.2372,;-2.3046,1.053,;-3.0726,1.3544,;-3.1955,2.1702,;-2.5505,2.6846,;-2.6735,3.5004,;-1.7826,2.3832,;-1.1376,2.8975,;-0.3696,2.5961,;-1.6596,1.5674,;-1.352,-0.8869,;-2.0665,-1.2994,;-2.0665,-2.1244,;-2.781,-2.5369,;-2.781,-3.3619,;-2.0665,-3.7744,;-1.352,-3.3619,;-1.352,-2.5369,;-0.6704,-1.3516,;0.118,-1.1085,;0.6791,-1.7132,;0.4194,-0.3405,;1.2421,-0.2788,;1.7068,-0.9605,;2.5295,-0.8988,;2.9942,-1.5805,;2.6363,-2.3238,;1.8136,-2.3854,;1.3488,-1.7038,;1.6523,0.2507,;2.4749,0.189,;2.9397,0.8707,;3.7624,0.809,)|'),
('CHEMBL177470',
'CC(C)(CCCN)CN(C[C@@H](O)[C@H](Cc1ccccc1)NC(=O)O[C@H]1CO[C@H]2OCC[C@@H]12)S(=O)(=O)c1ccc2c(c1)OCO2 |(8.05,-8.8625,;8.0542,-8.0375,;7.4667,-8.6167,;8.775,-7.6292,;9.4875,-8.0417,;10.2,-7.6292,;10.9125,-8.0417,;7.35,-7.6167,;7.3542,-6.7917,;6.6375,-6.3875,;5.9292,-6.8042,;5.9292,-7.6292,;5.2125,-6.3917,;5.2042,-5.5667,;5.9167,-5.1542,;5.9167,-4.3292,;6.625,-3.9167,;7.3417,-4.3167,;7.35,-5.15,;6.6375,-5.5667,;4.5,-6.8125,;3.7792,-6.4042,;3.775,-5.5792,;3.0667,-6.8167,;2.3542,-6.4042,;2.3625,-5.6875,;1.1167,-5.6792,;1.1167,-6.4,;0.4792,-7.475,;1.1042,-7.8417,;1.7375,-7.4875,;1.7417,-6.7667,;8.0667,-6.3792,;8.7792,-6.7792,;8.0542,-5.5542,;8.8625,-6.1625,;9.4417,-6.7417,;10.2417,-6.5292,;10.4542,-5.7292,;9.8625,-5.1417,;9.0667,-5.3667,;10.2292,-4.4042,;11.0542,-4.5292,;11.1917,-5.35,)|')]</pre>
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<p>Notice that we get CXSMILES with coordinates back from the database. We loaded the compounds from the SDF with coordinates and the CXSMILES coming back from the database includes those coordinates.</p>
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<p>Let's do one last query there to look at the composition of the dataset:</p>
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<div class="highlight hl-ipython3"><pre><span></span><span class="n">curs</span><span class="o">.</span><span class="n">execute</span><span class="p">(</span><span class="s2">"select sdf_data->>'pref_name',count(distinct(chembl_id)) cnt </span><span class="se">\</span>
<span class="s2">from mols group by (sdf_data->>'pref_name') order by cnt desc limit 20"</span><span class="p">)</span>
<span class="n">curs</span><span class="o">.</span><span class="n">fetchall</span><span class="p">()</span>
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<pre>[('Unchecked', 1398),
('Mu opioid receptor', 1112),
('Kappa opioid receptor', 712),
('Delta opioid receptor', 605),
('Human immunodeficiency virus type 1 protease', 588),
('Coagulation factor X', 558),
('Serotonin 1a (5-HT1a) receptor', 528),
('Histamine H3 receptor', 519),
('Neuronal acetylcholine receptor; alpha4/beta2', 460),
('Dopamine D3 receptor', 428),
('Thrombin', 386),
('Serotonin transporter', 366),
('Alpha-1a adrenergic receptor', 357),
('Serotonin 2a (5-HT2a) receptor', 348),
('Dopamine D2 receptor', 326),
('Apoptosis regulator Bcl-2', 259),
('Protease', 254),
('Serine/threonine-protein kinase PIM1', 252),
('TNF-alpha', 245),
('Serotonin 6 (5-HT6) receptor', 243)]</pre>
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<p>I think using JSONB this way, which basically lets us combine a relational database with a document store, opens up a bunch of interesting possibilties. I'll try and do another blog post on that in the near(ish) future.</p>
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</pre>greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com0tag:blogger.com,1999:blog-684443317148892945.post-88902960320923935302020-05-03T20:34:00.000-07:002020-05-03T20:36:30.524-07:00New Drawing Options - addendum<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>After I did the <a href="http://rdkit.blogspot.com/2020/04/new-drawing-options-in-202003-release.html">last blog post</a> about the new drawing options and Pen did his <a href="https://iwatobipen.wordpress.com/2020/05/01/draw-molecules-as-svg-in-horizontal-layout-drawing-rdkit-memo/">followup post</a>, Ed Griffen at MedChemica sent along another nice example. This one is a more attractive version of the "highlight the ring systems" example I did to show how to use multiple highlights.</p>
<p>You can read the other posts for more info, here's the example:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">Chem</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">Draw</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">rdMolDraw2D</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">rdDepictor</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">SetPreferCoordGen</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">IPythonConsole</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">SVG</span>
<span class="kn">import</span> <span class="nn">rdkit</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rdkit</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
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<pre>2020.09.1dev1
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="n">polycyclic</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'CN1C[C@@H](C=C2[C@H]1CC3=CNC4=CC=CC2=C34)C(=O)O'</span><span class="p">)</span>
<span class="n">rings</span> <span class="o">=</span> <span class="n">polycyclic</span><span class="o">.</span><span class="n">GetRingInfo</span><span class="p">()</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.0</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">),</span> <span class="p">(</span><span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">),</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mf">0.8</span><span class="p">)]</span>
<span class="n">athighlights</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="n">arads</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">rng</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">rings</span><span class="o">.</span><span class="n">AtomRings</span><span class="p">()):</span>
<span class="k">for</span> <span class="n">aid</span> <span class="ow">in</span> <span class="n">rng</span><span class="p">:</span>
<span class="n">athighlights</span><span class="p">[</span><span class="n">aid</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">arads</span><span class="p">[</span><span class="n">aid</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.3</span>
<span class="n">bndhighlights</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">rng</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">rings</span><span class="o">.</span><span class="n">BondRings</span><span class="p">()):</span>
<span class="k">for</span> <span class="n">bid</span> <span class="ow">in</span> <span class="n">rng</span><span class="p">:</span>
<span class="n">bndhighlights</span><span class="p">[</span><span class="n">bid</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">400</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span>
<span class="n">dos</span> <span class="o">=</span> <span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span>
<span class="n">dos</span><span class="o">.</span><span class="n">atomHighlightsAreCircles</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">dos</span><span class="o">.</span><span class="n">fillHighlights</span><span class="o">=</span><span class="kc">False</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMoleculeWithHighlights</span><span class="p">(</span><span class="n">polycyclic</span><span class="p">,</span> <span class="s1">'lysergic acid'</span><span class="p">,</span> <span class="nb">dict</span><span class="p">(</span><span class="n">athighlights</span><span class="p">),</span> <span class="nb">dict</span><span class="p">(</span><span class="n">bndhighlights</span><span class="p">),</span> <span class="n">arads</span><span class="p">,</span> <span class="p">{})</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<svg baseProfile="full" height="400px" version="1.1" viewBox="0 0 400 400" width="400px" xml:space="preserve" xmlns="http://www.w3.org/2000/svg" xmlns:rdkit="http://www.rdkit.org/xml" xmlns:xlink="http://www.w3.org/1999/xlink">
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<path d="M 251.615,302.138 L 227.059,316.383" style="fill:none;fill-rule:evenodd;stroke:#CC00CC;stroke-width:8px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path d="M 206.429,296.242 L 178.934,280.436" style="fill:none;fill-rule:evenodd;stroke:#CC00CC;stroke-width:8px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path d="M 194.815,316.447 L 167.319,300.641" style="fill:none;fill-rule:evenodd;stroke:#CC00CC;stroke-width:8px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path d="M 178.839,233.251 L 206.271,217.337" style="fill:none;fill-rule:evenodd;stroke:#CC00CC;stroke-width:8px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path d="M 167.145,213.092 L 194.576,197.179" style="fill:none;fill-rule:evenodd;stroke:#CC00CC;stroke-width:8px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<text dominant-baseline="central" style="font-size:12px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="200" y="377.8"><tspan>lysergic acid</tspan></text>
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<p>Thanks Ed!</p>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com0tag:blogger.com,1999:blog-684443317148892945.post-25327628948047136752020-04-27T02:49:00.004-07:002020-04-27T03:01:01.564-07:00Possible Google Season of Docs ProjectsWe're planning to apply to be a mentoring organization for <a href="https://developers.google.com/season-of-docs">Google Season of Docs</a> this year.<br />
<br />
The primary instigator/organizer of this is Vincent Scalfani. For people who may not have "met" him yet, Vin is the one who wrote the fantastic new version of the <a href="https://rdkit.org/docs/Cookbook.html">RDKit Cookbook</a> and has made a number of other contributions to improve the RDKit documentation.<br />
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Here's Vin's anouncement about our planned participation:<br />
<a href="https://www.mail-archive.com/rdkit-discuss@lists.sourceforge.net/msg09805.html">https://www.mail-archive.com/rdkit-discuss@lists.sourceforge.net/msg09805.html</a><br />
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And here are the ideas we're currently thinking of. I will try and keep this post up to date as our thinking evolves. If you have other ideas or suggestions, please get in touch!<br />
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<b><span style="background-color: transparent; color: black; font-family: "arial"; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Project Name:</span><span style="background-color: transparent; color: black; font-family: "arial"; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"> Expansion of the RDKit Book to Outline Additional Methods Implemented</span></b></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><br /><br /><i>Description</i>: The RDKit Book serves to describe and outline supported features available within RDKit [1]. The Book offers a high level overview of supported features for users. Some examples include supported molecular file formats, molecular descriptor calculation methods, and chemical reaction support. There are many supported methods that are not yet documented in the RDKit Book such as additional file format reading support, available molecular descriptor/fingerprint calculations (e.g., Morse atom fingerprints, Coulomb Matrices, QED descriptor), and chemical validation/standardization (MolVS) integration. This project would inventory the available features mentioned in the RDKit Release notes [2] that are not yet described in the RDKit Book. These additional features would then be added to the RDKit book with a description, example, and links to the original GitHub pull request that added the feature. Moreover, related scientific literature references and a link to the API docs describing the module can also be added. Further, it would be useful to update existing feature methods currently in the RDKit Book with links to the GitHub code, related literature, and API docs, where possible.<br /></span>
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<span style="background-color: transparent; color: black; font-family: "arial"; font-style: italic; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre;">Related Material:</span><span style="background-color: transparent; color: black; font-family: "arial"; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre;"> </span></div>
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<span style="background-color: transparent; color: black; font-family: "arial"; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre;">[1] RDKit Book: </span><a href="https://www.rdkit.org/docs/RDKit_Book.html" style="text-decoration: none;"><span style="background-color: transparent; color: #1155cc; font-family: "arial"; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: underline; vertical-align: baseline; white-space: pre;">https://www.rdkit.org/docs/RDKit_Book.html</span></a></div>
<span style="font-family: "arial"; vertical-align: baseline; white-space: pre-wrap;">[2] RDKit Release Notes: </span><span style="color: #1155cc; font-family: "arial"; vertical-align: baseline; white-space: pre-wrap;"><a href="https://github.com/rdkit/rdkit/releases" style="text-decoration-line: none;">https://github.com/rdkit/rdkit/releases</a></span><br />
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<b><span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: transparent; color: black; font-style: italic; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">Project Name: </span><span style="background-color: transparent; color: black; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">A Guide for Reading and Making the Most of the RDKit Python API Docs</span></span></b></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><i>Description</i>: The RDKit Python API Documentation serves as the comprehensive reference guideto the available Python modules for accessing RDKit functionality with Python [1]. This reference work can be intimidating and confusing to new users. A highly useful contribution to the RDKit documentation would be to create a guide on how to read and use the RDKit Python API documentation. This could include: </span><ol>
<li><span style="font-family: Arial, Helvetica, sans-serif;">A summary overview of the different packages/subpackages along with an explanation of the syntax and instructions for importing the appropriate function in a Python script (see the SciPy API docs as an example [2])</span></li>
<li><span style="font-family: Arial, Helvetica, sans-serif;">A graphical depiction of the API structure</span></li>
<li><span style="font-family: Arial, Helvetica, sans-serif;">A worked example of using a particular module and demonstrating how to import the module, use a particular class, and specify options</span></li>
<li><span style="font-family: Arial, Helvetica, sans-serif;">An explanation of what the C++ signatures mean and why these can be useful within the context of the Python API. </span></li>
<li><span style="font-family: Arial, Helvetica, sans-serif;">Addition of links to the source code (see for example the Deepchem API docs [3])</span></li>
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<span style="background-color: transparent; color: black; font-family: Arial, Helvetica, sans-serif; font-style: italic; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre;">Related Material: </span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: transparent; color: black; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre;">[1] RDKit Python API Reference: </span><a href="https://www.rdkit.org/docs/api-docs.html" style="text-decoration: none;"><span style="background-color: transparent; color: #1155cc; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: underline; vertical-align: baseline; white-space: pre;">https://www.rdkit.org/docs/api-docs.html</span></a></span></div>
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<span style="font-family: Arial, Helvetica, sans-serif;"><span style="background-color: transparent; color: black; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre;">[2] SciPy API Docs: (</span><a href="https://docs.scipy.org/doc/scipy/reference/api.html#api-definition" style="text-decoration: none;"><span style="background-color: transparent; color: #1155cc; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: underline; vertical-align: baseline; white-space: pre;">https://docs.scipy.org/doc/scipy/reference/api.html#api-definition</span></a><span style="background-color: transparent; color: black; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre;">)</span></span></div>
<span style="font-family: Arial, Helvetica, sans-serif;"><span style="color: black; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre;">[3] DeepChem API Docs: </span><a href="https://www.deepchem.io/docs/deepchem.html" style="text-decoration-line: none;"><span style="color: #1155cc; font-variant-east-asian: normal; font-variant-numeric: normal; text-decoration-line: underline; vertical-align: baseline; white-space: pre;">https://www.deepchem.io/docs/deepchem.html</span></a><span style="color: black; font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline; white-space: pre;"> </span></span><br />
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<span style="background-color: transparent; color: black; font-family: Arial, Helvetica, sans-serif; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre;"><br /></span></div>
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<span style="background-color: transparent; color: black; font-family: Arial, Helvetica, sans-serif; font-style: normal; font-variant: normal; font-weight: 400; text-decoration: none; vertical-align: baseline; white-space: pre;"><br /></span></div>
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<span style="background-color: transparent; color: black; font-family: Arial, Helvetica, sans-serif; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"></span><br />
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<span style="background-color: transparent; color: black; font-family: Arial, Helvetica, sans-serif; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><b><span style="font-style: italic; vertical-align: baseline;">Project Name: </span><span style="vertical-align: baseline;">RDKit and Pandas Book</span></b></span><br />
<span style="background-color: transparent; color: black; font-family: Arial, Helvetica, sans-serif; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;"><b><span style="vertical-align: baseline;"><br /></span></b></span></div>
<span style="background-color: transparent; color: black; font-family: Arial, Helvetica, sans-serif; font-size: 11pt; font-style: normal; font-variant: normal; text-decoration: none; vertical-align: baseline; white-space: pre-wrap;">
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<span style="font-style: italic; vertical-align: baseline;">Description:</span><span style="vertical-align: baseline;"> RDKit integrates with the Python Pandas data analysis and data structures tools [1]. This integration allows users to store and analyze molecules in convenient data frames [2]. There is not yet a detailed formal documentation guide to using RDKit with Pandas. However, there are individual code examples scattered throughout Stack Overflow [3], the RDKit mailing lists [4], and GitHub [5]. It would be useful to compile and harmonize these examples into an in-depth RDKit and Pandas Documentation Book. Similarly to other RDKit Documentation, the RDKit and Pandas Book would be written in reStructuredText with Sphinx doctest to allow testing of code snippets, where possible.</span></div>
</span><br /><span style="font-family: Arial, Helvetica, sans-serif;"><span style="text-decoration-color: initial; text-decoration-style: initial;"><i>Related Material: </i></span><br /><span style="text-decoration-color: initial; text-decoration-style: initial;">[1] </span><a href="https://pandas.pydata.org/" style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">https://pandas.pydata.org/</a><br /><span style="text-decoration-color: initial; text-decoration-style: initial;">[2] </span><a href="https://www.rdkit.org/docs/source/rdkit.Chem.PandasTools.html" style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">https://www.rdkit.org/docs/source/rdkit.Chem.PandasTools.html</a><span style="text-decoration-color: initial; text-decoration-style: initial;">; </span><br /><span style="text-decoration-color: initial; text-decoration-style: initial;">[3] </span><a href="https://stackoverflow.com/search?q=rdkit+pandas" style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">https://stackoverflow.com/search?q=rdkit+pandas</a><span style="text-decoration-color: initial; text-decoration-style: initial;">; </span><br /><span style="text-decoration-color: initial; text-decoration-style: initial;">[4] </span><a href="https://www.mail-archive.com/search?q=pandas&l=rdkit-discuss%40lists.sourceforge.net" style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">https://www.mail-archive.com/search?q=pandas&l=rdkit-discuss%40lists.sourceforge.net</a><span style="text-decoration-color: initial; text-decoration-style: initial;">; </span><br /><span style="text-decoration-color: initial; text-decoration-style: initial;">[5] </span><a href="https://github.com/rdkit/rdkit-tutorials/blob/master/notebooks/004_RDKit_pandas_support.ipynb" style="font-variant-east-asian: normal; font-variant-numeric: normal; vertical-align: baseline;">https://github.com/rdkit/rdkit-tutorials/blob/master/notebooks/004_RDKit_pandas_support.ipynb</a></span></div>
greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com0tag:blogger.com,1999:blog-684443317148892945.post-54160199584027268292020-04-26T19:57:00.000-07:002020-04-26T19:57:04.500-07:00New drawing options in the 2020.03 release<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>The 2020.03 release includes a set of significant improvements to the RDKit molecule drawing code. If you're interested in the history, the original issue <a href="https://github.com/rdkit/rdkit/issues/2931">is here</a>.</p>
<p>The work for this was done by Dave Cosgrove and it was funded by Medchemica (the changes tracked in that github issue), and T5 Informatics (atom and bond annotations). Many thanks to Al and Ed at Medchemica for the funding and to Dave for doing the work!</p>
<p>As an aside to people in companies that are using the RDKit: this is a good model for how to get specific improvements/additions made to the RDKit. You may not have developers working in your organization, but you can fund a third party to do the work. This approach is most effective when there's some communication with the RDKit development team before starting (that's what Dave did with the ticket above).</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">Chem</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">Draw</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">rdMolDraw2D</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">rdDepictor</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">SetPreferCoordGen</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">IPythonConsole</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="kn">import</span> <span class="n">SVG</span>
<span class="kn">import</span> <span class="nn">rdkit</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rdkit</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
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<pre>2020.03.1
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<p>Let's start by drawing a molecule using a <code>MolDraw2DSVG()</code> object:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">diclofenac</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'O=C(O)Cc1ccccc1Nc1c(Cl)cccc1Cl'</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">350</span><span class="p">,</span><span class="mi">300</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecule</span><span class="p">(</span><span class="n">diclofenac</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<h2 id="Atom-and-bond-indices">Atom and bond indices<a class="anchor-link" href="#Atom-and-bond-indices">¶</a></h2><p>We can easily add atom indices to the drawing:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">350</span><span class="p">,</span><span class="mi">300</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span><span class="o">.</span><span class="n">addAtomIndices</span><span class="o">=</span><span class="kc">True</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecule</span><span class="p">(</span><span class="n">diclofenac</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<text dominant-baseline="central" style="font-size:21px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#00CC00" text-anchor="end" x="203.12" y="254.637"><tspan>Cl</tspan></text>
<text dominant-baseline="central" style="font-size:21px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#00CC00" text-anchor="start" x="257.06" y="126.45"><tspan>Cl</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="82.026" y="40.3849"><tspan>0</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="63.5754" y="93.7855"><tspan>1</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="45.1238" y="147.183"><tspan>2</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="119.089" y="136.446"><tspan>3</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="137.548" y="93.7257"><tspan>4</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="146.764" y="56.3489"><tspan>5</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="192.976" y="29.6136"><tspan>6</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="239.23" y="56.2743"><tspan>7</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="239.273" y="109.661"><tspan>8</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="211.554" y="136.372"><tspan>9</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="193.103" y="189.773"><tspan>10</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="230.074" y="168.393"><tspan>11</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="211.622" y="221.793"><tspan>12</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="193.171" y="275.191"><tspan>13</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="267.136" y="264.463"><tspan>14</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="322.599" y="243.058"><tspan>15</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="322.548" y="178.996"><tspan>16</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="285.545" y="157.667"><tspan>17</tspan></text>
<text dominant-baseline="central" style="font-size:16px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="267.008" y="104.295"><tspan>18</tspan></text>
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<p>or bond indices:</p>
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<div class="prompt input_prompt">In [5]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">350</span><span class="p">,</span><span class="mi">300</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span><span class="o">.</span><span class="n">addBondIndices</span><span class="o">=</span><span class="kc">True</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecule</span><span class="p">(</span><span class="n">diclofenac</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<div class="prompt output_prompt">Out[5]:</div>
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<rect height="300" style="opacity:1.0;fill:#FFFFFF;stroke:none" width="350" x="0" y="0"> </rect>
<path class="bond-0" d="M 77.3333,76.4291 L 77.3459,92.2202" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 77.3459,92.2202 L 77.3585,108.011" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 84.913,76.423 L 84.9256,92.2142" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 84.9256,92.2142 L 84.9383,108.005" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 81.1484,108.008 L 67.8395,115.705" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 67.8395,115.705 L 54.5306,123.402" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-2" d="M 81.1484,108.008 L 117.632,129.029" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 117.632,129.029 L 154.082,107.949" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 154.082,107.949 L 154.049,65.8397" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 161.657,101.627 L 161.633,72.1501" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-18" d="M 154.082,107.949 L 190.566,128.97" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 154.049,65.8397 L 190.499,44.7512" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 190.499,44.7512 L 226.982,65.7808" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 192.186,54.4726 L 217.725,69.1933" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-7" d="M 226.982,65.7808 L 227.016,107.89" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 227.016,107.89 L 190.566,128.97" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 217.754,104.491 L 192.239,119.247" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 190.566,128.97 L 190.579,144.762" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 190.579,144.762 L 190.591,160.553" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 199.724,176.34 L 213.404,184.225" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 213.404,184.225 L 227.083,192.11" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-11" d="M 227.083,192.11 L 227.117,234.219" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-11" d="M 234.668,198.42 L 234.692,227.897" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-19" d="M 227.083,192.11 L 263.534,171.021" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 227.117,234.219 L 214.857,241.309" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 214.857,241.309 L 202.597,248.4" style="fill:none;fill-rule:evenodd;stroke:#00CC00;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-13" d="M 227.117,234.219 L 263.601,255.249" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-14" d="M 263.601,255.249 L 300.051,234.16" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-14" d="M 265.273,245.525 L 290.788,230.763" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-15" d="M 300.051,234.16 L 300.017,192.051" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-16" d="M 300.017,192.051 L 263.534,171.021" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-16" d="M 290.76,195.463 L 265.221,180.742" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-17" d="M 263.534,171.021 L 263.521,155.23" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-17" d="M 263.521,155.23 L 263.508,139.439" style="fill:none;fill-rule:evenodd;stroke:#00CC00;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text dominant-baseline="central" style="font-size:21px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="73.3879" y="69.0569"><tspan>O</tspan></text>
<text dominant-baseline="central" style="font-size:21px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="end" x="52.4252" y="132.247"><tspan>HO</tspan></text>
<text dominant-baseline="central" style="font-size:21px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="197.619" y="174.238"><tspan>HN</tspan></text>
<text dominant-baseline="central" style="font-size:21px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#00CC00" text-anchor="end" x="200.492" y="258.458"><tspan>Cl</tspan></text>
<text dominant-baseline="central" style="font-size:21px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#00CC00" text-anchor="start" x="253.675" y="132.07"><tspan>Cl</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="62.1822" y="89.3373"><tspan>0</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="72.41" y="137.321"><tspan>1</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="89.9301" y="137.306"><tspan>2</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="145.344" y="137.262"><tspan>3</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="141.433" y="89.2733"><tspan>4</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="181.763" y="74.0662"><tspan>5</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="218.204" y="41.2174"><tspan>6</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="214.366" y="89.2143"><tspan>7</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="218.278" y="137.203"><tspan>8</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="177.944" y="145.245"><tspan>9</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="199.379" y="200.381"><tspan>10</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="252.366" y="215.513"><tspan>11</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="199.405" y="230.724"><tspan>12</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="235.896" y="263.52"><tspan>13</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="291.316" y="263.475"><tspan>14</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="318.984" y="215.459"><tspan>15</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="291.239" y="167.487"><tspan>16</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="244.573" y="159.509"><tspan>17</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="181.784" y="104.41"><tspan>18</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="242.145" y="178.467"><tspan>19</tspan></text>
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<p>It's also possible to show both atom and bond indices, but this tends to be a bit crowded.</p>
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<h2 id="Atom-and-bond-annotations">Atom and bond annotations<a class="anchor-link" href="#Atom-and-bond-annotations">¶</a></h2>
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<p>It's also possible to add user-provided annotations to either atoms or bonds by setting atom/bond properties:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cp</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">Mol</span><span class="p">(</span><span class="n">diclofenac</span><span class="p">)</span>
<span class="n">cp</span><span class="o">.</span><span class="n">GetAtomWithIdx</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">SetProp</span><span class="p">(</span><span class="s2">"atomNote"</span><span class="p">,</span><span class="s2">"pKa=4.0"</span><span class="p">)</span>
<span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">350</span><span class="p">,</span><span class="mi">300</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span><span class="o">.</span><span class="n">setHighlightColour</span><span class="p">((</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.8</span><span class="p">))</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecule</span><span class="p">(</span><span class="n">cp</span><span class="p">,</span><span class="n">highlightAtoms</span><span class="o">=</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
</pre></div>
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<div class="prompt output_prompt">Out[6]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">cp</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">Mol</span><span class="p">(</span><span class="n">diclofenac</span><span class="p">)</span>
<span class="n">cp</span><span class="o">.</span><span class="n">GetBondWithIdx</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">SetProp</span><span class="p">(</span><span class="s2">"bondNote"</span><span class="p">,</span><span class="s2">"note"</span><span class="p">)</span>
<span class="n">cp</span><span class="o">.</span><span class="n">GetBondWithIdx</span><span class="p">(</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">SetProp</span><span class="p">(</span><span class="s2">"bondNote"</span><span class="p">,</span><span class="s2">"note 2"</span><span class="p">)</span>
<span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">350</span><span class="p">,</span><span class="mi">300</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span><span class="o">.</span><span class="n">setHighlightColour</span><span class="p">((</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.8</span><span class="p">))</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecule</span><span class="p">(</span><span class="n">cp</span><span class="p">,</span><span class="n">highlightAtoms</span><span class="o">=</span><span class="p">[],</span><span class="n">highlightBonds</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">])</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<div class="prompt output_prompt">Out[7]:</div>
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<svg baseProfile="full" height="300px" version="1.1" viewBox="0 0 350 300" width="350px" xml:space="preserve" xmlns="http://www.w3.org/2000/svg" xmlns:rdkit="http://www.rdkit.org/xml" xmlns:xlink="http://www.w3.org/1999/xlink">
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<rect height="300" style="opacity:1.0;fill:#FFFFFF;stroke:none" width="350" x="0" y="0"> </rect>
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<path class="bond-4" d="M 179.117,89.9092 L 179.091,56.9012" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-7" d="M 252.269,49.7688 L 252.306,96.9231" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-17" d="M 293.185,149.934 L 293.17,132.251" style="fill:none;fill-rule:evenodd;stroke:#00CC00;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text dominant-baseline="central" style="font-size:23px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="80.2738" y="53.4374"><tspan>O</tspan></text>
<text dominant-baseline="central" style="font-size:23px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="end" x="56.7997" y="124.197"><tspan>HO</tspan></text>
<text dominant-baseline="central" style="font-size:23px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="219.387" y="171.219"><tspan>HN</tspan></text>
<text dominant-baseline="central" style="font-size:23px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#00CC00" text-anchor="end" x="222.605" y="265.528"><tspan>Cl</tspan></text>
<text dominant-baseline="central" style="font-size:23px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#00CC00" text-anchor="start" x="282.159" y="123.999"><tspan>Cl</tspan></text>
<text dominant-baseline="central" style="font-size:17px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="53.5855" y="83.7034"><tspan>note</tspan></text>
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<h2 id="Stereochemistry">Stereochemistry<a class="anchor-link" href="#Stereochemistry">¶</a></h2><p>There's a convenience option to show stereochemistry labels.</p>
<p>Let's start with the two enantiomers of threonine:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">l_threonine</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'C[C@@H](O)[C@H](N)C(O)=O'</span><span class="p">)</span>
<span class="n">d_threonine</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'C[C@H](O)[C@@H](N)C(O)=O'</span><span class="p">)</span>
<span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">600</span><span class="p">,</span><span class="mi">280</span><span class="p">,</span><span class="mi">300</span><span class="p">,</span><span class="mi">280</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecules</span><span class="p">([</span><span class="n">l_threonine</span><span class="p">,</span><span class="n">d_threonine</span><span class="p">])</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<path class="bond-6" d="M 196.423,170.698 L 219.389,157.38" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="85.4876" y="182.624"><tspan>H</tspan><tspan style="baseline-shift:sub;font-size:27px;">2</tspan><tspan>N</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="186.505" y="254.553"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="249.277" y="145.332"><tspan>O</tspan></text>
<path class="bond-0" d="M 430.517,63.1958 L 429.865,64.3311" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<p>It can also be nice to show the absolute stereo labels to the drawing:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">600</span><span class="p">,</span><span class="mi">280</span><span class="p">,</span><span class="mi">300</span><span class="p">,</span><span class="mi">280</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span><span class="o">.</span><span class="n">addStereoAnnotation</span><span class="o">=</span><span class="kc">True</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecules</span><span class="p">([</span><span class="n">l_threonine</span><span class="p">,</span><span class="n">d_threonine</span><span class="p">])</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<path class="bond-6" d="M 196.423,170.698 L 219.389,157.38" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="85.4876" y="182.624"><tspan>H</tspan><tspan style="baseline-shift:sub;font-size:27px;">2</tspan><tspan>N</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="186.505" y="254.553"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="249.277" y="145.332"><tspan>O</tspan></text>
<text dominant-baseline="central" style="font-size:27px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="136.425" y="35.1105"><tspan>(R)</tspan></text>
<text dominant-baseline="central" style="font-size:27px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="105.111" y="126.095"><tspan>(S)</tspan></text>
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<path class="bond-6" d="M 496.423,170.698 L 519.389,157.38" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 519.389,157.38 L 542.356,144.062" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="486.069" y="36.3578"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="385.488" y="182.624"><tspan>H</tspan><tspan style="baseline-shift:sub;font-size:27px;">2</tspan><tspan>N</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="486.505" y="254.553"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="549.277" y="145.332"><tspan>O</tspan></text>
<text dominant-baseline="central" style="font-size:27px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="436.425" y="35.1105"><tspan>(S)</tspan></text>
<text dominant-baseline="central" style="font-size:27px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="405.111" y="126.095"><tspan>(R)</tspan></text>
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<p><strong>Note</strong>: the RDKit code that does CIP assignments (i.e. R/S and E/Z assignment) is not a faithful implementation of the CIP rules, which are <a href="https://pubs.acs.org/doi/abs/10.1021/acs.jcim.8b00324">complicated</a>. The labels are reliable (in the sense that you'll always get the same answer for the same molecule, no matter how it's drawn), but the assignment of atom priorities is only an approximation to the actual CIP rules.</p>
<p>We hope to have a better implementation of true CIP labels in a future release, but definitely take the current labels with a giant grain of salt.</p>
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<p>Now let's look allothreonine, the diasteromers of threonine:</p>
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<div class="prompt input_prompt">In [10]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">l_allothreonine</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'C[C@H](O)[C@H](N)C(O)=O'</span><span class="p">)</span>
<span class="n">d_allothreonine</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'C[C@@H](O)[C@@H](N)C(O)=O'</span><span class="p">)</span>
<span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">600</span><span class="p">,</span><span class="mi">280</span><span class="p">,</span><span class="mi">300</span><span class="p">,</span><span class="mi">280</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span><span class="o">.</span><span class="n">addStereoAnnotation</span><span class="o">=</span><span class="kc">True</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecules</span><span class="p">([</span><span class="n">l_allothreonine</span><span class="p">,</span><span class="n">d_allothreonine</span><span class="p">])</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<div class="prompt output_prompt">Out[10]:</div>
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<path class="bond-6" d="M 196.423,170.698 L 219.389,157.38" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="85.4876" y="182.624"><tspan>H</tspan><tspan style="baseline-shift:sub;font-size:27px;">2</tspan><tspan>N</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="186.505" y="254.553"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="249.277" y="145.332"><tspan>O</tspan></text>
<text dominant-baseline="central" style="font-size:27px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="136.425" y="35.1105"><tspan>(S)</tspan></text>
<text dominant-baseline="central" style="font-size:27px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="105.111" y="126.095"><tspan>(S)</tspan></text>
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<path class="bond-6" d="M 496.423,170.698 L 519.389,157.38" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="486.069" y="36.3578"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="385.488" y="182.624"><tspan>H</tspan><tspan style="baseline-shift:sub;font-size:27px;">2</tspan><tspan>N</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="486.505" y="254.553"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="549.277" y="145.332"><tspan>O</tspan></text>
<text dominant-baseline="central" style="font-size:27px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="436.425" y="35.1105"><tspan>(R)</tspan></text>
<text dominant-baseline="central" style="font-size:27px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="405.111" y="126.095"><tspan>(R)</tspan></text>
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<h2 id="adaptations-to-different-drawing-sizes:">adaptations to different drawing sizes:<a class="anchor-link" href="#adaptations-to-different-drawing-sizes:">¶</a></h2>
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<p>I've been using large drawings to make things super clear, but one of the other big improvements is that font scaling now works better:</p>
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<div class="prompt input_prompt">In [11]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">300</span><span class="p">,</span><span class="mi">140</span><span class="p">,</span><span class="mi">150</span><span class="p">,</span><span class="mi">140</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span><span class="o">.</span><span class="n">addStereoAnnotation</span><span class="o">=</span><span class="kc">True</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecules</span><span class="p">([</span><span class="n">l_allothreonine</span><span class="p">,</span><span class="n">d_allothreonine</span><span class="p">])</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
</pre></div>
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<path class="bond-6" d="M 101.498,91.0105 L 112.98,84.352" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-6" d="M 98.2141,85.3479 L 109.697,78.6893" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<text dominant-baseline="central" style="font-size:18px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="42.7504" y="91.3103"><tspan>H</tspan><tspan style="baseline-shift:sub;font-size:13.5px;">2</tspan><tspan>N</tspan></text>
<text dominant-baseline="central" style="font-size:18px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="93.2558" y="127.272"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:18px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="124.639" y="72.6656"><tspan>O</tspan></text>
<text dominant-baseline="central" style="font-size:13px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="68.2174" y="17.5589"><tspan>(S)</tspan></text>
<text dominant-baseline="central" style="font-size:13px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="52.5616" y="63.048"><tspan>(S)</tspan></text>
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<path class="bond-6" d="M 248.214,85.3479 L 259.697,78.6893" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<text dominant-baseline="central" style="font-size:18px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="192.75" y="91.3103"><tspan>H</tspan><tspan style="baseline-shift:sub;font-size:13.5px;">2</tspan><tspan>N</tspan></text>
<text dominant-baseline="central" style="font-size:18px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="243.256" y="127.272"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:18px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="274.639" y="72.6656"><tspan>O</tspan></text>
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<text dominant-baseline="central" style="font-size:13px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="202.562" y="63.048"><tspan>(R)</tspan></text>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span><span class="mi">80</span><span class="p">,</span><span class="mi">100</span><span class="p">,</span><span class="mi">80</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span><span class="o">.</span><span class="n">addStereoAnnotation</span><span class="o">=</span><span class="kc">True</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecules</span><span class="p">([</span><span class="n">l_allothreonine</span><span class="p">,</span><span class="n">d_allothreonine</span><span class="p">])</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<rect height="80" style="opacity:1.0;fill:#FFFFFF;stroke:none" width="200" x="0" y="0"> </rect>
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<path class="bond-6" d="M 65.1416,52.006 L 71.7031,48.2011" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 71.7031,48.2011 L 78.2646,44.3962" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 63.2652,48.7702 L 69.8267,44.9653" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<text dominant-baseline="central" style="font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="60.3072" y="10.39"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="31.5717" y="52.1773"><tspan>H</tspan><tspan style="baseline-shift:sub;font-size:7.5px;">2</tspan><tspan>N</tspan></text>
<text dominant-baseline="central" style="font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="60.4319" y="72.7271"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="78.3654" y="41.5232"><tspan>O</tspan></text>
<text dominant-baseline="central" style="font-size:7px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="46.1242" y="10.0336"><tspan>(S)</tspan></text>
<text dominant-baseline="central" style="font-size:7px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="37.1781" y="36.0275"><tspan>(S)</tspan></text>
<path class="bond-0" d="M 146.145,19.2549 L 129.677,6.19933 L 126.572,11.6049 Z" style="fill:#000000;fill-rule:evenodd;fill-opacity=1;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;"/>
<path class="bond-1" d="M 146.145,19.2549 L 152.707,15.45" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 152.707,15.45 L 159.268,11.6451" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-2" d="M 146.145,19.2549 L 146.187,40.0353" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 143.18,41.0592 L 143.805,42.1377" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 140.172,42.083 L 141.424,44.24" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 137.165,43.1069 L 139.042,46.3423" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 134.158,44.1307 L 136.66,48.4447" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 131.15,45.1546 L 134.279,50.547" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 146.187,40.0353 L 164.203,50.3881" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 164.203,50.3881 L 164.219,58.1808" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 164.219,58.1808 L 164.235,65.9734" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 165.142,52.006 L 171.703,48.2011" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 171.703,48.2011 L 178.265,44.3962" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 163.265,48.7702 L 169.827,44.9653" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 169.827,44.9653 L 176.388,41.1604" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text dominant-baseline="central" style="font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="160.307" y="10.39"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="131.572" y="52.1773"><tspan>H</tspan><tspan style="baseline-shift:sub;font-size:7.5px;">2</tspan><tspan>N</tspan></text>
<text dominant-baseline="central" style="font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="160.432" y="72.7271"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:10px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="178.365" y="41.5232"><tspan>O</tspan></text>
<text dominant-baseline="central" style="font-size:7px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="146.124" y="10.0336"><tspan>(R)</tspan></text>
<text dominant-baseline="central" style="font-size:7px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="137.178" y="36.0275"><tspan>(R)</tspan></text>
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<p>If we want the molecule scaling to be insensitive to the size of the drawing surface, we can use the <code>fixedBondLength</code> option:</p>
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<div class="prompt input_prompt">In [13]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">200</span><span class="p">,</span><span class="mi">100</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span><span class="o">.</span><span class="n">addStereoAnnotation</span><span class="o">=</span><span class="kc">True</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span><span class="o">.</span><span class="n">fixedBondLength</span><span class="o">=</span><span class="mi">30</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecule</span><span class="p">(</span><span class="n">l_allothreonine</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<svg baseProfile="full" height="100px" version="1.1" viewBox="0 0 200 100" width="200px" xml:space="preserve" xmlns="http://www.w3.org/2000/svg" xmlns:rdkit="http://www.rdkit.org/xml" xmlns:xlink="http://www.w3.org/1999/xlink">
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<rect height="100" style="opacity:1.0;fill:#FFFFFF;stroke:none" width="200" x="0" y="0"> </rect>
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<path class="bond-0" d="M 91.0645,20.8048 L 90.2882,22.1561" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 89.006,19.1728 L 87.8415,21.1999" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 86.9475,17.5409 L 85.3948,20.2436" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 84.889,15.9089 L 82.9481,19.2874" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 82.8305,14.277 L 80.5014,18.3311" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 80.772,12.645 L 78.0547,17.3749" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 78.7135,11.0131 L 75.608,16.4186" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 76.655,9.38111 L 73.1613,15.4624" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 74.5965,7.74916 L 70.7147,14.5061" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 95.1815,24.0686 L 103.383,19.3125" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 103.383,19.3125 L 111.585,14.5564" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-2" d="M 95.1815,24.0686 L 95.2335,50.0442" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 95.2335,50.0442 L 85.8358,53.2437 L 87.7909,56.6139 Z" style="fill:#000000;fill-rule:evenodd;fill-opacity=1;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;"/>
<path class="bond-3" d="M 85.8358,53.2437 L 80.3484,63.1837 L 76.4381,56.4432 Z" style="fill:#0000FF;fill-rule:evenodd;fill-opacity=1;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;"/>
<path class="bond-3" d="M 85.8358,53.2437 L 87.7909,56.6139 L 80.3484,63.1837 Z" style="fill:#0000FF;fill-rule:evenodd;fill-opacity=1;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;"/>
<path class="bond-4" d="M 95.2335,50.0442 L 117.754,62.9852" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 117.754,62.9852 L 117.774,72.726" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 117.774,72.726 L 117.793,82.4668" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 118.927,65.0075 L 127.129,60.2514" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 127.129,60.2514 L 135.331,55.4953" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 116.582,60.9628 L 124.783,56.2067" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 124.783,56.2067 L 132.985,51.4505" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text dominant-baseline="central" style="font-size:12px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="112.884" y="12.9875"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:12px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="76.9646" y="65.2217"><tspan>H</tspan><tspan style="baseline-shift:sub;font-size:9px;">2</tspan><tspan>N</tspan></text>
<text dominant-baseline="central" style="font-size:12px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="113.04" y="90.9088"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:12px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="135.457" y="51.904"><tspan>O</tspan></text>
<text dominant-baseline="central" style="font-size:9px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="95.1553" y="12.542"><tspan>(S)</tspan></text>
<text dominant-baseline="central" style="font-size:9px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="83.9726" y="45.0343"><tspan>(S)</tspan></text>
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<p>If I increase the size of the drawing surface but keep the same <code>fixedBondLength</code> value, the molecule is drawn the same size:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">400</span><span class="p">,</span><span class="mi">200</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span><span class="o">.</span><span class="n">addStereoAnnotation</span><span class="o">=</span><span class="kc">True</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span><span class="o">.</span><span class="n">fixedBondLength</span><span class="o">=</span><span class="mi">30</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecule</span><span class="p">(</span><span class="n">l_allothreonine</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<svg baseProfile="full" height="200px" version="1.1" viewBox="0 0 400 200" width="400px" xml:space="preserve" xmlns="http://www.w3.org/2000/svg" xmlns:rdkit="http://www.rdkit.org/xml" xmlns:xlink="http://www.w3.org/1999/xlink">
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<rect height="200" style="opacity:1.0;fill:#FFFFFF;stroke:none" width="400" x="0" y="0"> </rect>
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<path class="bond-0" d="M 189.68,66.2814 L 188.783,67.8422" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 187.303,64.3966 L 185.958,66.7378" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 184.925,62.5118 L 183.132,65.6334" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 182.548,60.627 L 180.306,64.529" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 180.17,58.7422 L 177.48,63.4246" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 177.793,56.8574 L 174.655,62.3202" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 175.416,54.9727 L 171.829,61.2157" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 173.038,53.0879 L 169.003,60.1113" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 170.661,51.2031 L 166.177,59.0069" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 194.435,70.051 L 203.908,64.558" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 203.908,64.558 L 213.38,59.065" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-2" d="M 194.435,70.051 L 194.495,100.051" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-3" d="M 183.641,103.746 L 185.899,107.639 L 177.304,115.226 Z" style="fill:#0000FF;fill-rule:evenodd;fill-opacity=1;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;"/>
<path class="bond-4" d="M 194.495,100.051 L 220.505,114.997" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 220.505,114.997 L 220.527,126.247" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 220.527,126.247 L 220.55,137.497" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 221.859,117.333 L 231.332,111.84" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 231.332,111.84 L 240.805,106.347" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 219.151,112.661 L 228.623,107.168" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 228.623,107.168 L 238.096,101.675" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="214.88" y="57.253"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="173.396" y="117.58"><tspan>H</tspan><tspan style="baseline-shift:sub;font-size:11.25px;">2</tspan><tspan>N</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="215.06" y="147.247"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:15px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="240.95" y="102.199"><tspan>O</tspan></text>
<text dominant-baseline="central" style="font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="194.405" y="56.7385"><tspan>(S)</tspan></text>
<text dominant-baseline="central" style="font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="181.489" y="94.265"><tspan>(S)</tspan></text>
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<p>Note that if the canvas is too small to fit the molecule, the bond size will be overridden.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">100</span><span class="p">,</span><span class="mi">75</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span><span class="o">.</span><span class="n">addStereoAnnotation</span><span class="o">=</span><span class="kc">True</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span><span class="o">.</span><span class="n">fixedBondLength</span><span class="o">=</span><span class="mi">30</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecule</span><span class="p">(</span><span class="n">l_allothreonine</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<div class="prompt output_prompt">Out[15]:</div>
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<svg baseProfile="full" height="75px" version="1.1" viewBox="0 0 100 75" width="100px" xml:space="preserve" xmlns="http://www.w3.org/2000/svg" xmlns:rdkit="http://www.rdkit.org/xml" xmlns:xlink="http://www.w3.org/1999/xlink">
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<rect height="75" style="opacity:1.0;fill:#FFFFFF;stroke:none" width="100" x="0" y="0"> </rect>
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<path class="bond-0" d="M 43.2984,15.1036 L 42.7161,16.1171" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 41.7545,13.8796 L 40.8811,15.3999" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 40.2106,12.6556 L 39.0461,14.6827" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-0" d="M 35.579,8.98375 L 33.541,12.5312" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 34.0351,7.75979 L 31.706,11.814" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 32.4913,6.53583 L 29.871,11.0968" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-0" d="M 30.9474,5.31187 L 28.036,10.3796" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 46.3862,17.5515 L 52.5375,13.9844" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 52.5375,13.9844 L 58.6889,10.4173" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-2" d="M 46.3862,17.5515 L 46.4251,37.0331" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-3" d="M 39.3768,39.4328 L 35.2613,46.8878 L 32.3286,41.8324 Z" style="fill:#0000FF;fill-rule:evenodd;fill-opacity=1;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;"/>
<path class="bond-3" d="M 39.3768,39.4328 L 40.8432,41.9605 L 35.2613,46.8878 Z" style="fill:#0000FF;fill-rule:evenodd;fill-opacity=1;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1;"/>
<path class="bond-4" d="M 46.4251,37.0331 L 63.3157,46.7389" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 63.3157,46.7389 L 63.3303,54.0445" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 63.3303,54.0445 L 63.3449,61.3501" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 64.1952,48.2556 L 70.3466,44.6885" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 70.3466,44.6885 L 76.498,41.1215" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 62.4361,45.2221 L 68.5875,41.655" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 68.5875,41.655 L 74.7389,38.0879" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text dominant-baseline="central" style="font-size:9px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="59.663" y="9.24062"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:9px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="32.7234" y="48.4162"><tspan>H</tspan><tspan style="baseline-shift:sub;font-size:6.75px;">2</tspan><tspan>N</tspan></text>
<text dominant-baseline="central" style="font-size:9px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="59.7799" y="67.6816"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:9px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="76.5926" y="38.428"><tspan>O</tspan></text>
<text dominant-baseline="central" style="font-size:7px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="46.3665" y="8.90653"><tspan>(S)</tspan></text>
<text dominant-baseline="central" style="font-size:7px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="37.9794" y="33.2757"><tspan>(S)</tspan></text>
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<h3 id="What-about-enhanced-stereo?">What about enhanced stereo?<a class="anchor-link" href="#What-about-enhanced-stereo?">¶</a></h3>
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<p>To display enhanced stereo information we currently need to use an extra function that adds the atom annotations:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">addEnhancedStereoAnnotations</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
<span class="n">gpLookup</span> <span class="o">=</span> <span class="p">{</span><span class="n">Chem</span><span class="o">.</span><span class="n">StereoGroupType</span><span class="o">.</span><span class="n">STEREO_OR</span><span class="p">:</span><span class="s2">"or"</span><span class="p">,</span>
<span class="n">Chem</span><span class="o">.</span><span class="n">StereoGroupType</span><span class="o">.</span><span class="n">STEREO_AND</span><span class="p">:</span><span class="s2">"&"</span><span class="p">,</span>
<span class="n">Chem</span><span class="o">.</span><span class="n">StereoGroupType</span><span class="o">.</span><span class="n">STEREO_ABSOLUTE</span><span class="p">:</span><span class="s2">"abs"</span><span class="p">,</span>
<span class="p">}</span>
<span class="n">sgs</span> <span class="o">=</span> <span class="n">m</span><span class="o">.</span><span class="n">GetStereoGroups</span><span class="p">()</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">sg</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">sgs</span><span class="p">):</span>
<span class="n">typ</span> <span class="o">=</span> <span class="n">gpLookup</span><span class="p">[</span><span class="n">sg</span><span class="o">.</span><span class="n">GetGroupType</span><span class="p">()]</span>
<span class="k">for</span> <span class="n">at</span> <span class="ow">in</span> <span class="n">sg</span><span class="o">.</span><span class="n">GetAtoms</span><span class="p">():</span>
<span class="n">nt</span> <span class="o">=</span> <span class="s2">""</span>
<span class="k">if</span> <span class="n">at</span><span class="o">.</span><span class="n">HasProp</span><span class="p">(</span><span class="s2">"atomNote"</span><span class="p">):</span>
<span class="n">nt</span> <span class="o">+=</span> <span class="n">at</span><span class="o">.</span><span class="n">GetProp</span><span class="p">(</span><span class="s2">"atomNote"</span><span class="p">)</span><span class="o">+</span><span class="s2">","</span>
<span class="n">nt</span> <span class="o">+=</span> <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">typ</span><span class="si">}{</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="si">}</span><span class="s2">"</span>
<span class="n">at</span><span class="o">.</span><span class="n">SetProp</span><span class="p">(</span><span class="s2">"atomNote"</span><span class="p">,</span><span class="n">nt</span><span class="p">)</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">threonine_and</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'C[C@@H](O)[C@H](N)C(O)=O |&1:1,3|'</span><span class="p">)</span>
<span class="n">threonine_or</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'C[C@@H](O)[C@H](N)C(O)=O |o1:1,3|'</span><span class="p">)</span>
<span class="n">addEnhancedStereoAnnotations</span><span class="p">(</span><span class="n">threonine_and</span><span class="p">)</span>
<span class="n">addEnhancedStereoAnnotations</span><span class="p">(</span><span class="n">threonine_or</span><span class="p">)</span>
<span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">600</span><span class="p">,</span><span class="mi">280</span><span class="p">,</span><span class="mi">300</span><span class="p">,</span><span class="mi">280</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecules</span><span class="p">([</span><span class="n">threonine_and</span><span class="p">,</span><span class="n">threonine_or</span><span class="p">])</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="186.069" y="36.3578"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="end" x="85.4876" y="182.624"><tspan>H</tspan><tspan style="baseline-shift:sub;font-size:27px;">2</tspan><tspan>N</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="186.505" y="254.553"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:36px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="start" x="249.277" y="145.332"><tspan>O</tspan></text>
<text dominant-baseline="central" style="font-size:27px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="136.425" y="35.1105"><tspan>&1</tspan></text>
<text dominant-baseline="central" style="font-size:27px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="105.111" y="126.095"><tspan>&1</tspan></text>
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<path class="bond-6" d="M 496.423,170.698 L 519.389,157.38" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<p>It should be easier to get this information displayed, so we'll add a new drawing option for that in the next release.</p>
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<h2 id="Advanced-highlighting">Advanced highlighting<a class="anchor-link" href="#Advanced-highlighting">¶</a></h2><p>Let's highlight pharmacophore features on a molecule. The tricky bit here is that one atom can be in multiple features. Fortunately there is new code to support this.</p>
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<div class="prompt input_prompt">In [18]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">atenolol</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'CC(C)NCC(O)COc1ccc(CC(N)=O)cc1'</span><span class="p">)</span>
<span class="n">atenolol</span>
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<div class="prompt output_prompt">Out[18]:</div>
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<p>It's worth pointing out how much nicer this drawing looks than it used to since the H is now above the amine N (instead of next to it).</p>
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<p>Start by getting the feature factory that we'll use to find the chemical features.</p>
<p>The default feature definition file that the RDKit includes isn't so great, but fortunately some years ago Markus Kossner contributed a better one that lives in the Contrib directory. We'll use that</p>
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<div class="prompt input_prompt">In [19]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">ChemicalFeatures</span>
<span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">RDConfig</span>
<span class="kn">import</span> <span class="nn">requests</span>
<span class="c1"># grab a feature definition file from the RDKit's contrib dir in github:</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">requests</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'https://raw.githubusercontent.com/rdkit/rdkit/master/Contrib/M_Kossner/BaseFeatures_DIP2_NoMicrospecies.fdef'</span><span class="p">)</span>
<span class="n">fdef</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">text</span>
<span class="n">ffact</span> <span class="o">=</span> <span class="n">ChemicalFeatures</span><span class="o">.</span><span class="n">BuildFeatureFactoryFromString</span><span class="p">(</span><span class="n">fdef</span><span class="p">)</span>
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<div class="prompt input_prompt">In [20]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">defaultdict</span>
<span class="n">feats</span> <span class="o">=</span> <span class="n">ffact</span><span class="o">.</span><span class="n">GetFeaturesForMol</span><span class="p">(</span><span class="n">atenolol</span><span class="p">)</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'SingleAtomDonor'</span><span class="p">:(</span><span class="mi">1</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span>
<span class="s1">'SingleAtomAcceptor'</span><span class="p">:(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span>
<span class="s1">'BasicGroup'</span><span class="p">:(</span><span class="mi">0</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">),</span>
<span class="s1">'Arom6'</span><span class="p">:(</span><span class="mi">1</span><span class="p">,</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.5</span><span class="p">),</span>
<span class="s1">'Hphobe'</span><span class="p">:(</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.3</span><span class="p">)}</span>
<span class="n">atomHighlights</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="n">highlightRads</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">feat</span> <span class="ow">in</span> <span class="n">feats</span><span class="p">:</span>
<span class="k">if</span> <span class="n">feat</span><span class="o">.</span><span class="n">GetType</span><span class="p">()</span> <span class="ow">in</span> <span class="n">colors</span><span class="p">:</span>
<span class="n">clr</span> <span class="o">=</span> <span class="n">colors</span><span class="p">[</span><span class="n">feat</span><span class="o">.</span><span class="n">GetType</span><span class="p">()]</span>
<span class="k">for</span> <span class="n">aid</span> <span class="ow">in</span> <span class="n">feat</span><span class="o">.</span><span class="n">GetAtomIds</span><span class="p">():</span>
<span class="n">atomHighlights</span><span class="p">[</span><span class="n">aid</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">clr</span><span class="p">)</span>
<span class="n">highlightRads</span><span class="p">[</span><span class="n">aid</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">600</span><span class="p">,</span><span class="mi">280</span><span class="p">,</span><span class="mi">300</span><span class="p">,</span><span class="mi">280</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMoleculeWithHighlights</span><span class="p">(</span><span class="n">atenolol</span><span class="p">,</span><span class="s2">"atenelol"</span><span class="p">,</span><span class="nb">dict</span><span class="p">(</span><span class="n">atomHighlights</span><span class="p">),{},</span><span class="n">highlightRads</span><span class="p">,{})</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
</pre></div>
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<div class="prompt output_prompt">Out[20]:</div>
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<path class="bond-17" d="M 198.338,141.862 L 184.476,133.842" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text dominant-baseline="central" style="font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="middle" x="64.6981" y="130.183"><tspan>N</tspan></text>
<text dominant-baseline="central" style="font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="start" x="60.8848" y="118.745"><tspan>H</tspan></text>
<text dominant-baseline="central" style="font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="start" x="100.142" y="107.338"><tspan>OH</tspan></text>
<text dominant-baseline="central" style="font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#FF0000" text-anchor="middle" x="143.944" y="130.247"><tspan>O</tspan></text>
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<text dominant-baseline="central" style="font-size:12px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="middle" x="150" y="265"><tspan>atenelol</tspan></text>
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<p>The atom highlights that we add are drawng as ellipses so that they can cover the full atom label, but it's possible to force them to be circles. We can also draw them just as outlines:</p>
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<div class="prompt input_prompt">In [21]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">600</span><span class="p">,</span><span class="mi">280</span><span class="p">,</span><span class="mi">300</span><span class="p">,</span><span class="mi">280</span><span class="p">)</span>
<span class="n">dos</span> <span class="o">=</span> <span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span>
<span class="n">dos</span><span class="o">.</span><span class="n">atomHighlightsAreCircles</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">dos</span><span class="o">.</span><span class="n">fillHighlights</span><span class="o">=</span><span class="kc">False</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMoleculeWithHighlights</span><span class="p">(</span><span class="n">atenolol</span><span class="p">,</span><span class="s2">"atenelol"</span><span class="p">,</span><span class="nb">dict</span><span class="p">(</span><span class="n">atomHighlights</span><span class="p">),{},</span><span class="n">highlightRads</span><span class="p">,{})</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<div class="prompt output_prompt">Out[21]:</div>
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<path class="bond-0" d="M 25.2841,128.224 L 45.4486,139.886" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 45.4486,139.886 L 45.43,163.181" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-2" d="M 45.4486,139.886 L 53.0163,135.526" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-3" d="M 70.6796,131.176 L 78.2379,135.547" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-4" d="M 85.7963,139.918 L 105.979,128.289" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-6" d="M 105.979,128.289 L 126.144,139.951" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-7" d="M 133.516,135.704 L 140.888,131.456" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-8" d="M 159.129,135.726 L 166.492,139.984" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 166.492,139.984 L 166.473,163.279" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 170.682,143.481 L 170.669,159.788" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-18" d="M 166.492,139.984 L 186.675,128.35" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 166.473,163.279 L 186.638,174.945" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-11" d="M 186.638,174.945 L 206.821,163.312" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-12" d="M 206.821,163.312 L 226.985,174.973" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-16" d="M 206.821,163.312 L 206.839,140.016" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-14" d="M 247.168,163.344 L 254.727,167.716" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-17" d="M 206.839,140.016 L 186.675,128.35" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:2px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<text dominant-baseline="central" style="font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#0000FF" text-anchor="start" x="61.7487" y="118.356"><tspan>H</tspan></text>
<text dominant-baseline="central" style="font-size:11px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;fill:#000000" text-anchor="start" x="101.724" y="106.741"><tspan>OH</tspan></text>
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<p>We can also have multiple highlights on bonds. Here's an example showing the ring systems in a <a href="https://www.ebi.ac.uk/chembl/compound_report_card/CHEMBL2314369/">molecule from ChEMBL</a>:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">chembl2314369</span><span class="o">=</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'N#CC(C#N)=C1C(=O)c2cccc3cccc1c23'</span><span class="p">)</span>
<span class="n">rings</span> <span class="o">=</span> <span class="n">chembl2314369</span><span class="o">.</span><span class="n">GetRingInfo</span><span class="p">()</span>
<span class="n">colors</span> <span class="o">=</span> <span class="p">[(</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.0</span><span class="p">,</span><span class="mf">0.8</span><span class="p">),(</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.8</span><span class="p">,</span><span class="mi">0</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.8</span><span class="p">),(</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mf">0.8</span><span class="p">)]</span>
<span class="n">athighlights</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="n">arads</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">rng</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">rings</span><span class="o">.</span><span class="n">AtomRings</span><span class="p">()):</span>
<span class="k">for</span> <span class="n">aid</span> <span class="ow">in</span> <span class="n">rng</span><span class="p">:</span>
<span class="n">athighlights</span><span class="p">[</span><span class="n">aid</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">arads</span><span class="p">[</span><span class="n">aid</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.3</span>
<span class="n">bndhighlights</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">rng</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">rings</span><span class="o">.</span><span class="n">BondRings</span><span class="p">()):</span>
<span class="k">for</span> <span class="n">bid</span> <span class="ow">in</span> <span class="n">rng</span><span class="p">:</span>
<span class="n">bndhighlights</span><span class="p">[</span><span class="n">bid</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">colors</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="n">d2d</span> <span class="o">=</span> <span class="n">rdMolDraw2D</span><span class="o">.</span><span class="n">MolDraw2DSVG</span><span class="p">(</span><span class="mi">400</span><span class="p">,</span><span class="mi">400</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMoleculeWithHighlights</span><span class="p">(</span><span class="n">chembl2314369</span><span class="p">,</span><span class="s1">'CHEMBL2314369'</span><span class="p">,</span><span class="nb">dict</span><span class="p">(</span><span class="n">athighlights</span><span class="p">),</span><span class="nb">dict</span><span class="p">(</span><span class="n">bndhighlights</span><span class="p">),</span><span class="n">arads</span><span class="p">,{})</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">SVG</span><span class="p">(</span><span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<p>There are some other improvements under the hood, but this covers the major new stuff.</p>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com5tag:blogger.com,1999:blog-684443317148892945.post-46574136074968569582020-04-23T21:38:00.002-07:002023-05-23T21:38:45.429-07:00Setting up a Windows environment to make Python contributions to the RDKit<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<h1 id="Setting-up-a-Windows-environment-to-make-Python-contributions-to-the-RDKit">Setting up a Windows environment to make Python contributions to the RDKit<a class="anchor-link" href="#Setting-up-a-Windows-environment-to-make-Python-contributions-to-the-RDKit">¶</a></h1><p>There is an updated version of this post, combined with the instructions for Linux and the Mac, available here: <a href="https://greglandrum.github.io/rdkit-blog/posts/2020-03-30-setting-up-an-environment.html">https://greglandrum.github.io/rdkit-blog/posts/2020-03-30-setting-up-an-environment.html</a></p><p>This is a followup to a longer post on <a href="http://rdkit.blogspot.com/2020/03/setting-up-environment-to-make-python.html">setting up an environment for Linux or the Mac</a>, there's more background there.</p>
<p>I'm going to explain each of the required steps, but the complete set of steps required is at the bottom of this post. Assuming that you have the prerequisites (explained directly below), I hope that these will "just work" for you, but one never knows... I'd like to be able to include this in the RDKit documentation, so please me know how it goes if you try the recipe out. Please do not add a comment to this blog post, I've created a <a href="https://github.com/rdkit/rdkit/issues/3052">github issue</a> so that we have the comments in one place. If you don't have a github account, please email me your comments and I'll add them to the issue.</p>
<h2 id="The-steps-explained">The steps explained<a class="anchor-link" href="#The-steps-explained">¶</a></h2><p>Prerequisites:</p>
<ul>
<li>you need to have either anaconda python or miniconda installed and in your path</li>
<li>you need to have git installed and in your path</li>
<li>these instructions assume that you are running in git bash (or some other capable bash-type shell). I'm sure you can do all of this in powershell, but I haven't managed to figure that out.</li>
</ul>
<p>You should start by changing into the directory where you want to clone the RDKit source repository and then running:</p>
<pre><code>git clone https://github.com/rdkit/rdkit.git
</code></pre>
<p>That will clone the repo from github into a local directory called rdkit. We now change into that directory and use it to set our RDBASE environment variable:</p>
<pre><code>cd rdkit
export RDBASE=`pwd`
</code></pre>
<p>The next step is to create the conda environment that we're going to use to hold the RDKit binary components and install a recent beta version of the RDKit into that environment:</p>
<pre><code>conda create -y -n py37_rdkit_beta python=3.7
conda activate py37_rdkit_beta
conda install -y -c rdkit/label/beta rdkit
</code></pre>
<p>If you have other Python packages that you'd like to work with, go ahead and install them into the environment now.</p>
<p>Next we copy the RDKit binary components from that environment into our local clone of the RDKit repo:</p>
<pre><code>cd $CONDA_PREFIX/lib/python3.7/site-packages/rdkit
find . -name '*.pyd' -exec cp --parents \{\} $RDBASE/rdkit \;
cp Chem/inchi.py $RDBASE/rdkit/Chem</code></pre>
<p>Finally we set our PYTHONPATH and then test that everything is working by importing the RDKit's Chem module:</p>
<pre><code>export PYTHONPATH="$RDBASE"
cd $RDBASE/rdkit
python -c 'from rdkit import Chem;print(Chem.__file__)'</code></pre>
<p>That last command should not generate errors and should show you a filename that is in your local github clone. As an example, I started the first step of this process in my <code>Code\rdkit_tmp</code> directory, so I see:</p>
<pre><code>c:\Users\glandrum\Code\rdkit_tmp\rdkit\Chem\__init__.py</code></pre>
<h2 id="Running-the-tests">Running the tests<a class="anchor-link" href="#Running-the-tests">¶</a></h2><p>If you're planning on making an RDKit contribution, it's important to know how to run the Python tests to make sure that your changes work and don't break anything else. For historic reasons the RDKit uses a self-written framework for running tests, but it's easy enough to use. You need to run the script <code>$RDBASE/rdkit/TestRunner.py</code> and point it to the test_list.py file containing the tests to be run. For example, if you want to run all the tests in the directory <code>$RDBASE/rdkit/Chem</code> (this corresponds to the python module <code>rdkit.Chem</code>), you would do:</p>
<pre><code>cd $RDBASE/rdkit/Chem
python $RDBASE/rdkit/TestRunner.py test_list.py</code></pre>
<p>That will take a while and generate a lot of output, including things that look like exceptions and errors, but should finish with something like:</p>
<pre><code>Script: test_list.py. Passed 40 tests in 69.70 seconds</code></pre>
<h2 id="Finishing-up">Finishing up<a class="anchor-link" href="#Finishing-up">¶</a></h2><p>You're set. The one thing to remember is that whenever you want to use this environment in a new terminal window or shell, you need to activate the py37_rdkit_beta conda environment (don't delete it!), set RDBASE, and set your PYTHONPATH:</p>
<pre><code>conda activate py37_rdkit_beta
cd your_local_rdkit_clone # <- replace this with the real name of the directory
export RDBASE=`pwd`
export PYTHONPATH="$RDBASE"</code></pre>
<p>If you are planning on working with the normal Windows command prompt or powershell, you'd do the equivalent things to set the RDBASE environment variable and the make sure your PYTHONPATH is pointing to the right place.</p>
<h2 id="The-recipe">The recipe<a class="anchor-link" href="#The-recipe">¶</a></h2><p>Here's the complete recipe:</p>
<pre><code>git clone https://github.com/rdkit/rdkit.git
cd rdkit
export RDBASE=`pwd`
conda create -y -n py37_rdkit_beta python=3.7
conda activate py37_rdkit_beta
conda install -y -c rdkit/label/beta rdkit
cd $CONDA_PREFIX/lib/python3.7/site-packages/rdkit
find . -name '*.pyd' -exec cp --parents \{\} $RDBASE/rdkit \;
cp Chem/inchi.py $RDBASE/rdkit/Chem
export PYTHONPATH="$RDBASE"
cd $RDBASE/rdkit
python -c 'from rdkit import Chem;print(Chem.__file__)'</code></pre>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com0tag:blogger.com,1999:blog-684443317148892945.post-19746195775184951832020-03-30T05:18:00.001-07:002023-05-23T19:46:04.829-07:00Setting up an environment to make Python contributions to the RDKit<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p><b>NOTE </b>There is an updated version of this post here: <a href="https://greglandrum.github.io/rdkit-blog/posts/2020-03-30-setting-up-an-environment.html">https://greglandrum.github.io/rdkit-blog/posts/2020-03-30-setting-up-an-environment.html</a></p><p><br /></p><p>This was originally done on blogger, but it made a completely mess of the formatting.</p>
<h1 id="Setting-up-an-environment-to-make-Python-contributions-to-the-RDKit">Setting up an environment to make Python contributions to the RDKit<a class="anchor-link" href="#Setting-up-an-environment-to-make-Python-contributions-to-the-RDKit">¶</a></h1><p>It has been tricky to contribute code or documentation to the RDKit if you're a Python programmer who doesn't want to deal with the complexities of getting an RDKit build working. We want to make it straightforward for people to contribute, so I'm working on some recipes to make thigs easier. This is the first pass at that.</p>
<p>In order to fix bugs or add features in Python you need to be able to clone a local fork of the RDKit from github, modify the code in that local clone, and then run the local code in order to test it. The problem is that most RDKit functionality requires some binary components that need to be built from C++ and installed in the appropriate places. We're going to work around that problem here by copying the binary components from a recent binary distribution of the RDKit into a local clone of the RDKit repo.</p>
<p>I'm going to explain each of the required steps, but the complete set of steps required is at the bottom of this post. Assuming that you have the prerequisites (explained directly below), I hope that these will "just work" for you, but one never knows... I'd like to be able to include this in the RDKit documentation, so please me know how it goes if you try the recipe out. Please do not add a comment to this blog post, I've created a <a href="https://github.com/rdkit/rdkit/issues/3052">github issue</a> so that we have the comments in one place. If you don't have a github account, please email me your comments and I'll add them to the issue.</p>
<h2 id="The-steps-explained">The steps explained<a class="anchor-link" href="#The-steps-explained">¶</a></h2><p>At the moment this recipe only works on linux and the mac. I will put together a similar recipe for windows and either do a separate post or update this one.</p>
<p>Prerequisites:</p>
<ul>
<li>you need to have either anaconda python or miniconda installed and in your path</li>
<li>you need to have git installed and in your path</li>
</ul>
<p>You should start by changing into the directory where you want to clone the RDKit source repository and then running:</p>
<pre><code>git clone https://github.com/rdkit/rdkit.git
</code></pre>
<p>That will clone the repo from github into a local directory called rdkit. We now change into that directory and use it to set our RDBASE environment variable:</p>
<pre><code>cd rdkit
export RDBASE=`pwd`
</code></pre>
<p>The next step is to create the conda environment that we're going to use to hold the RDKit binary components and install a recent beta version of the RDKit into that environment:</p>
<pre><code>conda create -y -n py37_rdkit_beta python=3.7
conda activate py37_rdkit_beta
conda install -y -c rdkit/label/beta rdkit
</code></pre>
<p>If you have other Python packages that you'd like to work with, go ahead and install them into the environment now.</p>
<p>Next we copy the RDKit binary components from that environment into our local clone of the RDKit repo:</p>
<pre><code>cd $CONDA_PREFIX/lib/python3.7/site-packages/rdkit
rsync -a -m --include '*/' --include='*.so' --include='inchi.py' --exclude='*' . $RDBASE/rdkit</code></pre>
<p>NOTE: that rsync command should be one long line.</p>
<p>Finally we set our PYTHONPATH and then test that everything is working by importing the RDKit's Chem module:</p>
<pre><code>export PYTHONPATH="$RDBASE"
cd $RDBASE/rdkit
python -c 'from rdkit import Chem;print(Chem.__file__)'</code></pre>
<p>That last command should not generate errors and should show you a filename that is in your local github clone. As an example, I started the first step of this process in my /scratch/rdkit_devel directory, so I see:</p>
<pre><code>/scratch/rdkit_devel/rdkit/rdkit/Chem/__init__.py</code></pre>
<h2 id="Running-the-tests">Running the tests<a class="anchor-link" href="#Running-the-tests">¶</a></h2><p>If you're planning on making an RDKit contribution, it's important to know how to run the Python tests to make sure that your changes work and don't break anything else. For historic reasons the RDKit uses a self-written framework for running tests, but it's easy enough to use. You need to run the script <code>$RDBASE/rdkit/TestRunner.py</code> and point it to the test_list.py file containing the tests to be run. For example, if you want to run all the tests in the directory <code>$RDBASE/rdkit/Chem</code> (this corresponds to the python module <code>rdkit.Chem</code>), you would do:</p>
<pre><code>cd $RDBASE/rdkit/Chem
python $RDBASE/rdkit/TestRunner.py test_list.py</code></pre>
<p>That will take a while and generate a lot of output, including things that look like exceptions and errors, but should finish with something like:</p>
<pre><code>Script: test_list.py. Passed 40 tests in 69.70 seconds</code></pre>
<h2 id="Finishing-up">Finishing up<a class="anchor-link" href="#Finishing-up">¶</a></h2><p>You're set. The one thing to remember is that whenever you want to use this environment in a new terminal window or shell, you need to activate the py37_rdkit_beta conda environment (don't delete it!), set RDBASE, and set your PYTHONPATH:</p>
<pre><code>conda activate py37_rdkit_beta
cd your_local_rdkit_clone # <- replace this with the real name of the directory
export RDBASE=`pwd`
export PYTHONPATH="$RDBASE"</code></pre>
<h2 id="The-recipe">The recipe<a class="anchor-link" href="#The-recipe">¶</a></h2><p>Here's the complete recipe:</p>
<pre><code>git clone https://github.com/rdkit/rdkit.git
cd rdkit
export RDBASE=`pwd`
conda create -y -n py37_rdkit_beta python=3.7
conda activate py37_rdkit_beta
conda install -y -c rdkit/label/beta rdkit
cd $CONDA_PREFIX/lib/python3.7/site-packages/rdkit
rsync -a -m --include '*/' --include='*.so' --include='inchi.py' --exclude='*' . $RDBASE/rdkit
export PYTHONPATH="$RDBASE"
cd $RDBASE/rdkit
python -c 'from rdkit import Chem;print(Chem.__file__)'</code></pre>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com0tag:blogger.com,1999:blog-684443317148892945.post-76863722725571691392020-01-30T20:49:00.000-08:002020-01-30T20:49:00.954-08:00Finding regioisomers<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>This is one that came up recently on the <a href="https://www.mail-archive.com/rdkit-discuss@lists.sourceforge.net/msg09570.html">mailing list</a> that I thought made for a good example to demonstrate how to write Python to do some more advanced structural searches with the RDKit.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">Chem</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">Draw</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">IPythonConsole</span>
<span class="kn">import</span> <span class="nn">rdkit</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rdkit</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
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<pre>2019.09.3
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<pre>RDKit WARNING: [05:43:19] Enabling RDKit 2019.09.3 jupyter extensions
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<p>My paraphrasing of the problem: Alexis wanted to be able to do the equivalent of a substructure
search that finds all aromatic rings that have both Cl and Br substituents. So he wanted to be able to match the first two of these, but not the second:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">ms</span> <span class="o">=</span> <span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="s1">'Clc1c(Br)cccc1 Clc1cc(Br)ccc1 Clc1cccc2c1c(Br)ccc2'</span><span class="o">.</span><span class="n">split</span><span class="p">()]</span>
<span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">(</span><span class="n">ms</span><span class="p">,</span><span class="n">legends</span><span class="o">=</span><span class="s1">'match match no-match'</span><span class="o">.</span><span class="n">split</span><span class="p">())</span>
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<img 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<p>It's really non-trivial to do this with SMARTS since it has no way to express that two atoms should be in the same ring without making the ring explicit in the SMARTS. I was able to come up with this SMARTS, which works, but is unwieldy (at best):</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">p</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmarts</span><span class="p">(</span><span class="s1">'Cl[c;$(c1(Cl)c(Br)cccc1),$(c1(Cl)cc(Br)ccc1),$(c1(Cl)ccc(Br)cc1)]'</span><span class="p">)</span>
<span class="nb">print</span><span class="p">([</span><span class="n">m</span><span class="o">.</span><span class="n">HasSubstructMatch</span><span class="p">(</span><span class="n">p</span><span class="p">)</span> <span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">])</span>
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<pre>[True, True, False]
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<p>So that does what we want, but it only handles six rings where every atom is a C. That second part is easy enough to change in the SMARTS, but handling other ring sizes starts to make the SMARTS really long.</p>
<p>A more difficult problem is that, because we use recursive SMARTS, we can't get the atoms matching the query. The pattern I used above would only return the Cl atom and the C atom it's connected to. I'm not sure Alexis even wanted to do that, but by this point I was interested in the problem and decided to write some Python to solve the problem flexibly.</p>
<p>Here we go.</p>
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<p>Before introducing the code and showing what it can do, a quick intro on the two pieces of functionality I'm going to be using from Python's <code>itertools</code> module. These are really useful.</p>
<p>Let's start with using itertools to flatten a sequence of sequences:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">itertools</span>
<span class="n">seqs</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">],[</span><span class="s1">'a'</span><span class="p">,</span><span class="s1">'b'</span><span class="p">],[</span><span class="mi">10</span><span class="p">,</span><span class="mi">20</span><span class="p">]]</span>
<span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">chain</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span><span class="n">seqs</span><span class="p">))</span>
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<pre>[1, 2, 3, 'a', 'b', 10, 20]</pre>
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<p>And to generate all the permutations of combinations of those sequences:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">seqs</span><span class="p">))</span>
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<pre>[(1, 'a', 10),
(1, 'a', 20),
(1, 'b', 10),
(1, 'b', 20),
(2, 'a', 10),
(2, 'a', 20),
(2, 'b', 10),
(2, 'b', 20),
(3, 'a', 10),
(3, 'a', 20),
(3, 'b', 10),
(3, 'b', 20)]</pre>
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<p>Ok, that's the background, let's define the functions we'll use:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">itertools</span>
<span class="k">def</span> <span class="nf">getAromaticRings</span><span class="p">(</span><span class="n">mol</span><span class="p">):</span>
<span class="sd">""" generator returning all aromatic rings (=only aromatic bonds) in a molecule </span>
<span class="sd"> </span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> mol : Mol</span>
<span class="sd"> Yields</span>
<span class="sd"> ------</span>
<span class="sd"> set</span>
<span class="sd"> IDs of the atoms in an aromatic ring</span>
<span class="sd"> </span>
<span class="sd"> """</span>
<span class="n">ri</span> <span class="o">=</span> <span class="n">mol</span><span class="o">.</span><span class="n">GetRingInfo</span><span class="p">()</span>
<span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="n">ri</span><span class="o">.</span><span class="n">BondRings</span><span class="p">():</span>
<span class="n">ats</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="n">isArom</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">for</span> <span class="n">bi</span> <span class="ow">in</span> <span class="n">ring</span><span class="p">:</span>
<span class="n">bnd</span> <span class="o">=</span> <span class="n">mol</span><span class="o">.</span><span class="n">GetBondWithIdx</span><span class="p">(</span><span class="n">bi</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">bnd</span><span class="o">.</span><span class="n">GetIsAromatic</span><span class="p">():</span>
<span class="n">isArom</span> <span class="o">=</span> <span class="kc">False</span>
<span class="k">break</span>
<span class="n">ats</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">bnd</span><span class="o">.</span><span class="n">GetBeginAtomIdx</span><span class="p">())</span>
<span class="n">ats</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">bnd</span><span class="o">.</span><span class="n">GetEndAtomIdx</span><span class="p">())</span>
<span class="k">if</span> <span class="n">isArom</span><span class="p">:</span>
<span class="k">yield</span> <span class="n">ats</span>
<span class="k">def</span> <span class="nf">getSharedRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">queries</span><span class="p">,</span><span class="n">rings</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span><span class="n">excludeQueries</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
<span class="sd">""" generator returning all rings that contain all the atoms defined in queries </span>
<span class="sd"> the first atom matching each query should be in the ring</span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> mol : Mol</span>
<span class="sd"> queries : sequence of Mols</span>
<span class="sd"> rings : list/tuple/set of list/tuple/sets, optional</span>
<span class="sd"> sequence of rings defined by sequences of atom ids</span>
<span class="sd"> If this isn't provided, all of the molecule's rings will be used</span>
<span class="sd"> excludeQueries : sequence of Mols, optional</span>
<span class="sd"> any ring containing an atom matching the first atom in any of these queries</span>
<span class="sd"> will be excluded</span>
<span class="sd"> Yields</span>
<span class="sd"> -------</span>
<span class="sd"> set</span>
<span class="sd"> containing atom IDs for a matching ring</span>
<span class="sd"> """</span>
<span class="k">if</span> <span class="n">rings</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">rings</span> <span class="o">=</span> <span class="n">mol</span><span class="o">.</span><span class="n">GetRingInfo</span><span class="p">()</span><span class="o">.</span><span class="n">AtomRings</span><span class="p">()</span>
<span class="n">alreadySeen</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">rings</span> <span class="o">=</span> <span class="p">[</span><span class="nb">set</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">rings</span><span class="p">]</span>
<span class="n">matchSets</span> <span class="o">=</span> <span class="p">[[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">mol</span><span class="o">.</span><span class="n">GetSubstructMatches</span><span class="p">(</span><span class="n">q</span><span class="p">)]</span> <span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="n">queries</span><span class="p">]</span>
<span class="k">if</span> <span class="n">excludeQueries</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">exclude</span> <span class="o">=</span> <span class="p">[[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">mol</span><span class="o">.</span><span class="n">GetSubstructMatches</span><span class="p">(</span><span class="n">q</span><span class="p">)]</span> <span class="k">for</span> <span class="n">q</span> <span class="ow">in</span> <span class="n">excludeQueries</span><span class="p">]</span>
<span class="c1"># flatten the lists of matches into a set:</span>
<span class="n">exclude</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">chain</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span><span class="n">exclude</span><span class="p">))</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">exclude</span> <span class="o">=</span> <span class="nb">set</span><span class="p">()</span>
<span class="k">for</span> <span class="n">combo</span> <span class="ow">in</span> <span class="n">itertools</span><span class="o">.</span><span class="n">product</span><span class="p">(</span><span class="o">*</span><span class="n">matchSets</span><span class="p">):</span>
<span class="n">scombo</span> <span class="o">=</span> <span class="nb">set</span><span class="p">(</span><span class="n">combo</span><span class="p">)</span>
<span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">scombo</span><span class="p">)</span> <span class="o"><</span> <span class="nb">len</span><span class="p">(</span><span class="n">combo</span><span class="p">):</span>
<span class="c1"># degenerate:</span>
<span class="k">continue</span>
<span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="n">rings</span><span class="p">:</span>
<span class="k">if</span> <span class="n">ring</span> <span class="ow">in</span> <span class="n">alreadySeen</span><span class="p">:</span>
<span class="k">continue</span>
<span class="k">if</span> <span class="n">scombo</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="n">ring</span><span class="p">)</span> <span class="ow">and</span> <span class="n">exclude</span><span class="o">.</span><span class="n">isdisjoint</span><span class="p">(</span><span class="n">ring</span><span class="p">):</span>
<span class="n">alreadySeen</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ring</span><span class="p">)</span>
<span class="k">yield</span> <span class="n">ring</span>
<span class="k">def</span> <span class="nf">drawMolWithRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">rings</span><span class="p">):</span>
<span class="sd">""" draws a molecule with a set of rings highlighted</span>
<span class="sd"> </span>
<span class="sd"> Parameters</span>
<span class="sd"> ----------</span>
<span class="sd"> mol : Mol</span>
<span class="sd"> rings : list/tuple/set of list/tuple/sets</span>
<span class="sd"> sequence of rings defined by sequences of atom IDs</span>
<span class="sd"> Returns</span>
<span class="sd"> -------</span>
<span class="sd"> Image</span>
<span class="sd"> """</span>
<span class="n">bondsToHighlight</span><span class="o">=</span><span class="p">[]</span>
<span class="k">for</span> <span class="n">bnd</span> <span class="ow">in</span> <span class="n">mol</span><span class="o">.</span><span class="n">GetBonds</span><span class="p">():</span>
<span class="n">keep</span> <span class="o">=</span> <span class="kc">False</span>
<span class="n">ats</span> <span class="o">=</span> <span class="nb">set</span><span class="p">([</span><span class="n">bnd</span><span class="o">.</span><span class="n">GetBeginAtomIdx</span><span class="p">(),</span><span class="n">bnd</span><span class="o">.</span><span class="n">GetEndAtomIdx</span><span class="p">()])</span>
<span class="k">for</span> <span class="n">ring</span> <span class="ow">in</span> <span class="n">rings</span><span class="p">:</span>
<span class="k">if</span> <span class="n">ats</span><span class="o">.</span><span class="n">issubset</span><span class="p">(</span><span class="n">ring</span><span class="p">):</span>
<span class="n">keep</span> <span class="o">=</span> <span class="kc">True</span>
<span class="k">break</span>
<span class="k">if</span> <span class="n">keep</span><span class="p">:</span>
<span class="n">bondsToHighlight</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">bnd</span><span class="o">.</span><span class="n">GetIdx</span><span class="p">())</span>
<span class="n">highlightAtoms</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">itertools</span><span class="o">.</span><span class="n">chain</span><span class="o">.</span><span class="n">from_iterable</span><span class="p">(</span><span class="n">rings</span><span class="p">))</span>
<span class="n">tmol</span> <span class="o">=</span> <span class="n">Draw</span><span class="o">.</span><span class="n">PrepareMolForDrawing</span><span class="p">(</span><span class="n">mol</span><span class="p">)</span>
<span class="n">d2d</span> <span class="o">=</span> <span class="n">Draw</span><span class="o">.</span><span class="n">MolDraw2DCairo</span><span class="p">(</span><span class="mi">300</span><span class="p">,</span> <span class="mi">250</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecule</span><span class="p">(</span><span class="n">tmol</span><span class="p">,</span> <span class="n">highlightAtoms</span><span class="o">=</span><span class="n">highlightAtoms</span><span class="p">,</span>
<span class="n">highlightBonds</span> <span class="o">=</span> <span class="n">bondsToHighlight</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="k">return</span> <span class="n">Draw</span><span class="o">.</span><span class="n">_drawerToImage</span><span class="p">(</span><span class="n">d2d</span><span class="p">)</span>
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<p>This is the molecule we'll work with:</p>
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<div class="prompt input_prompt">In [7]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">mol</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'c1c(Cl)cc(Br)c2c1CCc3c2cc(Cl)c4c3CCC(Cl)C4Br'</span><span class="p">)</span>
<span class="n">mol</span>
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<div class="prompt output_prompt">Out[7]:</div>
<div class="output_png output_subarea output_execute_result">
<img src="data:image/png;base64,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<p>Show what the <code>getAromaticRings()</code> function returns here:</p>
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<div class="prompt input_prompt">In [8]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">rings</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">getAromaticRings</span><span class="p">(</span><span class="n">mol</span><span class="p">))</span>
<span class="n">rings</span>
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<div class="prompt output_prompt">Out[8]:</div>
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<pre>[{0, 1, 3, 4, 6, 7}, {10, 11, 12, 13, 15, 16}]</pre>
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<p>We can use <code>drawMolWithRings()</code> to highlight those atoms:</p>
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<div class="prompt input_prompt">In [9]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">drawMolWithRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">rings</span><span class="p">)</span>
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<div class="prompt output_prompt">Out[9]:</div>
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<img 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<p>Now let's look at Alexis' question: find all the aromatic rings that have a Cl and a Br attached:</p>
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<div class="prompt input_prompt">In [10]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">matches</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">getSharedRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmarts</span><span class="p">(</span><span class="n">sma</span><span class="p">)</span> <span class="k">for</span> <span class="n">sma</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'[a]-Cl'</span><span class="p">,</span><span class="s1">'[a]-Br'</span><span class="p">)],</span>
<span class="n">rings</span><span class="o">=</span><span class="n">getAromaticRings</span><span class="p">(</span><span class="n">mol</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">matches</span><span class="p">)</span>
<span class="n">drawMolWithRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">matches</span><span class="p">)</span>
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<pre>[{0, 1, 3, 4, 6, 7}]
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<div class="prompt output_prompt">Out[10]:</div>
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86fV0+ldSASicLCwgDghx9+0FqlHxcdHc1msy9durRz504tVFdcXBwZGQkAy5cv10J1mqbzIaQouHtXueDYs2dPCwtbNmo0VuVPYO/eoN6N2Q0Nwd9fOdKGdPoef38mg7EtIeHCy5fVXpycDM+eqbP2D9uxY0dubq6np+egQbpyNqCtrW10dDQAzJs3LyMjQ9PVbd68mc/nDx48uHPNmb16SOdDmJICxcXKBevu3AGA5b17GyoPlJuYQKdO6q/d3h569FAu6NCkyQ99+1IA006d4gmF1V589iyUl6u/DdVJJJKNGzcCwPfff6+dG786CggIGDNmTHl5+eTJk2U1ZheqUVlZ2bZt2wDgu+++01wt2qTbIaQoqD5Efvb586Tc3GZmZpM8PKq9skcP0NAy0y++gKbVdvtd2qtXbweHt6Wl85X6igAABAI4fVojbVCyf//+V69etW/f3t/fX9N1faqIiAh7e/vbt29v2bJFc7Vs27aNy+X26dOntyYnaWiTbveO/vUX/PmnckGvnTvvZGf/NmjQgu7d/y41NoaQEA32ixQUQHS08qS5TB7PY/v2soqKWrpn/f1B5Q+E+shkMldX1/T09H379n39tS7u4HDp0qXBgwcbGRklJSV16NChIW8lEolUFmFxudzCwsLdu3fz+fwLFy7ozt14A+l27+jt28pfXc/KupOdzWaxvvGuftpO166a7Zls2hT69lWeKuBkZfXrgAFzzp2befZsr5YtmypvC3/+PDg6QiONnHkgPxuwVatWY8eO1cT7N9zAgQODgoJiYmImTpx47969Wpdo1Nxksdad4PLz82u9QjRt2lQikbRt21bzv42W6PCV8OVL2L9fuWDA3r1XMzNX9eu3Urlf3tAQQkKAxdJsY2Qy2LkT3r5VFFAUNXT//gsvX/q5uJxQiYSBAbi4QPfuoO5BM29v74cPH0ZEREyfPl2976xG5eXlnp6eL1++HDp0aOfOneWXsqKiIsViYqHKs/QHGBsbKy+5UizCOnbs2O3bt3v27Hnz5k066Qn0aqHDIdy1S3m/poS3b7tGR1sYG78KCbFSnsndvTsMHKiN9nC5EBmpvJbqbWmp2/btPKFw34gRX7u7V3sxjQYMBrRqBQEB6rpKnzlzZtiwYc2aNcvKytLNk8kUTp8+PXbsWIlEUuuy/ZrbwNW6E5ytrW2tPU/v3r1zc3N78+ZNaGjo/PnzNf/baJyu3o6+eaOyY9rquDgAmP3ZZ9USSKeD8sOhRrHZ0L8/XLigKGhuYbFp0KDJJ058e+5cHweHlsq3oBQFEglkZkJUFEybppYLtW6eDZifn0+j0ZpW77tKSkoSCATOzs6BgYGK6UqK2Uushv1rWFpa7tixY/DgwcuXL/fx8fkXDNbr6pXwjz9AabjpWVGRW3g4k8HICglpYqq0iUjnzuDrq71WURTs3w/Vx8FG/vnnsWfPBjg5XQoMrOUvN50OzZpBUFADpxBcu3atf//+bDb71atXOnUy2YwZM/bu3RsTEzN+/PsdlhRT6uLi4jTXgRkcHBwTE9OpU6cPPXnqEZ0cosjJUfkffdXNmzKKCvb2rpZAGk17l0FFjcOHq9xebvf1bWpmdiUzMzIpqZYfkUqhsBBSUhpYs26eDZiXl7dnzx6xWOyt1FW2bds2Doej6SGEsLCw1q1bP3z48Ndff9VcLdqhY1dCkQiuXoWkJOXt6DOKi122bqXRaC/nzq12y+fhAUTGyh49glOnlAtOpqX5HzpkamT0aPr0NrUuq2Oz4dtvP/KWYrFYeQe0oqIieb+8/Mvc3NyUlBQTE5OcnBydOpls/vz5YWFho0ePPlw12V0kEjk5OeXl5WlhCOHOnTt9+/al0Wh3794luJSk4XTpmbCgAPbuBbFY5UCINbduVcpk0zp1aqnS79+zp1abp+DlBYmJkJurKPBzcfna3f2P5OTJJ07ETZlCV9qL7f0mi8XFvMuXeSLRh3rkP9Qdr9C4cWOxWFxWVqY7IeRyuTExMQCwdOlSRWFMTExeXp6Xl9dAzfeW9ezZc968eaGhoZMmTUpKStKd/Rc/lc5cCXk8iIysubt2TklJ6y1bpBT11+zZ1S4y7dvD6NFabaGys2chMVG5gCcUum/f/qa01NvWtjGLpVhMXMdNFo2MjFTOYFE5lWX16tVnzpzp37//5cuXdWS22ooVK9asWfPll1+erponJJFI2rRp8/r16+PHjwcEBGihDWKxuHPnzqmpqYsWLdqwYYMWatQE3QghRUFExPtDOauTL2kf7+a2/6uvqn0jOJjkZmenTsGjRyplP9+8GXbvnsqEUiaDYWViYmViYmVvb2Vn96Hu+GbNmn38yDEOh9OxY8eCgoJt27bNmjVL/b/RJyotLXVwcHj37p18yE5euHPnzqCgoPbt26empmrtBLVHjx517dpVKpVeu3atb9++2qlUvXTjdvTFC3j3rub+9gVlZTsePaIBLOvVq9rrHRzIbjd47eVLdn6+R7Nq+5pcysjgCYWBHh5fu7vbVO1oaipfdmhsDOPHQ419QevO2to6MjLS399/8eLFAwYMID5fZOvWre/evevXr58igVKpdN26dQCwYsUKbZ5h6OXl9d133/30009Tpkx58uQJ2f0X60c3ekcTEmrd3z40Pl4okfi5uLhVH4YCos9FUql0+ubNXpGR17OyFIXXqqbUhfv6DnR29rK1bdmokali4W9lpcos8Hrw8/MLDAwUCASTJ0+WfuIZb+olEAg2b94MACtWrFAUHj169Pnz505OTlre5AIAVq5c2aVLl6ysrMWLF2u5arXQjRDm5NQsKxYKIxMTAeC7mj3dRDfAPnz48MvsbCcrK+VNLuRzCeZ362ZW6w4azZuDsRqOCvr9999btmwZHx8vX81ESlRUVGFhYdeuXfv16ycvoShq/fr1ALBs2TKGeld11oFi/8WoqKhz585pufaGq/8zIbeSG/Im5Cr/KpPG7GDSIdQ+1IZh0yu9V6pr6sd/sIKq4Eq43OdV3fGFhdyjR2tuQ8gTClmGhu1tbBK/+UblHd5Kpc1//rl+zW4giqI8PDxSUlJihg8PqlrBeP/Nm24xMRbGxq/nz7esOZ3F0BAmTKj3zoh5eXmmpqYWVSeBX7161cfHx8jIKCEhwV1lrpxWSCSS1q1bZ2dnnzp1atiwYfLC06dPDx8+3N7e/uXLl8bq+HNTD7/99tuiRYvs7OxSUlIaN25MpA31U/8/WmOzxnYz7ZbTMYdOo8eVxWWKM20YNgDAreQmC5OLKos4lRxuJZcr5XIrufJPiiRFXCm3VFoKAEZ9jCrKKz7y/gY0mkQqzeLx8svKmimNUK+6efOXuLiL/foROQXh5MmTKSkp9hYWgUrrlX6JiwOAbz/7rPYEduhQ7wReunRp/Pjx/v7+8sEAAOjfv//MmTPDw8MnTpyYkJDwCVtXqcmuXbuys7Pd3d2//PJLRaF8St2iRYtIJRAA5s+ff/r06Zs3b86ZM2d/9an/Oq6eV8IkQdL4rPFpHdJo8PeD3Dvpu17pvf5n979RmR87KtWQZshmsJ2/c6aX0uUTC20yMtiGhioHszQ2MfHdv//cixdftm17evzfh879fPPmj9evOzo6PnnyRHF90JoePXrEx8dvGTJkTteu8pIn+fleEREmhoaqU+oAwNAQHB1h7Nh6n+KUnp7u5eUlFAqPHTs2YsQIeaFAIPDy8nr+/PmKFSvkZ4NqjVQqbd++/YsXLw4dOqR49rt69eqAAQN0YUpdVlaWh4cHn88/fPjwaIIjWJ+oniHcW7z3QsmFA60OKBfKQ7jTYefS3KXWdGsbQxs2nc1msNkMtvwTa4a1DcPGgl4jOUeOwF9/1RyfyOXz3cLDi4XCHX5+U7285IWVMlnPHTsS3r6dNm2afF8Trbl8+fLAgQObmJpmhYSwquYrjjly5M+nT0O6dds0eHC1VxsaQt++0KNHA2eNbt68OSQkxMbGJjU1VXH8YHx8fO/evWk02u3bt7tW/TnQgj/++CMwMNDZ2Tk9PV2xjKhfv37Xr19fvXq1Luw3ERUVNX36dDabnZqa2qyZfmzLXf8Qni85f7BVtU2X5SH8x2fCWuTkwL59UNu49sGUlPHHjpkZGT2eMcO56kY/jcPpFBkplEjOnDnjq8UJ3J9//vnNmzfXDhiwtGrIJKO4uN3WrQY1p9Q1bw5ff62WRUwymWzAgAHXr18PCAg4fvy4onzZsmXr1q1zdnZ+/Pixdq4/FEW5u7unpqbu3LlzypQp8sL79+9369bNwsLi9evXOjKbx9fX99y5c8qzCHRcPW+TXJmuSYIkCtQ00N+iBTg41HrC0Tg3t9EdOpRVVEw+cUJW9ffCxdp6db9+ADB16tRCbfWU3rt37+bNm42YTOWze1ffuiWVyaZ4elZLII0Gfn7qWkZoYGCwd+9eS0vL2NjYQ4cOKcp/+uknd3f3jIwM5XECjYqNjU1NTW3RosWECRMUhatWrQKAOXPm6EgCASA6Orpx48ZnzpzRzv6LDVfPEHZmdW5h1OL73O8llIQCKq4s7nxpwzbeHDECTE3BwKDmTWm4r28zM7Pb2dnyM5jk5nXr9rmjY2Fh4YwZMxpUb539/PPPADCva9dGVenKKSnZn5xMNzBYVH1HNmjfHmxs1Fi1vb29/EzcmTNnvqna4d/Y2Hjv3r1GRka///77RZUtpzRDvl5h6dKlit6gJ0+enDt3jsVizZs3TwsNqCM7O7utW7eCxvZf5FZyA18F2qXYOaU6DcsY9kL84p30Xcdn9V/WWP9xwkOtDmVVZNmn2DumOm4s2NjWuGFzOExMIDgYbGxqHq7CZrF2+/vTAJZdufK06rpnQKPt8ve3MDaOjY09XH2/ek14/PjxhQsXTI2MlI/sXXfnToVUOq5jR9WVEyrze9Rh6tSpI0aMePfu3dSpUxVPEB4eHj/88ANFUdOmTePxeGqvVNnFixcTExObNm06depUReHq1aspipo+fbqNWv/oNNy4ceNGjx5dVlamif0Xx2aNdTRyzOmYk9kxc3HTxZnizAa+oW7MHVWgKEhKgrg4KCtTuSQGnTy589GjTra296ZNU+w4GvPwYfCpU1o4BWHkyJHHjh1b2KPHxqrFAQVlZa02bxZJJMmzZnWs6i8BAGjTBpT6ctWoqKioY8eOhYWFUVFRwcHB8sLKyspevXrdv39/ypQpGr376tOnz61bt9avX6+YlfLy5UsXFxcGg5GRkdG8eXPNVV0/XC63Y8eO+fn5YWFharxQf2RcoD69IQCgcyFUePsW9u8HpcnQZRUVnhERGcXFKhs9fXngwNnnzwcOHKi5UxDS0tI6dOhgaGCQOW+eXdXUxCWXL2+4cyegffvjKrO0pkxpyBzRj4uNjR0xYoSpqenjx48VRxFlZGR4enqWlZUpD2OoV2lp6ZAhQ9LS0l69eqWYnDllypTdu3dPnz49IiJCE5U2nHxXHiaTmZiYWJf9F0UykXxYm1PJkf+nPNAtLxnbeOwr8ataxwX+dSEEgOfPofqRd7ezs/vu2mVAo8VPm9a5agJ3YXm5W3h4YXl5dHT0tGnTNNGQiRMn7tu3b2aXLuFVPbHFQqFjWBhfLE4IDu6ifBFwdIRJkzTRBoVx48YdOnRIZa+xLVu2zJs3T2UYQ+1ycnJatGih+FwvzgYMCgrauXOnp6fnsWPHSkpK5KullXE4HHkhjU17HfP6H99wls2s4spitY0LAIBOhxAATp6Ex4+VC0IuXNh8757KYZ2xf/014vBhleuDumRnZ7du3RpksvRvv21lZSUv/N+NGz/duDGodesLKjvwBgaCk5N6G6BCsdfYb7/9tmDBAnkhRVFDhgy5ePGiv79/bGysRhsgN3v27PDw8MDAwL1792qhunorLS11cXEpKSkRCAQffyWDyWDEMxQj29YMa2uGtfKXbDrbmmFdUFkw6dWk9A7p/4HbUTmxGLZvVz6SSVRZ2Tkq6mlh4ZKePdf5+CjKxx49ejg1VRN7Uc6cOTMiImKSp+fuqq00yisqHMPCOAJB3JQp1Q4qbN4cNHMpVnH58uVBgwYZGRklJiYq9hp78+aNu7s7j8fbv3//eM08lCro19mAHTt2TE9Pt7Kyat68uWLzUuU104rCOg639n/Rv7tp9x9tf2TQGLfKbpXLyrubdv/3hhAAsrKg+t/ah3l53WJipDLZ9cmT+1Rl4J1I5BYe/qa0VL17Uebn5zs5OYlFouSZMztU3eatv3Nn6eXL3Vu0uBsUVO3VY8dCu3bqqvrjvvnmm+joaC8vr/v37yv2Gtu1a9fUqVMtLS2Tk5MV942asHjx4o0bN44YMeLYsWOaq0Utrly54uPjY21t/erVK1OVSYX1VVRZFPIm5ErpFaYB08PEY5P9JjaD3ZAQ6sZSpo9o1QqURgUAoJOt7dKePWUUNfnECX7VdhiWTOYOPz8ajbZ8+fLU1Hr+W9S0YcMGoVD4laurIoGiysqwe/cA4EeVRdw2NqDFhbabNm1q3br1o0eP1qxZoyicMmWKfBgjKChIc39b9etsQPkudQsXLlRXAgHAhmGz33F/gXvB646vTzmfcjZ2tqRb1juBoAchBIABA1QO6/zx888729ll8XhLlQ7rHOjsHOTlJRaLJ02aVOvGz5+KW1AQtX07ACxV2lFqx8OHeXy+l63tQGfnaq/u00dth5PWgamp6e7du+l0+i+//PJA6TjhiIiIJk2aXL58OSoqSi0VSSSS/Pz8p0+fxsXFxcbGRkdHjxo1Sl/OBrx3796NGzcaNWqktRkd9aMb21t8nKEhfPUVxMRA1agrw8BgT0CAd2RkxIMHw9q2HdKmjbw8bPDgG69eyfeibOgptrm5oogIv7Zt+SKRd1VPrEQq3Xj3LgB836dPteEQKytQOZtJ82rda8zGxiYyMjIgIGDhwoX9+vVrU/UvUysej1fzGBaVneAKCgpqDnaz2Wy92GJQPqVu7ty5ujOlrlY6/0yocO0a3LqlXCB/NrMzN0+ZNatx1XZ3N1+96rdnD93AIOHUKc+hQz+tispKEApBIIDXr+HSJflZaDKKUhzKvevRo6knT7a3sUmdNavaSd3Dh0PVIg9tEolE3t7ez549W758ufJ96YQJEw4cOODp6bl48WIej6dywJh8O9OysrK6VMFgMNjV5efnnz171srKKiUlRQfH6BWePHni5eXFYrGysrJ0bUKPCv0JoUwGMTGQl/d3AUV9sXt33OvXX7u771MapF548WJZRUXosGGmtrYwahRYWb1Pl0j098eyMuDzayn8MKlM1iE8PJ3DeX/8C0W9v/+0sIC5c2udfa4FiYmJP/74Y2RkpPKEIR6P5+TkZGJikqf0z1WT/NCVjx/M0rRp05q9zfL5QwMGDLh06ZKO7L9Y06hRo44ePbpgwQL5tFtdpj8hBICiIoiKgspKRUEWj+cREcEXi/8cNWrUh6ZEMBjKP1Jvh1NTxx496mRllT5nDkN5ke6QISpdR8Tl5eW1atWqoqLC19e3ZcuWKluYyg9maciuZEVFRW5ubgUFBdu3b9fNx633k5wMDXVzSp0KfXgmVLCxgb594epVRUErK6v1Pj4zz5yZdfZsbweHZrWO86gjgRRFrb9zBwCW9urFUFkm7+nZ8PdXr9DQULFY/NVXXx09elQT7y9/8vT391+0aFH//v0//uRJxK+//iqTyaZMmaL7CQT96B1V1rOnyszM6d7eQ9q04QgE0zW5gvPM8+cP8/Jszc0nqhyFTaeT3fqtJsUQwrJlyzRXi5+f34QJE8rLy4nvv1hTdnb2wYMH6XT6woULSbelTvTqSghV62UjIhTL8Gk0Wszw4W7h4afS0/c8fjypAdelSplMecc3rkBQJBDIt4G78PIlACzp2ZOpsp8fnQ5crtpP5G2IzZs38/n8IUOGaHoIYdu2bXFxcXfv3g0NDdWpDT/Xrl0rkUgmTpyoy5NalenVM6HCgwdQfXvJ/cnJXx8/3ojJTJ45U/XcGAAA4AmFuXw+TyTiCYWKj3l8vnJhYXm59ANrz+gGBgY02m4/v/EquwwaGsKgQaB0MBhZfD7fwcGBx+PdunWrlwaWNaq4cuXKwIEDjYyMHjx44Obmpunq6uL9JCexODk5uS4rJ3SBvl0J5ZycwMAAlAIzwd39ZHr6kadPB+7bN9jZmVN1NZMfzMKvcc5MregGBk1MTZV3fGObmNiYmrJNTB7k5m5/8GDehQv9nJyqPXkaGID6pmI0XHh4OI/H69OnjxYSCAADBgyQL2WaOHHi/fv3tb//Yk0bN24UCoUjR47UlwSCvl4Jc3Nhzx6oqLZtKUcg6LVzp1Qme1lcXPMnrExMbM3MrExMrJhM5Y925ua25ubyL5uamtI/sDchRVG+Bw6cf/FiWLt2p8aN+/sbDAbMmQNa33mxVoqzAS9evKiFk8nkysvLvby8Xrx48f333/9MaEdmheLiYkdHRz6fn5iY6K0ztyf/SD+vhLXtMGvNYj2bPftgSgpHIFC+mlmzWBYN3pFW8eR5Oj199+PHkxVPntbWOpJAAIiOjpafDeijtL5E0+QT6Pr06fPrr7/6+vpqc//FmsLCwvh8/tChQ/UogaCvV0KZDNasAQ11yilG4WuQP3laGBsnz5zpYGkJhoYwdqymFxDWkeJswNjYWH+tH2C8ZMmSDRs2uLi4PHz4kNRhnaWlpY6Ojlp7HlYjfRuikDMwgFatNDVh+sNvO8HdfVSHDqVi8dSTJykGAzp00JEEAsDevXtfv37dvn374cOHa7/2VatWubm5paWlEdz/V/483LdvX/1KIOhrCAGgb1/QxOk/TCY0bgzNmwOLVeve9fL9F69lZW3NyICq41CIk0ql8kORVq5cqc2zARUU+y9u2bLl+vXr2m+ASCTasmULVD+tTV/o5+2o3MmT8PRprft214LBACYTzM3B3ByYTDAx+fujolC+8akcRcG9e3DjBgBU6wEyMjqdljZ8714mk5mUlOSq9cUTtTp06NC4ceOcnJzS09O1fzKZwqpVq3744Qcix4TId9np1KlTYmKizk5n/RB9DqFUCn/8AW/ffjCHNBowGPDVV9C2bT3vXSUSSE+HjAwoLgYaDaytwdkZ2radGhy8a9cub2/v+Ph4xcJ2UiiK8vT0TE5O1txWV3Wk2H8xKChIcYyUFiieh0+cOOHn56e1etVFn0MIADIZXL0KDx6AVAoq4+xGRmBmBmPGgAZ2HystLXV3d3/9+rUunIJy6tQpPz8/e3v7jIwM4iN1aWlpnTp1EgqFx48fDwgI0E6lMTExwcHBrq6uKSkpRO7GG0jPQyj37h0kJkJaGpSUgFQKTCY0bw7e3tCuneZWu1+7ds3Hx4dOp8fHx5PtEO/Zs+fdu3fVu8VtQ4SFhc2fP79JkyYpKSma239RQSqVurq6Pn/+/MCBA+OUh3D1CIXqa+7cuQDg6uoqFApJteHy5csAwGaz+Xw+qTaokEqlX3zxBQAEBARooboDBw4AgLOzs0Qi0UJ1moAhrD+hUCifG7Vs2TJSbZAfV7xmzRpSDahVVlaWvGPm4MGDGq1IJpPJ56zGxMRotCKNwhA2SGJioqGhoYGBQVxcnPZrj4+PB4BGjRrxeDzt1/5x8o4ZS0vLnJwczdVy4sQJALC3txeLxZqrRdMwhA0lH5hycnLS/g3h0KFDAWDlypVarreO5B0zPj4+MplM7W9eWVlZUFAgX661efNmtb+/Nv0rOmaIqqys7N69e2Ji4uzZs+XH4mmH7m9kVFhY6ObmpnKMVF0IhUKVTd9q7gRXWFgolUqbNm1KUVRWVhaLxdLcL6JpGEI1ePbsmbe3t1gsPnv27JAhQ7RT6ejRo48cOaLjGxkpHyNlb2+vsu+b4nPlwtLS0rq8s4GBAZvN9vX1XbdunRb6YDUKQ6ge69atW7ZsWfPmzVNSUqyqzo3RnPT0dFdXV73YyEh+jJShoWEdd2RmMBi17kylODdC8bmmW641GEL1kMlkX3zxRVxcnHYOKpo8efKePXtmzJixfft2TdfVQMXFxbGxscHBwRRF1XuTxX83DKHaZGZmenh4lJWVHTlyZOTIkZqrKDs7u02bNrp/NqCysrKyOp559B+kf3N8dJaTk9O6desAYObMmQUFBZqraO3atRUVFRMmTNCXBAIAJvAj8EqoThRF+fr6nj9/fvjw4SdPnlTX28pkMkWXxsuXL2fMmCHfyEj3zwZEdYEhVLO3b9+6ubnxeLw9e/ZMnDjxH19fl+74oqKiSqUtjL28vFxcXOTTtdC/AIZQ/fbt2zdx4kQLC4sjR44YGRlxuVz5qeiKvni5oqKiEqVDiD+CRqMp9xaOGTNm/PjxerdqDn0IhlAjRowYcfHixX88Jx0AmEymSvdgzT7DJk2aEFyqizQNQ6gRRUVFqampc+fOVYx0qYx9yQt1/Nw8pB0YQoQIwyEKhAjDECJEGIYQIcIwhAgRhiFEiDAMIUKEYQgRIgxDiBBhGEKECMMQIkQYhhAhwjCECBGGIUSIMAwhQoRhCBEiDEOIEGEYQoQIwxAiRBiGECHCMIQIEYYhRIgwDCFChGEIESIMQ4gQYRhChAjDECJEGIYQIcIwhAgRhiFEiDAMIUKEYQgRIgxDiBBhGEKECMMQIkQYhhAhwjCECBGGIUSIMAwhQoRhCBEiDEOIEGEYQoQIwxAiRBiGECHCMIQIEYYhRIgwDCFChGEIESIMQ4gQYRhChAjDECJEGIYQIcIwhAgRhiFEiDAMIUKEYQgRIgxDiBBhGEKECMMQIkQYhhAhwjCECBGGIUSIMAwhQoRhCBEiDEOIEGEYQoQIwxAiRBiGECHCMIQIEfZ/YLIs8kZiey0AAAAASUVORK5CYII=
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<p>What about aromatic rings that have both a Cl and an aliphatic C attached?</p>
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<div class="prompt input_prompt">In [11]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">matches</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">getSharedRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmarts</span><span class="p">(</span><span class="n">sma</span><span class="p">)</span> <span class="k">for</span> <span class="n">sma</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'[a]-Cl'</span><span class="p">,</span><span class="s1">'[a]-C'</span><span class="p">)],</span>
<span class="n">rings</span><span class="o">=</span><span class="n">getAromaticRings</span><span class="p">(</span><span class="n">mol</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">matches</span><span class="p">)</span>
<span class="n">drawMolWithRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">matches</span><span class="p">)</span>
</pre></div>
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<div class="prompt"></div>
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<pre>[{0, 1, 3, 4, 6, 7}, {16, 10, 11, 12, 13, 15}]
</pre>
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<div class="prompt output_prompt">Out[11]:</div>
<div class="output_png output_subarea output_execute_result">
<img src="data:image/png;base64,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<p>What about just finding any rings (not just aromatic) that have both Cl and Br connected?</p>
<p>Here we just drop the <code>rings</code> argument to <code>getSharedRings()</code>, it will use all of the molecule's rings:</p>
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<div class="prompt input_prompt">In [12]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">matches</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">getSharedRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmarts</span><span class="p">(</span><span class="n">sma</span><span class="p">)</span> <span class="k">for</span> <span class="n">sma</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'[*]-Cl'</span><span class="p">,</span><span class="s1">'[*]-Br'</span><span class="p">)]))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">matches</span><span class="p">)</span>
<span class="n">drawMolWithRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">matches</span><span class="p">)</span>
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<pre>[{0, 1, 3, 4, 6, 7}, {15, 16, 17, 18, 19, 21}]
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<div class="prompt output_prompt">Out[12]:</div>
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<p>We can also find any rings that have a Cl, but <em>not</em> a Br:</p>
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<div class="prompt input_prompt">In [13]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">matches</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">getSharedRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmarts</span><span class="p">(</span><span class="n">sma</span><span class="p">)</span> <span class="k">for</span> <span class="n">sma</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'[*]-Cl'</span><span class="p">,)],</span>
<span class="n">excludeQueries</span><span class="o">=</span><span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmarts</span><span class="p">(</span><span class="n">sma</span><span class="p">)</span> <span class="k">for</span> <span class="n">sma</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'[*]-Br'</span><span class="p">,)]))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">matches</span><span class="p">)</span>
<span class="n">drawMolWithRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">matches</span><span class="p">)</span>
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<pre>[{10, 11, 12, 13, 15, 16}]
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<div class="prompt output_prompt">Out[13]:</div>
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6AGBjBpEtStq41eZGRk2NvbFxUVPX78uHVr/d6pWy/PhIcOHUpKSnJychoypIIi0D/++KM2GvX09Jw3b95vv/3m7+8fGxtbp04dbbRSseJiePiQeWDLgwfCwsKOX3xRJoEA0K2b1leyduwIL17AmzeqAwEeHmcSEsJevBh/5szF0aM/PgSSyWD7dujcGdzcNH5/uG7dOqlUOnToUH1PIOjj6KhSqfz9998BYNGiRWzdlhhatmyZh4dHYmLiwoW63aL93j0oKlJ9VySXB929CwA/q41IWVuDDh5msljg4wNlryl2ensL+Pzw16/3lL1mhpISuHkTNm2CsLCKC1JVS05Ozvbt2wFA1/8jtEP/QnjixIknT540atRoxIgROm7awMBg3759PB5v8+bNFy9e1FGrMhmUvcXdHR2dJpG41a/fp2nTMq/08tJRQQcLC+jdm3nA1tR0p7c3AMy4ePF1Tk6ZFyuVIJPBo0ewfTtUbhX/ZwUHB0skkr59+7Zr104jb0iW/oWQ3gth4cKFn5lvpR2tWrX6+eefKYqaMGGCqHT5rHZFRTFXr8sUinW3bwPAT926lZn9Y2kJLVvqoj+0tm2hbMWdIc7O37VuXVBS4n/6tLL8QINcDiIR/Pln+SpSVZWfn79582YAWLRoUQ3fqpbQsxCGhYVFRUXVr1+f4F4I8+fP79q1a2pq6qxZs7TemEIBt28zDxyMi0vOzXW2tvZRexzftatO63+yWODtDcbGzGPbBg60MzOLfPv2jwpHpxUKEArhwYMatrx58+bs7GwvLy8Cw2PaoWcDM7/99hsAzJ0717js/35dYrPZe/fudXV13bdvn7e3d/nhWY1JToazZ0EiUR1QUtTqyEgAWNSlS5mCS2Zm0KaNtrrxT0xNoV8/OHlSdcCCx9s9eHDfAwcWXLnSy8GhldoUAii9RWzfvjKXzUVFRWoTKrKzszMzM/fu3Qv/otMg6NcjiuvXr/fo0UMgECQnJ+t0cLIimzZtmj59urW1dVxcnE1ltnyoEoUCzp6F58+h7OyCo0+efHfsmL2l5Yvp07nM816/fqAqjK1jhw7By5fMpcMBZ8/uio5ua2t7l1kFQ8XQUDp8uMjY+NNLOkUi0fv37yv85bSxsRGLxU+fPrW3t9f2h9MNfToTrly5EgBmzpxJPIEAMHXq1LCwsIsXL06cOPH06dOafGuKgkOHICWlfG3fNZGRADC/c+cyCTQxAXd3TXagSpydP+xFUyqob98bycnR6ek+hw+3a9CAXj2cxVhMLK3cSczIyEhtiRn9sPfEiRORkZG+vr43b94kP6VeE/TmTHj//v0OHTqYmZklJyeXn1ZGRGpqqouLi0gkOnDgwOjRmtu4NyKiwura51+88D50qH6dOkkzZ5YpIfP111qZLFpJUVFw4YJa9bdzCQkjjh+XKZUVLtvnGRpaCgRqq8zKLz37p3Vnubm5Li4u7969CwwM1MVtufbpzZmQPg1OnTq1liQQABo2bLhhwwZ/f/9p06Z5eXk1atRIA29aWPhP9e3pu8F5nTuXSaCBARAdpn+fl8eSSm3KPjaMSk8vlMmaWlr6uroK+HwrRlFTgakpv39/6NCh2i1aWFjs3r27b9++Cxcu7NWrFz6s15GnT5+eP3+ez+fPnDmTdF/K8PPzGzZsmFgsHj9+vGauKR4/rvDwx70B1WZjWliQKutC+yUkxP733w8x1hkXlJRsvn8fAEJ8fJZ27z7tyy9HtG7dq2lTt/r1vzA35xsbg7V1DRvt3bv3+PHji4uL/fz8ZLraEkd79COEy5cvVyqVAQEB9coPuJG2detWGxubK1eu0HM4aur58wpPg2X2BmQqW31Ux9LT0/cdO1Ysl3vY2qoObn7wQFhY6NW4cVe1KXU0pRI0cckQFBTUrFmz6OhoesBcr+nBPeHr16+dnJxYLNarV680c8mnaWfOnPHx8TExMYmJianepjHFxcUfBuO3bcsWCrMKC7MZZQjTJJK4zExjLjflxx8t1M57LBYsWUJqh8BZs2YFBQUNb9XqyLcf6u0WyeUOwcHpEsnF0aP7lF/cwOVC+/Zqs22q7e+//+7WrRuLxbp9+3b79u018p5E6ME94apVq+Ry+YQJE2pnAgFg8ODBo0eP/vPPP/39/SMiIphDdpUssvhPw/EqdY2NixWK/JIS9RBCBTuZ6UZ2dvauXbsAYD5jWGhXdHS6ROJua9tbbUodjceD7t011YHOnTvPmDEjMDDQz88vKiqK4KPjGqrtZ0LVws1nz57Vop3JyhGJRG3atHn37p2Hh0fdunVVi4krWWTR0NDww2B8SYmAw7E2MbFibMki4PNXRkScf/HiaweHy76+ZcYMWSz46Scie4AtXrx41apVA5s3PzfqQ71dmULhuHHjm9zck999N0St5hqLBUZGMG5czW8ImYqLi9u1axcfHz9nzhx6Wr8+qu0hpJe0jxo16uDBg6T78hm//vprUFCQ2oRSHo9nWU754fj69et/WBFy5QrcucMsJEMTFha23rIlIz9/84ABU9QuvUg8osjLy2vcuHFubm7kuHGdS69Q9sTEjD9zxtnKKn7q1DITejgcsLKCESPAwkLjPYmJienQoYNCobh27VptrJVeCbX6cjQjI2P37t0sFmvBggWk+1LGtWvXBAKBq6sr82B4eLhIJPL19R09erRqMbFJVYv/ubnBvXvlQ2jF528fONDn8OG54eE9HRyaM2ur3bgBjo6g8Vk7n7Rp06bc3Nwe9vaqBCqUSnouwWIvL/UEDhsGTk5aOl27u7svWrRo2bJlY8eOjY2NNa3cJlO1Sq0eHQ0MDJRKpYMHD65VewMqFIqJEye6u7tfv35ddfDatWt///23QCDYsmVL79693d3dGzVqVOUEAoCVFTg6QkUTQQY7Ofm6uhbKZP6nTyuYKVUo4MQJDa7W+6zCwsLg4GAAWMwopXH86dMX2dkOlpbfqT24690bnJ21esG8ZMmS9u3bJyUlzZ07V3utaE/tDaFq4WZtm6p75MiRV69eOTg4MGfx03MJZs2apYEpdd7ewOdXOOC5sV+/Rubmd1JS1pVdWgFZWWq18bVqx44dmZmZHezsepTO3qQoau3ffwPAgi5d1KfUtW2r7f5wudx9+/YZGxvv2LHjr7/+0nZzGlf9e8JsefbMdzOvSq7yWLxWxq0C7QKtudZdErrEt/xMzckSqiRblp39Qn2jCLUyhCKRiM/nOzs7Pyxb2QEAUlNTGxLalZKiKFdX17i4uF27do0fP54+eO/ePU9PTzMzszdv3lho5LZHLIa9eyE/H+TydInExNDQrLTK9dXExF779xtyufcDAtowL0FZLPD318gjuE+TyWTNmjV7+/bt2ZEjvVu0oA+eS0gYFBpqZ2b26ocfjJgTenr2hM6dtd0l2vr16+fMmdOgQYO4uLi62ilsoyXVvycckTTC08QzpXUKh8WJyI9ILE605loDQLY8+7H0cZY8SygXZsuzsxXZ2fJs+j+yZFnZiuw8RR4AGHoZlhR8qgAhm82WyWRJSUnv37+vX/9jVcnly5evWLHi0qVL3TU32F15Z86ciYuLs7Oz8/X1VR1csWIFAEybNk0zCQQAc3OYMgVu3Ag/dGjU0aM+Tk67Bg2iVyp87eAwuX37LQ8ejDl16n5AgKHaZp2TJkENillVRkhIyNu3b9vY2AxklNKgp9TN6dSpTAKNjHQ5pW7WrFnnzp27efPm9OnTa/8wHlM1QxhVGPW25G24YzgLWADgVccLAHIVuQBwPf/6t4mf2irVgGUg4AqaftWUk8ehJ8irhjGYs+br1q07YMCAv/76KyAg4Ny5c6o/Tu88Qd+Fm2m/vKwaug73vHnzVIXbYmNjw8LC+Hz+jBkzNNmSgQH06tW4QYPC0NDd0dH9HR2Hlg76/96795XExNj373+9eXNFjx4f/4hIBFevQtkqrJqlUCjWrVsHAIu6dlU9KbmamHg7JUXA549Xu/L09AQj3e3XyWazQ0JCXF1dDx06NHjw4OHDh+us6RqqZgifFD3x4HvQCVTTyKBRd9PuVhwrawNrAUcg4AoEXAH9H1ZcK2uutRnHDADgXPk/qm7nzp0uLi7nz5/fs2fPuHHj6IOLFi0KCwu7f//+7Nmzd+7cWb3+V8/ly5fv3LlTr1491YUoAKxatYqiqO+//14bU+patGr125o1M2fOnHT+fJdGjejtB/kGBnt9fLqGhKyJjPRu3ryDnd3HP3D/Pjg6gtbqcIaGhr58+bJp3brfMEpp0Bv3/tixY5kpdQYGul/iaG9vv27duokTJ06ZMsXLy4t5AVWbVfOecH/O/gviC6H2ocyDuYrcytwTVkloaOioUaPq1Knz6NGjpqWTMJ4/f962bVupVHr+/PkBAwZosLlP6969+82bN1evXj1//nz6yOvXr1u0aMFms7U3pU6pVPbs2fP69etDnJ1PMjZaWXDlyprIyKZ16z6aNKnMb7+pKUyZoo1Z3RRFtWnTJj4+fs/gwWNLVzDee/fOc9cuMyOjN7NmlZnN06kT9Oql8T5UBn0BNXDgQOYFVG1WzdHRlryWUYVRFGj9Qf/IkSOHDx+en5/v7++vLB2Xd3Jyokcjx40bl8nYKkir7t69e/PmTXNzc+bevStXrlQoFGPHjtXelDo2m71//34LC4tTz54dZmy0sqx79zY2Nq9zchar7RcvkYB2KsGdOnUqPj7+C3Pz/zFKaSyPiACA6R06lEkghwPlN+7WlZ07d9atW5e+gCLVhyqpZgjb8dt9YfjFT2k/ySgZBVREfsSFvEpt01kNW7ZsqV+/fmRkJL0HE23GjBndu3fPzMzU2XbWv/76K92uqk5+SkrKwYMHORzOnDlztNq0nZ3d+vXrAWDy+fPv8vLog0Zc7v4hQww5nI337l0qu7YdYmPh2TONd4NerzC/c2fVaFDs+/d/vXjBNzCYobY+sG1bIPfQvEGDBps2bQKAGTNmvH79WuPvny3P9k32bRDXwCHewfu198vil7mK3NZPq7+ssfrPCQ/bH04qSbKLs2sS32RdxrrmRtoqOysQCPbu3UvPm3lSujsffRduZmZ26tSpI0eOaKlplUePHl28eNHExIS5Ze+aNWtKSkpGjhypg0mt48aNGzp0aG5R0bgzZ1R3EK716//crRsFMOHsWZFaSc/z56GgQIMduHTp0sOHD23q1BnHKKWx8tYtCmBiu3bWzGkJbDZ06qTBpquhwgsoTRmRNKKJYZOU1imJrRPn2sxNLE6s4RtWP4TWXOuDTQ5mtMl40/rN2aZnmxo1teBYaPaGUKVPnz5jx44tKioaM2aMahFnkyZN6PPDpEmT3r17p412VVasWEFR1KRJk6xL5x9nZGTs2bOHxWKp7g+1bdu2bfXq1bv8+vWu6GjVwfldunSws3uXlzc7PLzMqwsLQaN3RCt/+QUAZnfsaFxa7vVVTs7JZ8+MuNzZHTuWeWmbNtqYI1pVFV5A1Rz9XODXBr9yWBwA8Krj1cesTw3fs/bOmFETHBzctGnT6OhouvgvbcKECQMGDMjNzdXYwvaKPH/+/NSpU0ZGRsxdLtavXy+VSn18fHRWXsHa2nrbtm0AMOvSpVelVa65bPbBoUPrGBqGxMScVLsETUj4p3X6VSOX5x05okhLq2tsPInx3G9lRIRCqfR3c2vIfFDEYhE/DdIEAgE9eM68gPq0ImVRqiz1sfTxNcm1o6KjW7K2LE9fPvPdTN9k3/6v+n/5/EuHeIcTuSf+6blAtdX2VRRMkZGR3bp1Y7PZd+7cUdU/z8zMdHFxyczM3Llz54QJE7TR7pgxYw4cODB58uQtW7bQR3Jycpo0aSKRSO7fv6/j5aQjR448fPhw50aNbvr7c0oniP1x796MCxesTUzip0ypx7wy5HCgc2dwd6/+qamwEPbuBZEI5PIUsfgL1f2wWNzsjz8UFPV82rRmzOkprVqB2kaFRI0fP37Pnj1ubm4nTpwQi8Vq+1XSc7PogywB682uN599wynWU3LkOZp9LqBPIQSAmTNnBgcHq23WeerUqaFDh5qYmDx69EjjW9W9ffuWfs+EhARVoctffvll2bJlffr00d2OFKVUtcbW9+nzY+l1IEVR/Q4evPTqlY+T0ym1LTpYLOBwoFkzGDgQmPmUy0EqhaKij1/z80EiqeAgALOsKG1qWNiWBw98XV33DxlS5qfffw+MUhfE5eXlOTk5icXizy7s5PK43Dtc1ZNtK66VFdeK+a2AI7DiWmXIM/yS/RJaJTBPhv+tEBYVFbVr1+7Jkyfz5s1jXpeOGDHiyJEjnTt31ngtysmTJ2/bts3Pz48u/AwABQUFTZo0EQqFERERRCqxX758uU+fPoYczsPvv1dtlP1OLG6zbZtIKj04bNio8otOWCxgs8HGBhQKKCwEqbQmqy4y8vPtg4OLZLLHU6aU2am7eXMYObLab6slrVu3TkhIsLS0bNiwoap4KXN/WNXBSk6+//rl1x1NOi61XcplcW/l3ypQFnQ06fgfCiEAREdHe3p6KhSK69eve5UupdFSLcr37987ODgUFxc/fvy4VatW9MG1a9fOnz+/Y8eOt9WWMujQ999/v3PnTndb23uMKtchMTHjzpyx4PEeT578Rfn9hsudzaptbnj4utu3hzo7n2BMHgAAGD0aKqxqQc6VK1d69eplZWWVnJxcnZVlFcmSZ818N/NK3hUem+dq7LrBboOAK6hJCPVmYEalbdu28+fPVyqV/v7+ktJ9GuhalCwWa+HChfHxGhuh/f3336VS6bBhw1QJLCoqCgoKAoClS5dqqpVq2LBhQ7NmzWLS01fduqU6ONbdfaizc25R0fizZyv4t1VDCcyRSrdHRQHAQrWrABZLUzufaRA9qWP27NmaSiBo4bmA/oUQAJYuXdquXbukpCTm4wGN16LMzs7esWMHADBb2b17d3p6uru7e28NlQyrHhMTk71793I4nBUREQ9SU1XHtw0cWM/E5PLr1zuiojTSkEyheJ+f/yQzM+LNm1PPnu2Mivr26FFJcXHfZs3aNWhQ5qUUBRkZGmlUU+7evXvjxg21SU61kP5djtKePn3q4eFRXFwcFhbWr3TdQEFBgZub26tXr5YtW/bzzz/XsInU1NT58+dLJJIzZ87QR2QyWfPmzZOTk0+ePFnhTt06Nnv27MDAQGdr66jvv1c9vjv9/PmQw4dNDA1jJk50ZFbBKEcklaZJJKKiIpFUqvqaLpEwD2bk55ffbFDA509p3/7Xr75Sf0d3dxg0SHOfr6boSaQ//fQTPdup1tLXEELpvZnaIs6bN2/26NGDw+Hcv3/fzc2t5q0olUrVptwhISHjxo1zdnaOj4/X8U7dFSoqKvLw8Hj69OnCrl1Xff216vj/Tpw4FBfnVr/+3M6dRVIpXbw0WyoVFhYKSzdmyS/51GJOFS6bLShb9+19fn7YixeWxsZxkyerPyHs3BkY3SArNjbW3d2dz+cnJSVZa7TEm8bpcQiVSuVXX30VERExevToAwcOqI7Pnj07Pz8/MDBQg7cBAKBQKFq1apWQkKDh7V9q5uG9e0t9fbd7e9sx8iCSSh2Cg40NDNIZexuWZ2lsbFunjqWxsSWPp/rawNSUedDGxIRT7p+bb44ePfH0aU8Hh3Bm/UUjI/D2htKbZ+K+/fbb48eP//jjj/S0qtpMj0MIAElJSa6urhKJ5OjRo99++6mVxDV35MiRESNGODg4JCQkcLm1pkodRcGKFWrV2dIlEvvg4BKFYoCjYyNzcys+n3k2ozdmMa3BctusggKXrVsz8vO3Dhz4cQ4Nlwtz5uhyFe8nPH/+vFWrVgYGBq9fvyZVCaXyas0vU7XY29uvXbt28uTJU6ZM6dq1q/YWcVIURa+pnz9/fi1KIACwWMDnq+1IEXjnTrFcPqxly+PaWV1ubWJC11+cEx7+tb29o0AAXC64udWSBALAb7/9plQqx44dW/sTCHo6Oso0ceLEfv36CYXCiRMnaq+V8+fPR0dH29rajhkzRnutVFOLFszSbKpHCAu0WRF4sJPT/9q0KSgp+VB/kceDnj2111yVvH37NjQ0lMPhzJ49m3RfKqU2/aNeLSwWa9euXS4uLmfPnt23b5+fn1+130oul6tNLMzKyqLLwNHT0+bNm8cjug9ZxTw9ITZWdUUafPeupLi4n6Oj+iOEamOzgaLK73ixuX//iDdvbqekBD58OHf//tpzGly9erVMJhszZozG5zBqiX7fE6ocPHhw9OjR5ubmjx8/rnCRu0gkKr8rutrGLJmZmYqKdpYFAA6Hw2az9+7dO6p034Xa5fx5iI0FuVxSXNw4KEgkld4aN67LZxf7c7nA44GpKZiaAo8HxsYfv6oOmpgARcGVKxAVBUplmR15Wawrb970DgkxNDR88OBBLSnQXOEkp1ruXxJCABg+fPixY8datGjRt29fVRVTeoK85JODhCocDket4ptAIKArwT148GDr1q1WVlZxcXG1sXyQQgF790JGxprr1xdcueLVuPHNsWPVX8PlQuPG0L07GBsDnw9V3cNILIaoKEhIALEYKApMTKBxY/DwmLxy5bZt29zc3O7du2eo5WqLlTFnzpz169d/8803x44dI92Xyvr3hFAoFHbp0kWhULxSq/UAAAD0Huif3pvFxsbmnyZ/UxQ1YMCACxcueHt7nz17VssfpVrk8qITJxwCAtIlkku+vmV2JqMXUnTpAl5eGi9HX1BQ4O7u/vLly9rwTFy1xOzhw4cealsa12L/nhACgFKpDA0NFQqFahPkNVKeNC0tzcXFJScnJyQkxN/fv+ZvqHEbN2784Ycf3Js0iRo/nsXhAJsNCgWwWODsDF27widnz9TE7du3vby8WCxWZGRkhxpsRl9zP//88/Lly/v37x8WFkawG1VGoUr7888/AcDMzCw5OZl0X9SVlJQ0btwYAE6dOkUpFFRODpWWRonFlFKpg9bpnVicnJwKCwt10FyFxGKxpaUlANy6dYtUH6oHQ1g19JSAHj16KHXyy1159Ka5zs7OCoVC960XFRXRAzMzZ87Ufes0uhhct27dSHWg2jCEVZOVlUUPzPzxxx+k+/KRXC5v3rw5ABw8eJBUH2JiYgwNDdls9rVr13TfulQqtbW1BYDw8HDdt15DGMIqowdmeDzekydPSPflg9DQUABwcHCQyWQEu0EPzDRp0kQsFuu4aXq/xLZt29a2K5TKwBBWx9ixYwHAw8OjpKSEdF8opVLZpk0bANi5cyfZnshkMnpghi5+pzOq++HTp0/rsl1NwRBWh1gspv+vr1y5knRfKHq5o52dXXFxMem+UM+ePTM2NgaAkydP6qxRurRhy5YtidwP1xyGsJquXr3KZrMNDAwePnxItiedOnUCgKCgILLdUNmwYQMA1KtXLyMjQwfNqe6HDx06pIPmtAFDWH0//PAD/Q+wVCol1YfLly8DgEAgkEgkpPqgRqFQfPXVVwAwZMgQHTR36NAhAGjatCnZ++GawBBWn1QqpWcnLliwgFQf6O2K6T0Sa4+kpCR6gkRoaKhWG1IqlfSjkV27dmm1Ia3CENbIw4cPDQwM2Gx2RESE7lu/c+cOAJibm4tEIt23/mn0c0sLC4uUlBTttXL69Onacz9cbRjCmlq8eDH9eED3F4T9+/cHgCVLlui43Uqia2H16tVLG48N5HJ5RkYGvRtCcHCwxt9fl/5Vc0eJkMvlHTt2fPjw4dSpU+lt8XSj9hcyUm0TsmPHjoCAgMr/QalUqrbKrPzSM3rdmY2NDUVRSUlJfD5fex9E2zCEGlBh/UVto5du1fJCRsxtQuzs7JjbsKiWmzH/QygU5pXugvppbDZbIBAMGDBgzZo19Zil+PUQhlAz1qxZs2DBgoYNG8bFxdHTiLUqISGhZcuWelHIiN5GysDAoJIVmblcLnOjCNWSTuYiT5q2e64zGELNUNVf9PX13b9/v7ab8/f337dv36RJk7Zu3arttmooJyfn1KlTAQEBFEVVuKqTuaTz06s6/60whBqTmJjo6uqan59/7Nixb7S5R9/bt28dHR0VCsXz58/1pYxKfn5+Jfc8+g/S+2prtYeDgwO9W9vkyZMztLkrw+rVq0tKSv73v//pSwIBABP4CXgm1CSqtArGoEGDVDtY1JxSqVQNabx69WrSpEl0ISOd7dSNtApDqGGpqakuLi4ikWjfvn2VKVJameH4rKwsOWNPT3d3dycnJ3q6FvoXwBBq3oEDB8aMGWNmZnbs2DFDQ0NV0TfVWLyqEpxYLK7MG7JYLOZo4XfffTdq1CiWpks2IVIwhFoxdOjQS5cufXafdADg8Xhqw4Plxwzr1atXu2rvI43CEGpFVlZWfHz8Dz/8oHrSpfbsiz5oYWFBuqeIPAwhQoThIwqECMMQIkQYhhAhwjCECBGGIUSIMAwhQoRhCBEiDEOIEGEYQoQIwxAiRBiGECHCMIQIEYYhRIgwDCFChGEIESIMQ4gQYRhChAjDECJEGIYQIcIwhAgRhiFEiDAMIUKEYQgRIgxDiBBhGEKECMMQIkQYhhAhwjCECBGGIUSIMAwhQoRhCBEiDEOIEGEYQoQIwxAiRBiGECHCMIQIEYYhRIgwDCFChGEIESIMQ4gQYRhChAjDECJEGIYQIcIwhAgRhiFEiDAMIUKEYQgRIgxDiBBhGEKECMMQIkQYhhAhwjCECBGGIUSIMAwhQoRhCBEiDEOIEGEYQoQIwxAiRBiGECHCMIQIEYYhRIgwDCFChGEIESIMQ4gQYRhChAjDECJEGIYQIcIwhAgR9n/n3ognXbsSkQAAAABJRU5ErkJggg==
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<p>We aren't limited to just six membered rings, of course. Go back to the original query for aromatic rings with both Cl and Br attached:</p>
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<div class="prompt input_prompt">In [14]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">mol</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'Clc1[nH]c(Br)c(c2ccc(Cl)c(Br)c2)c1'</span><span class="p">)</span>
<span class="n">matches</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">getSharedRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmarts</span><span class="p">(</span><span class="n">sma</span><span class="p">)</span> <span class="k">for</span> <span class="n">sma</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'[*]-Cl'</span><span class="p">,</span><span class="s1">'[*]-Br'</span><span class="p">)],</span>
<span class="n">rings</span><span class="o">=</span><span class="n">getAromaticRings</span><span class="p">(</span><span class="n">mol</span><span class="p">)))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">matches</span><span class="p">)</span>
<span class="n">drawMolWithRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">matches</span><span class="p">)</span>
</pre></div>
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<pre>[{1, 2, 3, 5, 14}, {6, 7, 8, 9, 11, 13}]
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<div class="prompt output_prompt">Out[14]:</div>
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<img src="data:image/png;base64,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<p>What about aromatic rings that have both Cl and Br attached, but that don't contain a heteroatom?</p>
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<div class="prompt input_prompt">In [15]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">mol</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'Clc1[nH]c(Br)c(c2ccc(Cl)c(Br)c2)c1'</span><span class="p">)</span>
<span class="n">matches</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">getSharedRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmarts</span><span class="p">(</span><span class="n">sma</span><span class="p">)</span> <span class="k">for</span> <span class="n">sma</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'[*]-Cl'</span><span class="p">,</span><span class="s1">'[*]-Br'</span><span class="p">)],</span>
<span class="n">rings</span><span class="o">=</span><span class="n">getAromaticRings</span><span class="p">(</span><span class="n">mol</span><span class="p">),</span>
<span class="n">excludeQueries</span><span class="o">=</span><span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmarts</span><span class="p">(</span><span class="n">sma</span><span class="p">)</span> <span class="k">for</span> <span class="n">sma</span> <span class="ow">in</span> <span class="p">(</span><span class="s1">'[a;!#6]'</span><span class="p">,)]))</span>
<span class="nb">print</span><span class="p">(</span><span class="n">matches</span><span class="p">)</span>
<span class="n">drawMolWithRings</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">matches</span><span class="p">)</span>
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<pre>[{6, 7, 8, 9, 11, 13}]
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<div class="prompt output_prompt">Out[15]:</div>
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<p>Hopefully there's some useful stuff in here for you!</p>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com0tag:blogger.com,1999:blog-684443317148892945.post-16931778060428631432020-01-25T02:09:00.001-08:002020-01-25T02:09:11.772-08:00Trying out the new Tautomer Canonicalization code<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>During the 2018 RDKit Google Summer of Code (GSoC) project to port <a href="https://github.com/mcs07/MolVS">MolVS</a> to C++, doing the tautomer enumeration and canonicalization were stretch goals. Susan actually managed to complete the tautomer enumeration, but since canonicalization wasn't complete, we didn't publicize this particularly widely. As part of the work for the 2020.03 release, I implemented Matt's canonicalization scheme and we recently merged that into the RDKit core. Since this is a topic that may be contentious, and since making changes to the canonicalization algorithm post-release will have be done very deliberately, I'd like to collect some feedback before we do the release in a couple of months.</p>
<p>The implementation attempts to exactly duplicate what is currently being done in MolVS. Here's how Matt describes the process in the <a href="https://molvs.readthedocs.io/en/latest/guide/tautomer.html#tautomer-canonicalization">MolVS documentation</a>:</p>
<ol>
<li>Enumerate all possible tautomers using transform rules.</li>
<li>Use scoring system to determine canonical tautomer.</li>
<li>Canonical tautomer should be “reasonable” from a chemist’s point of view, but isn’t guaranteed to be the most energetically favourable.</li>
</ol>
<p>The scoring scheme:</p>
<ul>
<li>aromatic ring (defined by all bonds being aromatic) consisting entirely of carbons: 250 points </li>
<li>other aromatic rings : 100 points</li>
<li><p>a set of substructures are scored (if present). Here's the current (as of this writing) set of substructures and their associated scores (these are defined <a href="https://github.com/rdkit/rdkit/blob/master/Code/GraphMol/MolStandardize/Tautomer.cpp#L116">here</a>):</p>
<pre><code> {"benzoquinone", "[#6]1([#6]=[#6][#6]([#6]=[#6]1)=,:[N,S,O])=,:[N,S,O]", 25},
{"oxim", "[#6]=[N][OH]", 4},
{"C=O", "[#6]=,:[#8]", 2},
{"N=O", "[#7]=,:[#8]", 2},
{"P=O", "[#15]=,:[#8]", 2},
{"C=hetero", "[#6]=[!#1;!#6]", 1},
{"methyl", "[CX4H3]", 1},
{"guanidine terminal=N", "[#7][#6](=[NR0])[#7H0]", 1},
{"guanidine endocyclic=N", "[#7;R][#6;R]([N])=[#7;R]", 2},
{"aci-nitro", "[#6]=[N+]([O-])[OH]", -4}};</code></pre>
</li>
<li><p>one point is subtracted for each H attached to P, S, Se, or Te</p>
</li>
</ul>
<p>The highest scoring tautomer is selected. In the event of ties, the tautomer with the lexicographically smaller canonical SMILES is picked.</p>
<p>If this is something you feel strongly about, please try the code out and see what you think. If you see behavior you really don't like, or that you think is a bug, please add a comment to the associated issue in github: <a href="https://github.com/rdkit/rdkit/issues/2908">https://github.com/rdkit/rdkit/issues/2908</a> (preferred) or reply to the thread that I will create on the rdkit-discuss mailing list.</p>
<p>Remember that the goal of the exercise here is not to produce the "best" tautomer, but to produce a canonical one (always the same result for molecules which are tautomerically equivalent). We hope that this is also reasonable - in that it doesn't make a chemist's eyes burn - but that's not the primary goal.</p>
<h2 id="So-how-can-you-try-it-out?">So how can you try it out?<a class="anchor-link" href="#So-how-can-you-try-it-out?">¶</a></h2><p>This is C++ code, so you need an RDKit build done from the github master. I've done conda builds and made them available for people to try.</p>
<p>At the moment I've only built the beta version for python 3.7 on linux and windows. If you would like to do some testing on the Mac, let me know and I can do a build there too.</p>
<p>Here's how to setup a conda environment to use the beta:</p>
<pre><code>% conda create -n py37_tautomer_beta python=3.7 jupyter
% conda activate py37_tautomer_beta
% conda install -c rdkit/label/beta rdkit</code></pre>
<p>Ok, let's look at some examples:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="kn">import</span> <span class="n">Chem</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="kn">import</span> <span class="n">IPythonConsole</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="kn">import</span> <span class="n">Draw</span>
<span class="kn">import</span> <span class="nn">rdkit</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rdkit</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
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<pre>2020.03.1dev1
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<pre>RDKit WARNING: [11:00:33] Enabling RDKit 2020.03.1dev1 jupyter extensions
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit.Chem.MolStandardize</span> <span class="kn">import</span> <span class="n">rdMolStandardize</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">enumerator</span> <span class="o">=</span> <span class="n">rdMolStandardize</span><span class="o">.</span><span class="n">TautomerEnumerator</span><span class="p">()</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'Oc1c(cccc3)c3nc2ccncc12'</span><span class="p">)</span>
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<p>Get the canonical tautomer:</p>
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<div class="prompt input_prompt">In [5]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">enumerator</span><span class="o">.</span><span class="n">Canonicalize</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
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<div class="prompt output_prompt">Out[5]:</div>
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<p>The canonicalizer starts by enumerating a molecule's tautomers. If you want to see those, you can use the <code>Enumerate()</code> method:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">tauts</span> <span class="o">=</span> <span class="n">enumerator</span><span class="o">.</span><span class="n">Enumerate</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">(</span><span class="n">tauts</span><span class="p">)</span>
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<div class="prompt output_prompt">Out[6]:</div>
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LsOGFyQKxCERgj6mzxZ7wpgMEKuQBCmRgEAQGpohAAAIDWXU6NGI92+TUOHalkMBKKIiIjFixfHxcWpsjfkCjjkCgKHy0b46qtaluGdzs7O7u7u2NhYvQvxu7a2tsjIyKG6/n7HxsYeOXJErb0hV4EAudJSkOaqoaEhJSVF7yq8Y7PZbt68mZyc7O2GIUSUnk5jxpDVav/Qpk303HP2/05Pp/3774/esoXWrlVaq0IfffRRVlbWpk2bdK5DEy+88MKUKVP+8Y9/sOD/o2puYhYIkKsghVyprq2tbePGjRMnTjx27JjetXjhxIkTubm5P/rRj3p7e73d1n6N0Gql4mK161JbVVVVbm7u448/fuXKlbNnz3Z3d+tdkX91d3fX1NQ0NDQ8/fTTCxcurKqq0rsipQIzZsiV3hUphVyppbe3d/v27enp6a+//joRVVdX612RkGvXrv3iF7/Izs6uqKi4ffv2lStXvN2DvRE+/zxt23b/rCrQNDc3b9y4MTs7+/jx4/Hx8RaLpaKiIjIyUu+6/CsyMrKysrKkpCQpKen48ePz5s3Lz89vbGzUuy7fBVrMkCvkyh+CNFeHDh2aNWvWb3/729bW1qVLl1ZXV//ud7/TuygPurq6tm3blpmZuWfPniFDhhQWFp47dy49Pd3rHTHG0tJYWRnLy2MWC2OMvfAC27CBcWlp7KOPmENhIXv6aaale/fuWSyWmJgYIgoPDzeZTN99953TkVarNT8/n4hSUlLq6uo0rVKBuro6PhGfn59vtVqdjrlz547ZbB4yZAgRRUdHm83mrq4ujevsz2azFRcXE1FiYqL4Vm5ipj3kiiFXfhCkuaqrq1u9ejXvCOnp6fv27XMz0mOuNFNaWjpx4kRe9qpVqy5fvux0mEiu7jfCigqWnMy6ugY2wpgYFh9v/xcVpWkjLCsry8jI4N/n8uXLv/76a1cjP/jgg7S0NCKKiooiosjIyM2bN9++fVu7Wr13+/btzZs38/NEXnZaWtoHH3zganz/sKalpbkJq19VV1cvXLiQiEJCQk6fPi2+oZuYaQy56g+5Uksw5kr8ZMjbXPnVqVOneFqIaObMmZ999pmrkYK5ut8IGbOfVQ1ohLt3s2vX7P+efVajRlhbW7ty5Ur+fU6dOvXAgQNuRq5YscIxcs+ePSaTKTQ0lIj4pERvb68WFXvDZrPxiSkiMhgMRqPx3XffnTFjBv8ulixZcubMGVfbHjx40DFy6dKlbkaqrrm5Wclr6zRmNhu7dctfBT8IuXK1LXKlRDDm6sG03LhxQ3CkeK5UJ54Wr3L1vUbIz6o2bNBzarS1tdVkMoWFhRFRbGxsUVFRd3e3tyMdZwFENGvWrKNHj/q3aG+cOHFiwYIFvLa5c+d+8cUX/OM9PT3FxcWjRo3iJy9Go7GpqcnpHsRHqoVP+IwYMcLjhI8bTmP21lssOpqZzczfsyzIFXLlD0GaK1dpER8ZyGnxIVffa4SMsbw8FhenTyP07Tc2LCysoKDA6cgBM8jffvutv0oXc/36daPRaDAYiCglJaWkpMRmsw0Y09raWlhYGBERoeT3Sl1lZWWZmZmOCZ9z5875th+nMSsoYESMiE2ezEpLVau5P+SKIVd+EKS5EkmL+EjxXCkknhbfcjWwEVZUMCLPjfCtt5ivuXVOfGbm4MGD06dPF5zDuXv3blFR0bBhw4ho6NChhYWFHR0datYtprOzs6ioaPjw4UQUFRVVWFjo/nqAVzMtgiN9UFtb+5Of/ITvfMqUKfv371eyN1cxKy9n06fbD1vLlrGvvlJcdz/IVX/IlVqCMVfiafFfrnwgnhYluSIfKquvZ0OHsrAwVlDAlL8bFr9WL760aQDxkyB/EFza9KAB197dnwQJXqUX1P9Eb+TIkf470eN6elhxMUtIYETIlSjkyiPkykE8LRrkSpB4WpTnypdG2NLCfvMbFhrKiFh8PPvrX5lv13fFFyypss67srIyOzub/5zmzZtXUVHhS9HeOHXq1KJFi/hXdL+0yRXx1djiI93r6+srKSlJTEyk/0743Lx504f9+KClhZlMLCyMEbG4OGaxsJ4eX/aDXHmEXPkgSHMlnhYtc+WeeFrUypUvjZA7f549+qh95mHaNPbxx15sa7PZdu3axZchhYSEPPPMM+ILllyNFMFftdGjRzteNSV7c0PhQrgBbt26Jbi3GzduPPPMMyEhIUSUlJS0a9cur84lDx061H8xmFer2NWCXLmBXPlMwlzdvHlz/fr1/KeWmJj497//va+vz+lIvXLl1KFDhwQnk1XMle+NkCstZamp9nitWsW++UZ0w7y8PPJ1wZJC/HyN3xDDz9dUvDNUlYVwTokvLauurs7NzeUxEtx5fX290WjkOx83blxJSYkqNfsMuRoAuVKFVLmqq6uLjIxUfYGlIJ+Xwr744ose06J6rpQ2QsZYdzezWFhMDCNi4eHMZGLt7Z63qqmpefvtt5UsWFLo4sWLvs3guzFgwVJNTY3yfQ4guLTMZrO9/fbbIgUE2uNFHLq62Msvs2HDGBGbMaNqy5YtIgsHkCvfIFfuBWmudu3adeHCBVefDZxc9dfR0fHnP//Zr9POD1KhEXINDSw/nxkMjIiNGWN7551S36Lg7YIlhcrLyx966CH+c1q2bNlXvq4tu3DhgooL4dxTa2mZzWbbt2/f+PHj+TTO6tWrr169qnq1CvFc/eAHC4koOTl59+7dyJWfIFc+7Ae58igocqVaI+SqqlhODnv44XeIaPbs2ceOHfNq89LS0kmTJjlOH8QXLCnB7/JJSEig/97lc8ubh1K0tbVpuRDO4dq1a0pOQk+ePPnwww/zl3rOnDnHjx/3X6nKVVVV5eTk8GqRK79CrsQhV+ICPFcqN0LGmM3G9uz5J38wq8FgeOqpp65fv+5xK+ULlhRqaWlx3EccFxdnsVh6PK0t03EhnIMPS8saGhoKCgr4JfQxY8YUFxe7uoQeUPj54IQJExzng1euXPG4FXLlG+TKPeTKNwGbK/UbIXf37l2z2cyfzTp06FCz2dzZ2el0pLoLlhQ6f/78o48+yn9O06ZN++STT1yN7L9g6ZFHHtFlIRz34NKyxsZGpyO7u7stFgufxomIiDCZTAH+/OgHIVeaQa6cjkSuFArMXPmrEXL93w6PHTt2wNth/y1YUqi0tDQ1NdUx4/HN99eWBdpCOM7j0rIB0ziXLl3Sq1TlkCvNIFeOzyJXKgq0XPm3EXJHjhz54Q9/yL+lxYsX//vf/2aaLFhSgp+M9L8ztL29vf+CJX7aGCAL4RycLi37+uuvf/zjH/MPZmRk/Otf/9K7THUgV5pBrpArfwicXGnRCBljvb29O3bs4I+dDQ0N5X+Li3+fbt7O666hoSE/P5+fISYkJPAL1AaDIT8/v6GhQe/qXPrkk08czzpKS0vj0zijRo3asWNHAP7xICWQKy0hV8iVPwRCrjRqhBxfsBQZGblo0SItFyz57PDhw1u3bi0uLs7JyRk5cmR8fPycOXO2b9++devWw4cP612dO46lZYsWLQoPD/d2aVlwQa40g1zpXZRnfCnskCFDoqKiAn/hLqd7rjRthFxdXV1TU1NQ/P6YzWYiMpvNNpvt4sWLFy9etNlsjg/qXZ1nt27dampqqqur07sQLSBXmkGuApzNZuNvsLR8CrxyOuYqjDSXnp6u/RdVyGAwTJ48We8qvMbnRvgMz6CHXGkGuQpwfHa0/38EBR1zFaL9lwQAAAgcaIQAACA1NEIAAJAaGiEAAEgNjRAAAKSGRggAAFJDIwQAAKmhEQIAgNTQCAEAQGpohAAAIDU0QgAAkBoaIQAASE2Hh24HkV8OHz5vzpzJw4d7/CCAOOQK/O3AnDl6lxBM8I7QnckdHSurqiZ3dHj8IIA45Ar8bWVV1cqqKr2rCBpohAAAIDU0QgAAkBoaIQAASA2NEAAApIZGCAAAUkMjBAAAqaERAgCA1NAIAQBAamiEAAAgNTRCAACQGhohAABIDY0QAACkhkYIAABSMzDG9K4hgN29S3fvUnQ0RUd7+CCAOOQK/K2piYgoMVHvOoIDGiEAAEgNU6NiduygjAyKjKQJE2jrVurt1bsgGBSQK1CFqyClp9P+/feHbdlCa9fqUV+gQyMUsG0bvfQSvfoqNTfTu+/SP/9Jv/qV3jVB8EOuQBUIkmKYGvXkzh1KSqK33qKf/tT+kQsXKCuLzpyhrCw9C4OghlyBKtwHKT2dLBZatcr+qS1bqLGRdu/Wp9QAhneEnpw8ST099Pjj9z8ydSo99BAdOaJbSTAIIFegCgRJDWF6FxDwWlpo1CgK+f4ZQ1ISNTfrVBAMCsgVqMJjkH75SwoPt/93Zyf9/Oealhck0Ag9iY+nW7fIZvte1BobKSFBv5og+CFXoAqPQXr9dVq2zP7ff/oT3b2rdYXBAFOjnsydS+HhVFp6/yMXLlBNDT3yiG4lwSCAXIEqPAYpPp7GjrX/Gz5cjxKDAN4RejJsGP3v/9Kvf00REbRwIZ0/T+vW0VNPYUUDKIJcgSoQJDWgEQooLKSYGHr+ebp8mUaPprVryWzWuyYIfsgVqAJBUgy3TwAAgNRwjRAAAKSGRggAAFJDIwQAAKmhEQIAgNTQCAEAQGpohAAAIDU0QgAAkBoaIQAASA2NEAAApIZGCAAAUkMjBAAAqaERAgCA1NAIAQBAamiEAAAgNTRCAACQGhohAABIDY0QAACkhkYIAABSQyMEAACpoRECAIDU0AgBAEBqaIQAACA1NEIAAJAaGiEAAEgNjRAAAKSGRggAAFJDIwQAAKmhEQIAgNTQCAEAQGpohAAAIDU0QgAAkBoaIQAASA2NEAAApIZGCAAAUkMjBAAAqaERAgCA1NAIAQBAamiEAAAgNTRCAACQGhohAABIDY0QAACk9v+xEs6tp62+NAAAAABJRU5ErkJggg==
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<p>I find this function, which reorders the list of tautomers so that the canonical one is in the first position, really useful:</p>
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<div class="prompt input_prompt">In [7]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">reorderTautomers</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
<span class="n">enumerator</span> <span class="o">=</span> <span class="n">rdMolStandardize</span><span class="o">.</span><span class="n">TautomerEnumerator</span><span class="p">()</span>
<span class="n">canon</span> <span class="o">=</span> <span class="n">enumerator</span><span class="o">.</span><span class="n">Canonicalize</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">csmi</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolToSmiles</span><span class="p">(</span><span class="n">canon</span><span class="p">)</span>
<span class="n">res</span> <span class="o">=</span> <span class="p">[</span><span class="n">canon</span><span class="p">]</span>
<span class="n">tauts</span> <span class="o">=</span> <span class="n">enumerator</span><span class="o">.</span><span class="n">Enumerate</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">smis</span> <span class="o">=</span> <span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolToSmiles</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">tauts</span><span class="p">]</span>
<span class="n">stpl</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">((</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">smis</span><span class="p">,</span><span class="n">tauts</span><span class="p">)</span> <span class="k">if</span> <span class="n">x</span><span class="o">!=</span><span class="n">csmi</span><span class="p">)</span>
<span class="n">res</span> <span class="o">+=</span> <span class="p">[</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">stpl</span><span class="p">]</span>
<span class="k">return</span> <span class="n">res</span>
</pre></div>
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<p>So now we can display all the tautomers found for a molecule. The first one drawn is the canonical one:</p>
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<div class="prompt input_prompt">In [8]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">(</span><span class="n">reorderTautomers</span><span class="p">(</span><span class="n">m</span><span class="p">))</span>
</pre></div>
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<div class="prompt output_prompt">Out[8]:</div>
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<img src="data:image/png;base64,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<div class="prompt input_prompt">In [9]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">(</span><span class="n">reorderTautomers</span><span class="p">(</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'CN=c1nc[nH]cc1'</span><span class="p">)))</span>
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<div class="prompt output_prompt">Out[9]:</div>
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<img 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<div class="prompt input_prompt">In [10]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">(</span><span class="n">reorderTautomers</span><span class="p">(</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'CC=CO'</span><span class="p">)))</span>
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<div class="prompt output_prompt">Out[10]:</div>
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<p>As an aside, it's worth noticing that double bond stereochemistry is removed in all tautomers if the double bond is involved in the tautomerization:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">m</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'C/C=C(/O)F'</span><span class="p">)</span>
<span class="n">tauts</span> <span class="o">=</span> <span class="n">reorderTautomers</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Original SMILES:'</span><span class="p">,</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolToSmiles</span><span class="p">(</span><span class="n">m</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s1">'Tautomers (canonical first):'</span><span class="p">,[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolToSmiles</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">tauts</span><span class="p">])</span>
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<pre>Original SMILES: C/C=C(/O)F
Tautomers (canonical first): ['CCC(=O)F', 'CC=C(O)F']
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com2tag:blogger.com,1999:blog-684443317148892945.post-15750086909847503322020-01-21T00:33:00.002-08:002020-01-23T06:12:44.963-08:00Some thoughts on the performance of the RDKit cartridge[<b>EDITED on 23.01.2020</b> John Mayfield pointed out that the way I had constructed the set of 10 million molecules wasn't reproducible. This update fixes that, but it also changes the results.]<br />
<br />
It's been a while since I did a post about the cartridge! This one is a little bit ranty, but hopefully it's still useful.<br />
<h3>
Some background and disclaimers</h3>
<br />
I normally do performance testing on the cartridge using ChEMBL, but since that's "only" around 1.7 million compounds I wanted to try something a bit bigger. But how big? I don't see the point in shoving 100 million compounds (or a billion) into PostgreSQL (there's a mini-rant explaining this at the bottom of the post), so I went with 10 million. That's more than five times bigger than ChEMBL, but it's still not unimaginable that you'd actually have a relational schema with a molecule table this size that has data about most of those molecules in separate tables (again, see the mini-rant).<br />
<br />
My goal here is to get a sense for what kind of performance you can expect for doing "normal" chemical queries using a local PostgreSQL instance without having to spend time optimizing the database or hardware configuration. I generated all the numbers below on my Linux desktop - which is 4-5 years old at this point - using an out-of-the-box install of PostgreSQL 12 (the only configuration tuning was to increase the size of <span style="font-family: "courier new" , "courier" , monospace;">shared_buffers</span> to 4096M and <span style="font-family: "courier new" , "courier" , monospace;">work_mem</span> as described in the <a href="https://www.rdkit.org/docs/Cartridge.html">RDKit docs</a>). I was also using the machine for other things (like reading email or writing this post in Chrome) while these queries ran, so the numbers are really just ballpark estimates.<br />
<br />
So which molecules to use? Rather than agonizing overly much about this, I just grabbed a<a href="ftp://ftp.ncbi.nlm.nih.gov/pubchem/Compound/Weekly/2020-01-05/Extras/"> PubChem Compound SMILES dump</a> and took the first 10 million molecules from that.<br />
<br />
I hedged above by saying "normal" chemical queries. I have a longer rant about that topic that I will spare you the details of, but I generally think that including worst-case queries (i.e. things that return tens or hundreds of thousands of rows) in real-world benchmarking examples is pointless. It's definitely useful to understand the worst-case performance of a system, but that's not what I'm doing here. So my queries will often limit the number of rows returned to what I think is a "reasonable" number for interactive usage.<br />
<h3>
Creating the database</h3>
This tends to be time consuming, particularly creating the molecule index.<br />
Start by creating the database from the command line and loading all of the raw data:<br />
<br />
<pre>glandrum@otter:/tmp/pubchem_load$ createdb pubchem_compound
glandrum@otter:/tmp/pubchem_load$ psql -c 'create extension rdkit' pubchem_compound
CREATE EXTENSION
glandrum@otter:/tmp/pubchem_load$ psql -c 'create table raw_data (id SERIAL,cid integer, smiles text)' pubchem_compound
CREATE TABLE
glandrum@otter:/tmp/pubchem_load$ zcat CID-SMILES.gz | sed 's/\\/\\\\/g' | psql -c "copy raw_data (cid,smiles) from stdin with delimiter E'\t'" pubchem_compound
COPY 102397130
</pre>
<br />
Now create the molecule table and build the index:<br />
<br />
<pre>pubchem_compound=# alter table raw_data add primary key (cid);
ALTER TABLE
pubchem_compound=# \timing
Timing is on.
pubchem_compound=# select * into mols from (select cid,mol_from_smiles(smiles::cstring) m from raw_data order by cid limit 10000000) tmp where m is not null;
... lots of info about failing molecules deleted here
WARNING: could not create molecule from SMILES '[B+]12(CC3CC(C1)CC(C2)C3)NCC[O-]'
WARNING: could not create molecule from SMILES '[B+]12(CC3CC(C1)CC(C2)C3)NCCO'
SELECT 9999165
Time: 1722464.775 ms (28:42.465)
pubchem_compound=# create index molidx on mols using gist(m);
CREATE INDEX
Time: 3735718.147 ms (01:02:15.718)
</pre>
<br />
Add fingerprints and their index along with the primary keys we'll use to link things:<br />
<br />
<span style="font-family: monospace;"><span style="white-space: pre;">pubchem_compound=# create index fps_mfp2_idx on fps using gist(mfp2);
CREATE INDEX
Time: 155112.279 ms (02:35.112)
pubchem_compound=# alter table mols add primary key (cid);
ALTER TABLE
Time: 44271.680 ms (00:44.272)
pubchem_compound=# alter table fps add primary key (cid);
ALTER TABLE
Time: 27675.441 ms (00:27.675)</span></span><br />
<pre></pre>
Now let's do some queries.<br />
<h3>
Substructure queries</h3>
<div>
Note that all of the results below are done with a "warm" disk cache: I ran a substructure search and similarity search to try and ensure that at least parts of the indices are being cached by the operating system.</div>
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<br />
Picking the queries for a demo is always tricky, and can obviously completely slant the results one way or another, so I went to the ASAP page for J Med Chem and pulled stuff from there.<br />
<br />
Let's start with a query for parts of the "head" and "gate" from this paper: <a href="https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01912">https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01912</a><br />
<br />
<pre>pubchem_compound=# select * from mols where m@>'CC1=C(C=C(C=C1)C(N)=O)C#CC1=CN=CC=C1' limit 10;</pre>
<span style="font-family: monospace;"><span style="white-space: pre;"> cid | m </span></span><br />
<span style="font-family: monospace;"><span style="white-space: pre;">----------+-------------------------------------------------------------------------</span></span><br />
<span style="font-family: monospace;"><span style="white-space: pre;"> 11526637 | Cc1ccc(C(=O)Nc2cc(OC[C@@H]3CCCN3C)cc(C(F)(F)F)c2)cc1C#Cc1cnc2nc[nH]c2c1</span></span><br />
<span style="font-family: monospace;"><span style="white-space: pre;">(1 row)</span></span><br />
<span style="font-family: monospace;"><span style="white-space: pre;"><br /></span></span>
<span style="font-family: monospace;"><span style="white-space: pre;">Time: 5359.046 ms (00:05.359)</span></span><br />
Here's the one hit:<br />
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Now the core from: <a href="https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01427">https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01427</a><br />
<br />
<pre>pubchem_compound=# select * from mols where m@>'CC1=CC2=C(S1)C(=O)NC(C)=N2' limit 10; cid | m
----------+---------------------------------------------------
3758971 | CCOC(=O)C1CCCn2c1nc1cc(-c3ccccc3)sc1c2=O
10622152 | COc1ccc(NC(=S)Nn2c(C)nc3cc(-c4ccccc4)sc3c2=O)cc1
1040399 | COC(=O)[C@@H]1CCc2nc3cc(-c4ccc(OC)cc4)sc3c(=O)n21
1040400 | COC(=O)[C@H]1CCc2nc3cc(-c4ccc(OC)cc4)sc3c(=O)n21
3310123 | O=c1c2sc(-c3ccccc3)cc2nc2n1CCCCC2
4376566 | CCOC(=O)C1CCCn2c1nc1cc(-c3ccc(OC)cc3)sc1c2=O
10574247 | COc1ccccc1NC(=S)Nn1c(C)nc2cc(-c3ccccc3)sc2c1=O
11404362 | Cc1cc2nc(-c3ccccc3)n(-c3ccccc3)c(=O)c2s1
10716322 | Cc1cccc(NC(=S)Nn2c(C)nc3cc(-c4ccccc4)sc3c2=O)c1
661611 | COc1ccc(-c2cc3nc4n(c(=O)c3s2)CCOC4)cc1
(10 rows)
Time: 6.532 ms
</pre>
And the results:<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg1e3nPVksYGppQ-vSO5GQNmzhbarAvnSPPf0pM7OHZLhV34kogt4qX7fu-_htmPWzaqd1HkVF_8XGQVnPLjTGrQazG8kaf-AcKCNFcsh59v-Qur3tl6AIppVFyyLC1o5W52tFVrPGk3cI/s1600/img2.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="400" data-original-width="800" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg1e3nPVksYGppQ-vSO5GQNmzhbarAvnSPPf0pM7OHZLhV34kogt4qX7fu-_htmPWzaqd1HkVF_8XGQVnPLjTGrQazG8kaf-AcKCNFcsh59v-Qur3tl6AIppVFyyLC1o5W52tFVrPGk3cI/s640/img2.png" width="640" /></a></div>
<div class="separator" style="clear: both; text-align: center;">
</div>
<br />
<br />
And, finally, the core from: <a href="https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01684">https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01684</a><br />
<pre>pubchem_compound=# select * from mols where m@>'CN1C(=O)N(C)C2=C1C=NC(N)=N2' limit 10;
cid | m
----------+---------------------------------------------------------------------------------
10359733 | C=CCn1c(=O)n(CCCCCCCC)c2nc(N)nc(Cl)c21
10452174 | Nc1nc2c(c(=O)n1N)n(CCCl)c(=O)n2[C@@H]1O[C@H](CO)[C@@H](O)[C@H]1O
10048317 | Nc1nc2c(c(=O)n1N)n(C/C=C/c1ccccc1)c(=O)n2[C@@H]1O[C@H](CO)[C@@H](O)[C@H]1O
10454396 | [N-]=[N+]=NCC(O)Cn1c(=O)n([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c2nc(N)n(N)c(=O)c21
10476329 | Nc1nc2c(c(=O)n1N)n(Cc1ccccc1)c(=O)n2[C@@H]1O[C@H](CO)[C@@H](O)[C@H]1O
11428584 | C=CCn1c(=O)n([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c2nc(N)nc(OCC)c21
11453816 | C=CCn1c(=O)n([C@@H]2O[C@H](CO)[C@@H](O)[C@H]2O)c2nc(N)nc(OCN(C)C(=O)OCC)c21
9980347 | COc1ccc(Cn2c(=O)n([C@@H]3O[C@H](CO)[C@@H](O)[C@H]3O)c3nc(N)n(N)c(=O)c32)cc1
10085856 | Cn1c(=O)n2c3c1c(=O)nc(N)n3C[C@H]1O[C@@H]2[C@H](O)[C@@H]1O
9994808 | C=CCn1c(=O)n(CCCCO)c2nc(N)nc(Cl)c21
(10 rows)
Time: 107.934 ms
</pre>
<br />
And the first results:<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi7ijYIrT_PrLIP_T4ERjaTPE7E55jWWxzc51-ZVpehdNBFC_ZU-IDPdjkI5_Bi4EDGtGQPki3Mvdn9SL51H1rrIAW4Q37K4CsupyqgCqzyCu1zsUK1b1WxxkMSEC9GxaCh6a1HZegWBok/s1600/img3.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="400" data-original-width="800" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi7ijYIrT_PrLIP_T4ERjaTPE7E55jWWxzc51-ZVpehdNBFC_ZU-IDPdjkI5_Bi4EDGtGQPki3Mvdn9SL51H1rrIAW4Q37K4CsupyqgCqzyCu1zsUK1b1WxxkMSEC9GxaCh6a1HZegWBok/s640/img3.png" width="640" /></a></div>
<div class="separator" style="clear: both; text-align: center;">
</div>
<br />
These queries are, in general, pretty quick. I'm certainly likely to spend more time looking at the results than I am waiting for them to come back.<br />
<br />
One point it's worth making is that queries that don't return any results tend to be pretty fast. Here's an example of that:<br />
<br />
<pre>pubchem_compound=# select * from mols where m@>'O1C=NC2=C1N=C1N=CN=CC1=N2' limit 5;
cid | m
-----+---
(0 rows)
Time: 9.778 ms
</pre>
<div>
<br /></div>
</div>
<div style="font-family: "times new roman";">
</div>
Note that this is definitely not always true... the index does not work particularly well for queries that have massively repeating common substructures:<br />
<pre>pubchem_compound=# select * from mols where m@>'COCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCOCCOCOCOCOCOCCO' limit 5;
cid | m
-----+---
(0 rows)
Time: 150521.647 ms (02:30.522)</pre>
<div style="font-family: "times new roman";">
<br /></div>
<div style="font-family: "times new roman";">
Remember what I said about about worst-case performance? There ya go.</div>
<h3 style="font-family: "times new roman";">
Similarity queries</h3>
<div>
Now let's do some similarity searches using the molecule drawn in the graphical abstract as queries. For this blog post I'm going to use the cartridge's "neighbor" operator <span style="font-family: "courier new" , "courier" , monospace;"><%></span> to order the results and allow the N closest neighbors of each query molecule to be retrieved. Here's the simple SQL function that I use for doing that:</div>
<div>
<br /></div>
<div>
<pre>pubchem_compound=# pubchem_compound=# create or replace function get_mfp2_neighbors2(smiles text)
returns table(cid integer, m mol, similarity double precision) as
$$
select cid,m,tanimoto_sml(morganbv_fp(mol_from_smiles($1::cstring)),mfp2) as similarity
from fps join mols using (cid)
order by morganbv_fp(mol_from_smiles($1::cstring))<%>mfp2;
$$ language sql stable ;
</pre>
</div>
<div>
<br /></div>
<div>
That's adapted from the function <span style="font-family: "courier new" , "courier" , monospace;">get_mfp2_neighbors</span> that is in the <a href="https://rdkit.org/docs/Cartridge.html#loading-chembl">RDKit cartridge documentation</a>.</div>
<div>
<br />
Let's start with a query for the five nearest neighbors of balovaptan (https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01478):<br />
<br />
<div>
<pre>pubchem_compound=# select * from get_mfp2_neighbors2('CN1CC2=NN=C(C3CCC(CC3)OC3=CC=CC=N3)N2C2=CC=C(Cl)C=C2C1') limit 5;
cid | m | similarity
----------+-------------------------------------------------------------+--------------------
11237434 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(c3ccccn3)CC2)C1 | 0.7049180327868853
11237433 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(c3ccccn3)CC2)C1.Cl.Cl.Cl | 0.6935483870967742
10070061 | CN1CCc2cc(Cl)ccc2-n2c(nnc2C2CCN(c3ccccn3)CC2)C1 | 0.6212121212121212
11270862 | Cc1ccccc1CC(=O)N1CCC(c2nnc3n2-c2ccc(Cl)cc2CN(C)C3)CC1 | 0.5797101449275363
11442570 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(C(=O)c3c[nH]c4ccccc34)CC2)C1 | 0.5774647887323944
(5 rows)
Time: 239.879 ms
</pre>
</div>
<div>
<br /></div>
<div>
It's easy (and quick) to expand that to the ten nearest neighbors:<br />
<br /></div>
<div>
<pre>pubchem_compound=# select * from get_mfp2_neighbors2('CN1CC2=NN=C(C3CCC(CC3)OC3=CC=CC=N3)N2C2=CC=C(Cl)C=C2C1') limit 10;
cid | m | similarity
----------+-------------------------------------------------------------+--------------------
11237434 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(c3ccccn3)CC2)C1 | 0.7049180327868853
11237433 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(c3ccccn3)CC2)C1.Cl.Cl.Cl | 0.6935483870967742
10070061 | CN1CCc2cc(Cl)ccc2-n2c(nnc2C2CCN(c3ccccn3)CC2)C1 | 0.6212121212121212
11270862 | Cc1ccccc1CC(=O)N1CCC(c2nnc3n2-c2ccc(Cl)cc2CN(C)C3)CC1 | 0.5797101449275363
11442570 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(C(=O)c3c[nH]c4ccccc34)CC2)C1 | 0.5774647887323944
11486399 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(C(=O)CC3CC3)CC2)C1 | 0.5757575757575758
11349733 | CCCC(=O)N1CCC(c2nnc3n2-c2ccc(Cl)cc2CN(C)C3)CC1 | 0.5757575757575758
11305733 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(C(=O)c3cc4ccccc4[nH]3)CC2)C1 | 0.5694444444444444
11166614 | CN1Cc2cc(Cl)ccc2-n2c(nnc2C2CCN(C(=O)C3(C)CCCCC3)CC2)C1 | 0.5671641791044776
10028677 | CN1CCOC(CN2Cc3cc(Cl)ccc3-n3c(nnc3C3CCN(c4ccccn4)CC3)C2)C1 | 0.5616438356164384
(10 rows)
Time: 189.425 ms
</pre>
<div>
<br /></div>
</div>
<div>
Here are the first few of those, along with the similarity values:<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhplVlhab_EePmGmKxZzN5m5kmZ40TinpT5hBPg41R7U3Xi529xcwv5ad9qo0ZKY6BjLOu7alvD3gB1B_7vgzN8yWQy0pH3gTzohzaP8EEFwS0Noi02mAmeeXgVKpkMASlUXpBV7SZ0JSY/s1600/img4.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="400" data-original-width="800" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEhplVlhab_EePmGmKxZzN5m5kmZ40TinpT5hBPg41R7U3Xi529xcwv5ad9qo0ZKY6BjLOu7alvD3gB1B_7vgzN8yWQy0pH3gTzohzaP8EEFwS0Noi02mAmeeXgVKpkMASlUXpBV7SZ0JSY/s640/img4.png" width="640" /></a></div>
<div class="separator" style="clear: both; text-align: center;">
</div>
<br />
The five nearest neighbors for AZD7648 (<a href="https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01684">https://pubs.acs.org/doi/10.1021/acs.jmedchem.9b01684</a>) aren't particularly similar:<br />
<br />
<pre>pubchem_compound=# select * from get_mfp2_neighbors2('CN1C(=O)N(C2CCOCC2)C2=C1C=NC(NC1=CN3N=CN=C3C=C1C)=N2') limit 5;
cid | m | similarity
----------+-----------------------------------------------------------------------+---------------------
11504903 | Cn1c(=O)c(=O)n(C2CCCC2)c2nc(Nc3ccc(C(=O)NC4CCC(N5CCOCC5)CC4)cc3)ncc21 | 0.4166666666666667
11406590 | Cn1c(=O)c(Oc2ccc(F)cc2F)cc2cnc(NC3CCOCC3)nc21 | 0.3924050632911392
10230233 | Cc1cc(=O)n(C2CCCC2)c2nc(Nc3ccc(N4CCC(CCCN5CCOCC5)CC4)cc3)ncc12 | 0.3595505617977528
9801449 | Cc1nc2cnc(Nc3ccc(N4CCOCC4)cc3)nc2n(C2CCCC2)c1=O | 0.35443037974683544
10224126 | Cc1cc(=O)n(C2CCCC2)c2nc(Nc3ccc(N4CCOCC4)c(F)c3)ncc12 | 0.3488372093023256
(5 rows)
Time: 219.653 ms
</pre>
</div>
</div>
<div>
<br /></div>
<div>
As we can see:</div>
<div class="separator" style="clear: both; text-align: center;">
<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjm1g6LrbLKP7k6N9KHMRjUxgmlA3ssIGdonArwWTgjO052BxsbV-9ioldn7k_4ApVQJKekTq6sjikYdheNCgQg5RzWRvAlofA_xgTWHBcG1EW1v48g6btKLqxm6ibpXMJsa1RQ30y8jkI/s1600/img5.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="400" data-original-width="800" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjm1g6LrbLKP7k6N9KHMRjUxgmlA3ssIGdonArwWTgjO052BxsbV-9ioldn7k_4ApVQJKekTq6sjikYdheNCgQg5RzWRvAlofA_xgTWHBcG1EW1v48g6btKLqxm6ibpXMJsa1RQ30y8jkI/s640/img5.png" width="640" /></a></div>
<div class="separator" style="clear: both; text-align: center;">
</div>
<div>
<br /></div>
<div>
The performance of these queries isn't quite as good as what you can do with specialized open-source tools like <a href="http://chemfp.com/">chemfp</a>, <a href="https://github.com/schrodinger/gpusimilarity">gpusimilarity</a>, or <a href="https://github.com/chembl/FPSim2">FPSim2</a>, but I think it's reasonable for retrieving a set of neighbors that I'm then going to do (probably more time consuming) further analysis on.<br />
<b>Note</b> that the similarity queries were considerably slower in the original version of this blog post. This was likely due to me doing a bad job of warming up the disk cache.</div>
<h3>
Some technical details:</h3>
<h4>
Sizes of the tables and indices</h4>
<div>
<pre>pubchem_compound=# \d+
List of relations
Schema | Name | Type | Owner | Size | Description
--------+-----------------+----------+----------+------------+-------------
public | fps | table | glandrum | 964 MB |
public | mols | table | glandrum | 4236 MB |
public | raw_data | table | glandrum | 9091 MB |
public | raw_data_id_seq | sequence | glandrum | 8192 bytes |
(4 rows)
</pre>
<pre></pre>
<pre>pubchem_compound=# \di+
List of relations
Schema | Name | Type | Owner | Table | Size | Description
--------+---------------+-------+----------+----------+---------+-------------
public | fps_mfp2_idx | index | glandrum | fps | 1226 MB |
public | fps_pkey | index | glandrum | fps | 214 MB |
public | molidx | index | glandrum | mols | 4060 MB |
public | mols_pkey | index | glandrum | mols | 214 MB |
public | raw_data_pkey | index | glandrum | raw_data | 2193 MB |
(5 rows)
</pre>
</div>
<div>
<br /></div>
<div>
<pre></pre>
</div>
<h4>
Performance of the substructure indices</h4>
<div>
Here are results for the substructure queries executed above. For the purposes of this analysis I removed the limit on the query:</div>
<div>
<br /></div>
<div>
<pre>pubchem_compound=# explain analyze select * from mols where m@>'CC1=C(C=C(C=C1)C(N)=O)C#CC1=CN=CC=C1';
QUERY PLAN
---------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on mols (cost=2157.91..37897.03 rows=9999 width=398) (actual time=167.546..168.902 rows=1 loops=1)
Recheck Cond: (m @> 'Cc1ccc(C(N)=O)cc1C#Cc1cccnc1'::mol)
Rows Removed by Index Recheck: 328
Heap Blocks: exact=309
-> Bitmap Index Scan on molidx (cost=0.00..2155.41 rows=9999 width=0) (actual time=137.703..137.703 rows=329 loops=1)
Index Cond: (m @> 'Cc1ccc(C(N)=O)cc1C#Cc1cccnc1'::mol)
Planning Time: 0.119 ms
Execution Time: 169.178 ms
(8 rows)
pubchem_compound=# explain analyze select * from mols where m@>'CC1=CC2=C(S1)C(=O)NC(C)=N2';
QUERY PLAN
--------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on mols (cost=2157.91..37897.03 rows=9999 width=398) (actual time=436.998..510.558 rows=46 loops=1)
Recheck Cond: (m @> 'Cc1nc2cc(C)sc2c(=O)[nH]1'::mol)
Rows Removed by Index Recheck: 14
Heap Blocks: exact=55
-> Bitmap Index Scan on molidx (cost=0.00..2155.41 rows=9999 width=0) (actual time=436.902..436.902 rows=60 loops=1)
Index Cond: (m @> 'Cc1nc2cc(C)sc2c(=O)[nH]1'::mol)
Planning Time: 0.113 ms
Execution Time: 510.801 ms
(8 rows)
pubchem_compound=# explain analyze select * from mols where m@>'CN1C(=O)N(C)C2=C1C=NC(N)=N2';
QUERY PLAN
----------------------------------------------------------------------------------------------------------------------------
Bitmap Heap Scan on mols (cost=2157.91..37897.03 rows=9999 width=398) (actual time=5111.590..5113.136 rows=38 loops=1)
Recheck Cond: (m @> 'Cn1c(=O)n(C)c2nc(N)ncc21'::mol)
Rows Removed by Index Recheck: 10
Heap Blocks: exact=48
-> Bitmap Index Scan on molidx (cost=0.00..2155.41 rows=9999 width=0) (actual time=5081.767..5081.768 rows=48 loops=1)
Index Cond: (m @> 'Cn1c(=O)n(C)c2nc(N)ncc21'::mol)
Planning Time: 0.058 ms
Execution Time: 5113.296 ms
(8 rows)
</pre>
</div>
<div>
<br /></div>
<div>
All of those show that the index is doing a reasonably good job of pruning compounds - the worst case query (the first one) only ends up trying at 329 substructure queries (instead of 10 million!) to find the 1 actual match.</div>
<div>
<br />
<div style="-webkit-text-stroke-width: 0px; color: black; font-family: "Times New Roman"; font-size: medium; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; font-weight: 400; letter-spacing: normal; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
</div>
<br />
<h4 style="-webkit-text-stroke-width: 0px; color: black; font-family: "Times New Roman"; font-size: medium; font-style: normal; font-variant-caps: normal; font-variant-ligatures: normal; letter-spacing: normal; orphans: 2; text-align: start; text-decoration-color: initial; text-decoration-style: initial; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px;">
Warming up the disk cache</h4>
<div>
There are tons of ways to do this. Here's the approach I used for this post:</div>
<div>
<span style="font-family: monospace;"><span style="white-space: pre;">pubchem_compound=# select cid,mol_murckoscaffold(m) scaff into temporary table scaffs from mols order by cid desc limit 10;
SELECT 10
pubchem_compound=# select count(*) from mols cross join scaffs where mols.m@>scaffs.scaff;
count
--------
178828
(1 row)</span></span><br /><span style="font-family: monospace;"><span style="white-space: pre;">pubchem_compound=# select * into blah from fps order by cid desc limit 10;
SELECT 10
pubchem_compound=# select count(*) from fps cross join blah where blah.mfp2%fps.mfp2;
count
-------
401
(1 row)
</span></span></div>
That takes ~10 minutes to run for me.<br />
<h4>
Doing the queries and making the images</h4>
Since I'm not doing this one from a jupyter notebook, here's the code snippet I'm using in jupyter to do the substructure queries and display the results:<br />
<pre></pre>
<pre>q='CN1C(=O)N(C)C2=C1C=NC(N)=N2'
t1=time.time()
d = %sql postgresql://localhost/pubchem_compound select * from mols where m@>:q limit 10;
t2 = time.time()
print(f'{t2-t1:.2f} seconds')
ms = [Chem.MolFromSmiles(x) for x in [q]+[x[1] for x in d]]
ls = ['query']+[str(x[0]) for x in d]
Draw.MolsToGridImage(ms[:8],legends=ls,molsPerRow=4)
</pre>
And the equivalent thing for nearest-neighbor queries:<br />
<pre></pre>
<pre>q='CN1CC2=NN=C(C3CCC(CC3)OC3=CC=CC=N3)N2C2=CC=C(Cl)C=C2C1'
t1=time.time()
d = %sql postgresql://localhost/pubchem_compound select * from get_mfp2_neighbors2(:q) limit 10;</pre>
<pre>t2 = time.time()
print(f'{t2-t1:.2f} seconds')
ms = [Chem.MolFromSmiles(x) for x in [q]+[x[1] for x in d]]
ls = ['query']+[f'{x[0]} {x[2] :.2f}' for x in d]
Draw.MolsToGridImage(ms[:8],legends=ls,molsPerRow=4)</pre>
<pre></pre>
Both of these are using the fantastic <a href="https://github.com/catherinedevlin/ipython-sql">sql-magic for jupyter</a> from Catherine Devlin.<br />
<br />
<br />
<div style="font-family: "times new roman";">
</div>
<br />
<h3 style="font-family: "times new roman"; font-size: medium;">
Mini-rant:</h3>
</div>
PostgreSQL is a general purpose relational database, it lets you store collections of tables with links (relations) between them and makes it easy to do queries that combine data across tables. If you have data on a bunch of molecules that can't be logically captured in a single table (like ChEMBL!), a relational database is a great place to put that. If you have a single table with a bunch of columns (i.e. molecules and values or fingerprints computed from them), you may want to look to another type of system. If you have a huge number of rows that you are really only interested in doing chemical queries (substructure and similarity searches) against, and you care about performance, then you almost certainly should be using a specialized system. There are plenty to choose from!<br />
<br />greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com2tag:blogger.com,1999:blog-684443317148892945.post-64122946486892450612020-01-03T02:59:00.003-08:002020-01-03T02:59:53.667-08:00Similarity maps with the new drawing code<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>As part of the 2019.09 release we added a C++ implementation of the RDKit's similarity map functionality (<a href="https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-5-43">https://jcheminf.biomedcentral.com/articles/10.1186/1758-2946-5-43</a>). I forgot to mention this during the <a href="https://github.com/rdkit/UGM_2019/blob/master/Notebooks/Landrum_Whats_New.ipynb">"What's New"</a> bit of my presentation at the UGM, but I think it's worth calling attention to. So here's a quick blog post.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="k">import</span> <span class="n">Chem</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">Draw</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="k">import</span> <span class="n">SimilarityMaps</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">SVG</span>
<span class="kn">import</span> <span class="nn">io</span>
<span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">Image</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">import</span> <span class="nn">rdkit</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rdkit</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
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<pre>RDKit WARNING: [11:53:45] Enabling RDKit 2019.09.2 jupyter extensions
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<pre>2019.09.2
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<p>I start by using "classic" similarity map functionality to show why atorvastatin (Lipitor) and rosuvastatin (Crestor) are similar to each other when using the Morgan fingerprint.</p>
<p>Here are the two molecules:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">atorvastatin</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'O=C(O)C[C@H](O)C[C@H](O)CCn2c(c(c(c2c1ccc(F)cc1)c3ccccc3)C(=O)Nc4ccccc4)C(C)C'</span><span class="p">)</span>
<span class="n">rosuvastatin</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmiles</span><span class="p">(</span><span class="s1">'OC(=O)C[C@H](O)C[C@H](O)\C=C\c1c(C(C)C)nc(N(C)S(=O)(=O)C)nc1c2ccc(F)cc2'</span><span class="p">)</span>
<span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">((</span><span class="n">atorvastatin</span><span class="p">,</span><span class="n">rosuvastatin</span><span class="p">))</span>
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<p>To use the new drawing code, we create a <code>Draw2D</code> object and pass that to <code>SimilarityMaps.GetSimilarityMapForFingerprint</code>:</p>
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<div class="prompt input_prompt">In [3]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">show_png</span><span class="p">(</span><span class="n">data</span><span class="p">):</span>
<span class="n">bio</span> <span class="o">=</span> <span class="n">io</span><span class="o">.</span><span class="n">BytesIO</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">Image</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">bio</span><span class="p">)</span>
<span class="k">return</span> <span class="n">img</span>
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<div class="prompt input_prompt">In [4]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="n">Draw</span><span class="o">.</span><span class="n">MolDraw2DCairo</span><span class="p">(</span><span class="mi">400</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">maxWeight</span> <span class="o">=</span> <span class="n">SimilarityMaps</span><span class="o">.</span><span class="n">GetSimilarityMapForFingerprint</span><span class="p">(</span><span class="n">atorvastatin</span><span class="p">,</span> <span class="n">rosuvastatin</span><span class="p">,</span>
<span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">:</span> <span class="n">SimilarityMaps</span><span class="o">.</span><span class="n">GetMorganFingerprint</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">fpType</span><span class="o">=</span><span class="s1">'bv'</span><span class="p">),</span>
<span class="n">draw2d</span><span class="o">=</span><span class="n">d</span><span class="p">)</span>
<span class="n">d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">show_png</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[4]:</div>
<div class="output_png output_subarea output_execute_result">
<img src="data:image/png;base64,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<p>We can do the same thing with count-based fingerprints:</p>
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<div class="prompt input_prompt">In [5]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">d</span> <span class="o">=</span> <span class="n">Draw</span><span class="o">.</span><span class="n">MolDraw2DCairo</span><span class="p">(</span><span class="mi">400</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span> <span class="n">maxWeight</span> <span class="o">=</span> <span class="n">SimilarityMaps</span><span class="o">.</span><span class="n">GetSimilarityMapForFingerprint</span><span class="p">(</span><span class="n">atorvastatin</span><span class="p">,</span> <span class="n">rosuvastatin</span><span class="p">,</span>
<span class="k">lambda</span> <span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">:</span> <span class="n">SimilarityMaps</span><span class="o">.</span><span class="n">GetMorganFingerprint</span><span class="p">(</span><span class="n">m</span><span class="p">,</span> <span class="n">i</span><span class="p">,</span> <span class="n">radius</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">fpType</span><span class="o">=</span><span class="s1">'count'</span><span class="p">),</span>
<span class="n">draw2d</span><span class="o">=</span><span class="n">d</span><span class="p">)</span>
<span class="n">d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">show_png</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<div class="prompt output_prompt">Out[5]:</div>
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<img 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<p>The other <code>GetSimilarityMapFrom...</code> functions also accept the optional <code>draw2d</code> argument. Here's a visualization of the contributions made by the atoms in atorvastatin to its calculatied logp value:</p>
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<div class="prompt input_prompt">In [6]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">rdMolDescriptors</span>
<span class="n">ator_contribs</span> <span class="o">=</span> <span class="n">rdMolDescriptors</span><span class="o">.</span><span class="n">_CalcCrippenContribs</span><span class="p">(</span><span class="n">atorvastatin</span><span class="p">)</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">Draw</span><span class="o">.</span><span class="n">MolDraw2DCairo</span><span class="p">(</span><span class="mi">400</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span>
<span class="n">SimilarityMaps</span><span class="o">.</span><span class="n">GetSimilarityMapFromWeights</span><span class="p">(</span><span class="n">atorvastatin</span><span class="p">,[</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ator_contribs</span><span class="p">],</span><span class="n">draw2d</span><span class="o">=</span><span class="n">d</span><span class="p">)</span>
<span class="n">d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">show_png</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<div class="prompt output_prompt">Out[6]:</div>
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<img 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<p>And a couple more visualizations of various partial charge schemes.</p>
<p>Starting with Gasteiger-Marsilli charges:</p>
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<div class="prompt input_prompt">In [7]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">rdPartialCharges</span>
<span class="n">rdPartialCharges</span><span class="o">.</span><span class="n">ComputeGasteigerCharges</span><span class="p">(</span><span class="n">atorvastatin</span><span class="p">)</span>
<span class="n">chgs</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span><span class="o">.</span><span class="n">GetDoubleProp</span><span class="p">(</span><span class="s2">"_GasteigerCharge"</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">atorvastatin</span><span class="o">.</span><span class="n">GetAtoms</span><span class="p">()]</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">Draw</span><span class="o">.</span><span class="n">MolDraw2DCairo</span><span class="p">(</span><span class="mi">400</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span>
<span class="n">SimilarityMaps</span><span class="o">.</span><span class="n">GetSimilarityMapFromWeights</span><span class="p">(</span><span class="n">atorvastatin</span><span class="p">,</span><span class="n">chgs</span><span class="p">,</span><span class="n">draw2d</span><span class="o">=</span><span class="n">d</span><span class="p">)</span>
<span class="n">d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">show_png</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<div class="prompt output_prompt">Out[7]:</div>
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<p>And also the partial charges calculated with extended Hueckel theory (eHT) using Mulliken analysis:</p>
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<div class="prompt input_prompt">In [8]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">rdEHTTools</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">rdDistGeom</span>
<span class="n">mh</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">AddHs</span><span class="p">(</span><span class="n">atorvastatin</span><span class="p">)</span>
<span class="n">rdDistGeom</span><span class="o">.</span><span class="n">EmbedMolecule</span><span class="p">(</span><span class="n">mh</span><span class="p">)</span>
<span class="n">_</span><span class="p">,</span><span class="n">res</span> <span class="o">=</span> <span class="n">rdEHTTools</span><span class="o">.</span><span class="n">RunMol</span><span class="p">(</span><span class="n">mh</span><span class="p">)</span>
<span class="n">static_chgs</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">GetAtomicCharges</span><span class="p">()[:</span><span class="n">atorvastatin</span><span class="o">.</span><span class="n">GetNumAtoms</span><span class="p">()]</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">Draw</span><span class="o">.</span><span class="n">MolDraw2DCairo</span><span class="p">(</span><span class="mi">400</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span>
<span class="n">SimilarityMaps</span><span class="o">.</span><span class="n">GetSimilarityMapFromWeights</span><span class="p">(</span><span class="n">atorvastatin</span><span class="p">,</span><span class="nb">list</span><span class="p">(</span><span class="n">static_chgs</span><span class="p">),</span><span class="n">draw2d</span><span class="o">=</span><span class="n">d</span><span class="p">)</span>
<span class="n">d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">show_png</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
</pre></div>
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<div class="prompt output_prompt">Out[8]:</div>
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<img src="data:image/png;base64,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<p>As one final demo, I'll use the method to visualize the variability of the eHT charges with conformation for atorvastatin.</p>
<p>Start by generating 10 diverse conformers, calculating the charges for each, and plotting the average:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">mh</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">AddHs</span><span class="p">(</span><span class="n">atorvastatin</span><span class="p">)</span>
<span class="n">ps</span> <span class="o">=</span> <span class="n">rdDistGeom</span><span class="o">.</span><span class="n">ETKDGv2</span><span class="p">()</span>
<span class="n">ps</span><span class="o">.</span><span class="n">pruneRmsThresh</span> <span class="o">=</span> <span class="mf">0.5</span>
<span class="n">ps</span><span class="o">.</span><span class="n">randomSeed</span> <span class="o">=</span> <span class="mh">0xf00d</span>
<span class="n">rdDistGeom</span><span class="o">.</span><span class="n">EmbedMultipleConfs</span><span class="p">(</span><span class="n">mh</span><span class="p">,</span><span class="mi">10</span><span class="p">,</span><span class="n">ps</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s1">'Found {mh.GetNumConformers()} conformers'</span><span class="p">)</span>
<span class="n">chgs</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">conf</span> <span class="ow">in</span> <span class="n">mh</span><span class="o">.</span><span class="n">GetConformers</span><span class="p">():</span>
<span class="n">_</span><span class="p">,</span><span class="n">res</span> <span class="o">=</span> <span class="n">rdEHTTools</span><span class="o">.</span><span class="n">RunMol</span><span class="p">(</span><span class="n">mh</span><span class="p">,</span><span class="n">confId</span><span class="o">=</span><span class="n">conf</span><span class="o">.</span><span class="n">GetId</span><span class="p">())</span>
<span class="n">chgs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">res</span><span class="o">.</span><span class="n">GetAtomicCharges</span><span class="p">()[:</span><span class="n">atorvastatin</span><span class="o">.</span><span class="n">GetNumAtoms</span><span class="p">()])</span>
<span class="n">chgs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">chgs</span><span class="p">)</span>
<span class="n">mean_chgs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">chgs</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">std_chgs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">chgs</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">Draw</span><span class="o">.</span><span class="n">MolDraw2DCairo</span><span class="p">(</span><span class="mi">400</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span>
<span class="n">SimilarityMaps</span><span class="o">.</span><span class="n">GetSimilarityMapFromWeights</span><span class="p">(</span><span class="n">atorvastatin</span><span class="p">,</span><span class="nb">list</span><span class="p">(</span><span class="n">mean_chgs</span><span class="p">),</span><span class="n">draw2d</span><span class="o">=</span><span class="n">d</span><span class="p">)</span>
<span class="n">d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">show_png</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<pre>Found 10 conformers
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
"
>
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<p>That doesn't look hugely different from what we saw above.</p>
<p>To show the variability, plot the standard deviation of the charges across the 10 conformers:</p>
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<div class="prompt input_prompt">In [10]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="n">std_chgs</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">std_chgs</span><span class="p">),</span><span class="nb">min</span><span class="p">(</span><span class="n">std_chgs</span><span class="p">))</span>
<span class="n">d</span> <span class="o">=</span> <span class="n">Draw</span><span class="o">.</span><span class="n">MolDraw2DCairo</span><span class="p">(</span><span class="mi">400</span><span class="p">,</span> <span class="mi">400</span><span class="p">)</span>
<span class="n">SimilarityMaps</span><span class="o">.</span><span class="n">GetSimilarityMapFromWeights</span><span class="p">(</span><span class="n">atorvastatin</span><span class="p">,</span><span class="nb">list</span><span class="p">(</span><span class="n">std_chgs</span><span class="p">),</span><span class="n">draw2d</span><span class="o">=</span><span class="n">d</span><span class="p">)</span>
<span class="n">d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">show_png</span><span class="p">(</span><span class="n">d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">())</span>
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<pre>[0.01292592 0.00743163 0.01971312 0.01433223 0.01063085 0.01283745
0.01219511 0.00748435 0.01234194 0.01492494 0.00640842 0.02264999
0.02481744 0.00987842 0.00843151 0.01289956 0.00560632 0.00498617
0.00604883 0.005569 0.00452067 0.00796675 0.00718033 0.00581337
0.00702613 0.00634237 0.00699789 0.00539868 0.00521868 0.02412709
0.03131741 0.03709349 0.00657276 0.01175903 0.00674661 0.01012909
0.0050995 0.01139418 0.00831795 0.00581207 0.00960073]
0.03709348867462464 0.00452067345998171
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<div class="output_area">
<div class="prompt output_prompt">Out[10]:</div>
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<img 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<p>The deviations aren't huge (the printed array shows that), but the largest value is clearly the amide N.</p>
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<p>There's definitely a ToDo here to improve the way the negative contours are drawn so that the fact that they are being drawn with dashed lines is visible.</p>
<p><em>Note:</em> <a href="https://github.com/greglandrum/rdkit_blog/blob/master/notebooks/Similarity%20Maps%20with%20New%20Drawing%20Code.ipynb">The GitHub version of this post</a> uses SVG instead of PNG. The SVG currently generated by the RDKit for similarity maps is quite inefficient and blogger wasn't up to the challenge, so I used PNGs here.</p>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com0tag:blogger.com,1999:blog-684443317148892945.post-47509589554226386042020-01-01T04:38:00.002-08:002020-01-01T04:38:53.437-08:00Looking back at 2019I haven't done a "year in review" post before, let's see if it's interesting. My primary focus here is on the health/activity of the community.<br />
<br />
Some high-level things that happened in 2019:<br />
<br />
<ul>
<li>The usual two feature releases, along with six patch releases.</li>
<li>The RDKit UGM was in Hamburg this year. It was the largest we've had so far, with more than 110 attendees and a sizable waiting list.</li>
<li>The RDKit participated in the Google Summer of Code for the third time as part of the OpenChemistry organization. This year's project was an <a href="https://github.com/rdkit/neo4j">integration of the RDKit</a> into the open-source graph database Neo4J.</li>
<li>We added two new committers to the project this year: Dan Nealschneider and Paolo Tosco joined Brian Kelley and myself as committers to the main RDKit repo in GitHub.</li>
</ul>
<br />
Here are some stats for 2019:<br />
<br />
<ul>
<li>795 <a href="https://scholar.google.com/scholar?as_ylo=2019&q=rdkit&hl=en">Google Scholar hits</a> for "rdkit" (this isn't a perfect metric, but since there's still no RDKit paper it's the best we've got).</li>
<li>933 <a href="https://www.mail-archive.com/search?a=1&l=rdkit-discuss%40lists.sourceforge.net&haswords=&x=0&y=0&from=&subject=&datewithin=1y&date=2020-01-01&notwords=&o=relevance">rdkit-discuss posts</a></li>
<li>322 pull requests from 46 unique users</li>
<li>755 issues from 237 unique users (this includes questions, PRs, etc)</li>
<li>345 commits (the command I used for this was <span style="font-family: Courier New, Courier, monospace;">git log --before={2019-12-31} --after={2019-01-01} --oneline | wc -l</span>)</li>
<li>I did 15 RDKit blog posts. This is less than I'd like... I'm going to try and do more next year.</li>
</ul>
<br />
Here are the <a href="https://github.com/rdkit/rdkit/graphs/contributors?from=2019-01-01&to=2019-12-31&type=c">top committers</a> (in terms of number of commits) to the github repo in 2019:<br />
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<a href="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgOcVfDTYsO6JTmpn3d_cWlmibJOwJCodNBT4M7pOwizdgvdUu21PO1uF3kOn8HOAANGz3uJ5H6rNF6icoCzVYCWn00HKrP89aWrH9GyD6VkGWThtbbuTnOYpj4VF_duZrernQxpqGQZGQ/s1600/2019_contribs.png" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" data-original-height="1600" data-original-width="1474" height="320" src="https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgOcVfDTYsO6JTmpn3d_cWlmibJOwJCodNBT4M7pOwizdgvdUu21PO1uF3kOn8HOAANGz3uJ5H6rNF6icoCzVYCWn00HKrP89aWrH9GyD6VkGWThtbbuTnOYpj4VF_duZrernQxpqGQZGQ/s320/2019_contribs.png" width="294" /></a></div>
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General community metrics. These are not specific to 2019 and some are of questionable value:<br />
<br />
<ul>
<li>The <a href="https://sourceforge.net/p/rdkit/mailman/rdkit-discuss/">rdkit-discuss</a> mailing list has 365 subscribers</li>
<li>The <a href="https://www.linkedin.com/groups/8192558/">RDKit LinkedIn group</a> has 416 members</li>
<li>A search for "from rdkit import Chem" across github returns 10741 code results. It's non-trivial to convert this into something more useful, but a preliminary analysis of these results shows that they are spread across >450 repos with different names</li>
<li> <a href="https://twitter.com/RDKit_org">@RDKit_org</a>, the RDKit twitter account, has 1215 followers</li>
</ul>
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There are certainly things I've forgotten/overlooked. If you think of anything let me know... I can always come back and update the post.<br />
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<br />greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com0tag:blogger.com,1999:blog-684443317148892945.post-78120926700890296962019-12-13T21:11:00.001-08:002019-12-13T21:15:18.163-08:00Using the R-Group Decomposition Code<div class="cell border-box-sizing text_cell rendered">
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The RDKit's code for doing R-group decomposition (RGD) is quite flexible but also rather "undocumented". Thanks to that fact, you may not be aware of some of the cool stuff that's there. This post is an attempt to at least begin to remedy that.<br />
We'll look at a number of difficult/interesting problems that arise all the time when doing RGD on real-world datasets:<br />
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<li>Handling symmetric cores</li>
<li>Handling stereochemistry</li>
<li>Handling sidechains that attach to the core at more than one point</li>
<li>Handling multiple scaffolds or variable scaffolds</li>
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Unfortunately, this is one of those posts that completely choke blogger. Rather than doing a bunch of editing to find the magic "just right" amount of content that I can include and still post, I will just include the nbviewer version as an iframe and point you to the original notebook for this post in github: <a href="https://github.com/greglandrum/rdkit_blog/blob/master/notebooks/RGroupEdgeCases.ipynb">https://github.com/greglandrum/rdkit_blog/blob/master/notebooks/RGroupEdgeCases.ipynb</a>
or here's <a href="https://nbviewer.jupyter.org/github/greglandrum/rdkit_blog/blob/master/notebooks/RGroupEdgeCases.ipynb">the nbviewer link</a><br />
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Here's the nbviewer iframe:<br />
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com0tag:blogger.com,1999:blog-684443317148892945.post-18441559884495520442019-11-29T02:41:00.003-08:002019-11-29T02:41:31.579-08:00A buried data treasure<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>This is a relatively short one because I just wanted to point people to an older (thus buried) dataset from some former colleagues that I've found really useful in the past but that I think a lot of folks aren't aware of.
The dataset is in the supplementary material to this paper: <a href="https://pubs.acs.org/doi/abs/10.1021/jm020472j">https://pubs.acs.org/doi/abs/10.1021/jm020472j</a>
"Informative Library Design as an Efficient Strategy to Identify and Optimize Leads: Application to Cyclin-Dependent Kinase 2 Antagonists" by Erin Bradley <em>et al</em>. The paper itself is worth reading, but the buried treasure is the Excel file in the supplementary material, which contains SMILES and measured data for >17K compounds. There's also a very useful PDF which explains the columns in that file.</p>
<p>What's very cool is that the compounds are a mix of things from a small general-purpose screening library and compounds purchased or synthesized for a med chem project. I'm not aware of any other public datasets that have this type of information.</p>
<p>Let's look at what's there.</p>
<b>Note:</b> This is another one of those posts that blogger couldn't quite deal with, so I did some editing here. There's a bit more in the <a href="https://github.com/greglandrum/rdkit_blog/blob/master/notebooks/A%20buried%20data%20treasure.ipynb">jupyter notebook in github</a>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="k">import</span> <span class="n">Chem</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">PandasTools</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">Draw</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="k">import</span> <span class="n">IPythonConsole</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">rdFMCS</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">rdkit</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rdkit</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
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<pre>RDKit WARNING: [11:36:05] Enabling RDKit 2020.03.1dev1 jupyter extensions
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<pre>2020.03.1dev1
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<p>Start by reading in the file and adding a molecule column:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_excel</span><span class="p">(</span><span class="s1">'../data/jm020472j_s2.xls'</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<th></th>
<th>Smiles</th>
<th>mol_name</th>
<th>cdk2_ic50</th>
<th>cdk2_inhib</th>
<th>scaffold</th>
<th>sourcepool</th>
<th>cdk_act_bin_1</th>
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<th>0</th>
<td>C#CC(/C=C/C)OC(=O)c(ccc1)c(c1)C(=O)O</td>
<td>mol_1</td>
<td>None</td>
<td>23.3</td>
<td>Scaffold_00</td>
<td>divscreen</td>
<td>0</td>
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<th>1</th>
<td>C#CC(C)(C)N(CC1C)CC\C1=N\OC(=O)c2cc([N+]([O-])...</td>
<td>mol_2</td>
<td>None</td>
<td>20.5</td>
<td>Scaffold_00</td>
<td>divscreen</td>
<td>0</td>
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<th>2</th>
<td>C#CC(C)(C)N(CN1)CNC1=S</td>
<td>mol_3</td>
<td>None</td>
<td>20.7</td>
<td>Scaffold_00</td>
<td>divscreen</td>
<td>0</td>
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<th>3</th>
<td>C#CC(C)(C)N(CN1)C\N=C/1SC</td>
<td>mol_4</td>
<td>None</td>
<td>1.9</td>
<td>Scaffold_00</td>
<td>divscreen</td>
<td>0</td>
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<th>4</th>
<td>C#CC(C)(C)NC(=O)CN(c(c1)cccc1)[S](=O)(=O)c2ccc...</td>
<td>mol_5</td>
<td>None</td>
<td>19.8</td>
<td>Scaffold_00</td>
<td>divscreen</td>
<td>0</td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">PandasTools</span><span class="o">.</span><span class="n">AddMoleculeColumnToFrame</span><span class="p">(</span><span class="n">df</span><span class="p">)</span>
<span class="n">df</span> <span class="o">=</span> <span class="n">df</span><span class="o">.</span><span class="n">drop</span><span class="p">(</span><span class="s1">'Smiles'</span><span class="p">,</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
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<p>The "sourcepool" column relates to which kind of compound it is:</p>
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<li><code>divscreen</code> is from the screening library</li>
<li><code>simscreen</code> are vendor compounds picked by chemists using similarity screening</li>
<li><code>synscreen</code> are compounds made and screened during the course of the project</li>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'sourcepool'</span><span class="p">)[</span><span class="s1">'mol_name'</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
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<pre>sourcepool
divscreen 13359
simscreen 951
synscreen 3240
Name: mol_name, dtype: int64</pre>
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<p>Another interesting column is the <code>scaffold</code>, which contains human-assigned scaffolds for the compounds</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">sourcepool</span><span class="o">==</span><span class="s1">'divscreen'</span><span class="p">]</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'scaffold'</span><span class="p">)[</span><span class="s1">'mol_name'</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
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<pre>scaffold
Scaffold_00 12280
Scaffold_01 32
Scaffold_02 111
Scaffold_03 57
Scaffold_04 461
Scaffold_05 37
Scaffold_06 16
Scaffold_07 15
Scaffold_08 103
Scaffold_09 88
Scaffold_11 7
Scaffold_12 109
Scaffold_18 2
Scaffold_20 41
Name: mol_name, dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">sourcepool</span><span class="o">==</span><span class="s1">'simscreen'</span><span class="p">]</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'scaffold'</span><span class="p">)[</span><span class="s1">'mol_name'</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
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<pre>scaffold
Scaffold_00 460
Scaffold_01 90
Scaffold_03 2
Scaffold_04 91
Scaffold_05 18
Scaffold_08 216
Scaffold_10 37
Scaffold_11 8
Scaffold_12 29
Name: mol_name, dtype: int64</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">synscreen</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">sourcepool</span><span class="o">==</span><span class="s1">'synscreen'</span><span class="p">]</span>
<span class="n">synscreen</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'scaffold'</span><span class="p">)[</span><span class="s1">'mol_name'</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
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<pre>scaffold
Scaffold_01 204
Scaffold_02 106
Scaffold_04 78
Scaffold_05 265
Scaffold_06 273
Scaffold_07 76
Scaffold_08 13
Scaffold_09 344
Scaffold_10 502
Scaffold_12 61
Scaffold_13 17
Scaffold_14 12
Scaffold_15 11
Scaffold_16 269
Scaffold_17 157
Scaffold_18 38
Scaffold_19 256
Scaffold_21 558
Name: mol_name, dtype: int64</pre>
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<p>Let's look at some compounds:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">IPythonConsole</span><span class="o">.</span><span class="n">drawOptions</span><span class="o">.</span><span class="n">bondLineWidth</span><span class="o">=</span><span class="mi">1</span>
<span class="n">IPythonConsole</span><span class="o">.</span><span class="n">ipython_useSVG</span> <span class="o">=</span> <span class="kc">True</span>
<span class="n">PandasTools</span><span class="o">.</span><span class="n">FrameToGridImage</span><span class="p">(</span><span class="n">synscreen</span><span class="p">[</span><span class="n">synscreen</span><span class="o">.</span><span class="n">scaffold</span><span class="o">==</span><span class="s1">'Scaffold_18'</span><span class="p">],</span><span class="n">molsPerRow</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span><span class="n">maxMols</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
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<pre>/scratch/RDKit_git/rdkit/Chem/Draw/IPythonConsole.py:188: UserWarning: Truncating the list of molecules to be displayed to 8. Change the maxMols value to display more.
% (maxMols))
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<path class="bond-8" d="M 58.9721,78.843 L 53.8322,73.8653" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 53.8322,73.8653 L 48.6923,68.8875" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 46.6123,63.0885 L 48.4529,55.7508" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 48.4529,55.7508 L 50.2935,48.4132" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-11" d="M 110.377,84.5764 L 115.367,89.4088" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-11" d="M 115.367,89.4088 L 120.357,94.2412" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 118.585,93.7967 L 116.744,101.134" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 116.744,101.134 L 114.904,108.472" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 122.129,94.6857 L 120.288,102.023" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 120.288,102.023 L 118.448,109.361" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-13" d="M 120.357,94.2412 L 127.719,92.1414" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-13" d="M 127.719,92.1414 L 135.082,90.0417" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-14" d="M 140.769,91.9847 L 145.909,96.9625" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-14" d="M 145.909,96.9625 L 151.049,101.94" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-15" d="M 151.049,101.94 L 168.618,96.9297" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-16" d="M 168.618,96.9297 L 181.741,109.639" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-17" d="M 168.618,96.9297 L 173.063,79.2095" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="82.5803" y="107.307"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="73.9017" y="76.8773"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="51.8879" y="99.6081"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="43.0046" y="69.1783"><tspan>O</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="101.955" y="84.5764"><tspan>NH</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="113.068" y="115.006"><tspan>O</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="135.082" y="92.2755"><tspan>O</tspan></text>
<path class="bond-0" d="M 230.989,93.1937 L 229,75.0331" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-19" d="M 230.989,93.1937 L 247.712,100.551" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 229,75.0331 L 243.732,64.2298" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-2" d="M 243.732,64.2298 L 260.455,71.5872" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 260.455,71.5872 L 261.283,79.145" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 261.283,79.145 L 262.111,86.7029" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 259.805,91.683 L 253.758,96.117" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 253.758,96.117 L 247.712,100.551" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 265.083,90.9089 L 272.125,94.007" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 272.125,94.007 L 279.167,97.1051" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 279.167,97.1051 L 279.995,104.663" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 279.995,104.663 L 280.823,112.221" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 283.047,98.9745 L 283.627,104.265" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 283.627,104.265 L 284.206,109.556" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-20" d="M 279.167,97.1051 L 285.213,92.6711" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-20" d="M 285.213,92.6711 L 291.26,88.237" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-7" d="M 283.795,116.427 L 290.837,119.525" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-7" d="M 290.837,119.525 L 297.878,122.623" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 297.878,122.623 L 298.706,130.181" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 298.706,130.181 L 299.534,137.739" style="fill:none;fill-rule:evenodd;stroke:#CCCC00;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 297.878,122.623 L 303.925,118.189" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 303.925,118.189 L 309.972,113.755" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 297.532,118.346 L 301.765,115.242" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 301.765,115.242 L 305.997,112.139" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 297.43,142.571 L 291.282,147.079" style="fill:none;fill-rule:evenodd;stroke:#CCCC00;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 291.282,147.079 L 285.135,151.587" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-11" d="M 312.278,108.775 L 311.45,101.217" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-11" d="M 311.45,101.217 L 310.622,93.6591" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 310.622,93.6591 L 303.58,90.5611" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 303.58,90.5611 L 296.538,87.463" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 307.038,96.0742 L 302.109,93.9055" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 302.109,93.9055 L 297.179,91.7368" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-13" d="M 310.622,93.6591 L 315.912,89.7799" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-13" d="M 315.912,89.7799 L 321.202,85.9008" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-14" d="M 330.633,85.1782 L 336.355,87.6957" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-14" d="M 336.355,87.6957 L 342.077,90.2132" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-15" d="M 340.261,90.4122 L 341.089,97.97" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-15" d="M 341.089,97.97 L 341.917,105.528" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-15" d="M 343.893,90.0143 L 344.721,97.5721" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-15" d="M 344.721,97.5721 L 345.549,105.13" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-16" d="M 342.077,90.2132 L 348.021,85.8542" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-16" d="M 348.021,85.8542 L 353.966,81.4953" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-17" d="M 359.653,80.6611 L 366.593,83.7142" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-17" d="M 366.593,83.7142 L 373.532,86.7673" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-18" d="M 373.532,86.7673 L 388.265,75.964" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="259.805" y="92.7926"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="278.517" y="118.311"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#CCCC00" x="297.43" y="143.828"><tspan>S</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="309.972" y="114.865"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="291.26" y="89.3467"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="320.076" y="85.9008"><tspan>NH</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="341.222" y="111.419"><tspan>O</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="353.966" y="82.4548"><tspan>O</tspan></text>
<path class="bond-0" d="M 414.821,93.7876 L 412.57,75.6576" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-23" d="M 414.821,93.7876 L 431.647,100.903" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 412.57,75.6576 L 427.145,64.6431" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-2" d="M 427.145,64.6431 L 443.972,71.7585" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 443.972,71.7585 L 444.908,79.3011" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 444.908,79.3011 L 445.845,86.8437" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 443.584,91.8829 L 437.616,96.393" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 437.616,96.393 L 431.647,100.903" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 448.862,91.0046 L 455.956,94.0043" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 455.956,94.0043 L 463.05,97.004" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 463.05,97.004 L 463.986,104.547" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 463.986,104.547 L 464.923,112.089" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 466.956,98.8166 L 467.612,104.096" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-24" d="M 463.05,97.004 L 469.018,92.4939" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-24" d="M 469.018,92.4939 L 474.986,87.9838" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-7" d="M 467.94,116.25 L 475.034,119.25" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-7" d="M 475.034,119.25 L 482.127,122.249" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 482.127,122.249 L 483.064,129.792" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 483.064,129.792 L 484,137.335" style="fill:none;fill-rule:evenodd;stroke:#CCCC00;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 482.127,122.249 L 488.095,117.739" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 488.095,117.739 L 494.064,113.229" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 481.715,117.981 L 485.893,114.824" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 485.893,114.824 L 490.07,111.667" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 481.94,142.222 L 475.872,146.808" style="fill:none;fill-rule:evenodd;stroke:#CCCC00;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-14" d="M 520.08,86.7641 L 525.854,89.2059" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-15" d="M 524.977,96.9735 L 525.914,104.516" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-15" d="M 527.667,88.9807 L 528.603,96.5233" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-17" d="M 543.273,79.3939 L 550.264,82.3503" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-17" d="M 550.264,82.3503 L 557.256,85.3068" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-18" d="M 557.256,85.3068 L 559.507,103.437" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-18" d="M 561.22,87.576 L 562.795,100.267" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-25" d="M 557.256,85.3068 L 571.831,74.2922" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-19" d="M 559.507,103.437 L 576.334,110.552" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-20" d="M 576.334,110.552 L 590.909,99.5377" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-20" d="M 576.317,105.985 L 586.52,98.2748" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-21" d="M 590.909,99.5377 L 588.658,81.4077" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-22" d="M 588.658,81.4077 L 571.831,74.2922" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-22" d="M 584.711,83.7057 L 572.932,78.7249" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="443.584" y="92.9334"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="462.662" y="118.179"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#CCCC00" x="481.94" y="143.424"><tspan>S</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="494.064" y="114.28"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="474.986" y="89.0343"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="503.749" y="85.1353"><tspan>NH</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="525.261" y="110.381"><tspan>O</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="537.585" y="81.2362"><tspan>O</tspan></text>
<path class="bond-0" d="M 633.249,85.1828 L 647.175,97.0077" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 647.175,97.0077 L 643.898,114.981" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-2" d="M 647.175,97.0077 L 664.379,90.8597" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 664.379,90.8597 L 669.92,95.5648" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 669.92,95.5648 L 675.461,100.27" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 681.149,101.668 L 688.329,99.1025" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 688.329,99.1025 L 695.509,96.5367" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 697.306,96.8644 L 698.667,89.4004" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 698.667,89.4004 L 700.028,81.9365" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 693.712,96.2089 L 695.073,88.7449" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 695.073,88.7449 L 696.434,81.2809" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 695.509,96.5367 L 700.679,100.927" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 700.679,100.927 L 705.849,105.317" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-7" d="M 714.713,106.475 L 720.676,104.344" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-7" d="M 720.676,104.344 L 726.639,102.214" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 726.639,102.214 L 741.725,112.518" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 726.639,102.214 L 728.762,94.9701" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 728.762,94.9701 L 730.885,87.7267" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 730.782,101.068 L 732.268,95.9978" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 732.268,95.9978 L 733.754,90.9274" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 741.725,112.518 L 756.186,101.355" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 741.661,107.951 L 751.785,100.137" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 734.416,84.6051 L 740.805,84.4194" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 740.805,84.4194 L 747.195,84.2336" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-11" d="M 751.127,87.1959 L 753.657,94.2753" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-11" d="M 753.657,94.2753 L 756.186,101.355" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="675.461" y="105.729"><tspan>O</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="695.943" y="81.6087"><tspan>O</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="704.157" y="111.406"><tspan>NH</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="729.138" y="87.7267"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="747.195" y="87.1959"><tspan>O</tspan></text>
<path class="bond-0" d="M 14.27,281.894 L 27.6062,294.38" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 27.6062,294.38 L 23.4608,312.173" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-2" d="M 27.6062,294.38 L 45.0879,289.074" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 45.0879,289.074 L 50.3341,293.986" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 50.3341,293.986 L 55.5803,298.898" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 61.2679,300.697 L 68.5868,298.476" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 68.5868,298.476 L 75.9058,296.254" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 77.685,296.669 L 79.403,289.295" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 79.403,289.295 L 81.121,281.921" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 74.1265,295.84 L 75.8445,288.466" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 75.8445,288.466 L 77.5625,281.092" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 75.9058,296.254 L 80.9478,300.975" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 80.9478,300.975 L 85.9899,305.696" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-7" d="M 94.5203,307.138 L 100.622,305.286" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-7" d="M 100.622,305.286 L 106.724,303.434" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 106.724,303.434 L 109.187,296.326" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 109.187,296.326 L 111.651,289.217" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 110.915,302.498 L 112.64,297.522" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 112.64,297.522 L 114.364,292.546" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 106.724,303.434 L 112.688,307.948" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 112.688,307.948 L 118.653,312.461" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-17" d="M 115.345,286.224 L 123.159,286.376" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-17" d="M 123.159,286.376 L 130.972,286.528" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 121.233,317.503 L 121.085,325.113" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 121.085,325.113 L 120.936,332.724" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-11" d="M 123.931,312.618 L 130.105,308.314" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-11" d="M 130.105,308.314 L 136.278,304.009" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-12" d="M 139.776,301.073 L 152.231,303.974" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-13" d="M 154.071,308.155 L 166.557,294.819" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-14" d="M 166.557,294.819 L 161.251,277.337" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-14" d="M 162.265,293.258 L 158.551,281.02" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-15" d="M 161.251,277.337 L 143.458,273.191" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-16" d="M 143.458,273.191 L 130.972,286.528" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-16" d="M 144.253,277.689 L 135.512,287.025" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="55.5803" y="304.605"><tspan>O</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="77.2073" y="281.506"><tspan>O</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="83.9637" y="311.785"><tspan>NH</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="110.067" y="289.217"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="118.653" y="317.503"><tspan>N</tspan></text>
<path class="bond-0" d="M 214.591,285.513 L 228.034,297.884" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 228.034,297.884 L 224.042,315.711" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-2" d="M 228.034,297.884 L 245.469,292.427" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 245.469,292.427 L 250.769,297.304" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 250.769,297.304 L 256.069,302.181" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 261.756,303.908 L 269.052,301.625" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 269.052,301.625 L 276.348,299.342" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 278.131,299.741 L 279.786,292.35" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 279.786,292.35 L 281.441,284.958" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 274.565,298.943 L 276.22,291.551" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 276.22,291.551 L 277.875,284.16" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 276.348,299.342 L 281.415,304.005" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 281.415,304.005 L 286.482,308.668" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-7" d="M 295.069,310.061 L 301.148,308.159" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-7" d="M 301.148,308.159 L 307.226,306.256" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 307.226,306.256 L 308.882,298.865" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 308.882,298.865 L 310.537,291.474" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 311.289,304.837 L 312.447,299.663" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 312.447,299.663 L 313.606,294.489" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-13" d="M 307.226,306.256 L 312.628,311.228" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-13" d="M 312.628,311.228 L 318.03,316.199" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 313.858,287.603 L 321.256,285.287" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 321.256,285.287 L 328.654,282.972" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 328.654,282.972 L 332.646,265.144" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-18" d="M 328.654,282.972 L 334.056,287.943" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-18" d="M 334.056,287.943 L 339.458,292.915" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-18" d="M 327.8,287.152 L 331.582,290.632" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-18" d="M 331.582,290.632 L 335.363,294.112" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-11" d="M 332.646,265.144 L 350.081,259.688" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 332.646,265.144 L 319.203,252.773" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-14" d="M 323.309,317.802 L 330.707,315.486" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-14" d="M 330.707,315.486 L 338.105,313.171" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-14" d="M 324.437,313.62 L 329.616,311.999" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-14" d="M 329.616,311.999 L 334.794,310.379" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-15" d="M 338.105,313.171 L 339.76,305.78" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-15" d="M 339.76,305.78 L 341.415,298.388" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-16" d="M 338.105,313.171 L 343.405,318.048" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-16" d="M 343.405,318.048 L 348.704,322.925" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-17" d="M 350.866,328.587 L 349.211,335.978" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-17" d="M 349.211,335.978 L 347.556,343.37" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="256.069" y="307.843"><tspan>O</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="277.496" y="284.559"><tspan>O</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="284.513" y="314.758"><tspan>NH</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="308.579" y="291.474"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="318.03" y="321.672"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="339.458" y="298.388"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="348.704" y="328.587"><tspan>O</tspan></text>
<path class="bond-0" d="M 457.025,335.079 L 474.253,329.001" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 474.253,329.001 L 488.131,340.882" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-2" d="M 474.253,329.001 L 477.604,311.042" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 477.604,311.042 L 471.984,306.231" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 471.984,306.231 L 466.365,301.42" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 478.294,306.823 L 474.36,303.455" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 474.36,303.455 L 470.427,300.088" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 477.604,311.042 L 484.898,308.468" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-8" d="M 484.898,308.468 L 492.193,305.895" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 464.294,296.116 L 465.685,288.658" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-4" d="M 465.685,288.658 L 467.076,281.201" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 467.076,281.201 L 474.371,278.628" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 474.371,278.628 L 481.665,276.054" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 470.48,283.875 L 475.586,282.073" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-5" d="M 475.586,282.073 L 480.693,280.272" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 467.076,281.201 L 461.559,276.478" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-6" d="M 461.559,276.478 L 456.042,271.754" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-16" d="M 486.944,277.382 L 492.563,282.193" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-16" d="M 492.563,282.193 L 498.183,287.004" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-7" d="M 453.766,266.275 L 455.157,258.818" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-7" d="M 455.157,258.818 L 456.548,251.36" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 495.4,301.919 L 496.791,294.462" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 496.791,294.462 L 498.183,287.004" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 492.226,299.012 L 493.199,293.791" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-9" d="M 493.199,293.791 L 494.173,288.571" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 498.183,287.004 L 504.158,284.896" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-10" d="M 504.158,284.896 L 510.133,282.788" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-11" d="M 518.968,283.971 L 524.128,288.389" style="fill:none;fill-rule:evenodd;stroke:#0000FF;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-11" d="M 524.128,288.389 L 529.289,292.808" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 527.493,292.472 L 526.102,299.93" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 526.102,299.93 L 524.711,307.387" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 531.085,293.143 L 529.694,300.6" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-12" d="M 529.694,300.6 L 528.303,308.057" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-13" d="M 529.289,292.808 L 536.481,290.27" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-13" d="M 536.481,290.27 L 543.674,287.733" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-14" d="M 549.361,289.164 L 554.879,293.887" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-14" d="M 554.879,293.887 L 560.396,298.611" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-15" d="M 560.396,298.611 L 577.624,292.533" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="461.086" y="302.205"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="481.665" y="278.168"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="450.354" y="272.365"><tspan>O</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="492.193" y="308.009"><tspan>N</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#0000FF" x="510.133" y="283.971"><tspan>NH</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="523.095" y="313.812"><tspan>O</tspan></text>
<text style="font-size:6px;font-style:normal;font-weight:normal;fill-opacity:1;stroke:none;font-family:sans-serif;text-anchor:start;fill:#FF0000" x="543.674" y="289.774"><tspan>O</tspan></text>
<path class="bond-0" d="M 609.091,296.422 L 626.653,291.387" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 626.653,291.387 L 631.801,296.36" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-1" d="M 631.801,296.36 L 636.95,301.332" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-2" d="M 642.638,303.264 L 649.997,301.154" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-2" d="M 649.997,301.154 L 657.356,299.045" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 659.128,299.487 L 660.959,292.146" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 660.959,292.146 L 662.79,284.805" style="fill:none;fill-rule:evenodd;stroke:#FF0000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-3" d="M 655.583,298.603 L 657.414,291.262" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-7" d="M 688.058,306.702 L 693.052,311.525" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<path class="bond-16" d="M 732.024,261.181 L 745.165,273.872" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
<path class="bond-17" d="M 745.165,273.872 L 740.744,291.599" style="fill:none;fill-rule:evenodd;stroke:#000000;stroke-width:1px;stroke-linecap:butt;stroke-linejoin:miter;stroke-opacity:1"/>
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<p>We don't have the actual scaffold definitions, but we can use the standard MCS trick to guess:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">scaff</span> <span class="o">=</span> <span class="s1">'Scaffold_18'</span>
<span class="n">params</span> <span class="o">=</span> <span class="n">rdFMCS</span><span class="o">.</span><span class="n">MCSParameters</span><span class="p">()</span>
<span class="n">params</span><span class="o">.</span><span class="n">BondCompareParameters</span><span class="o">.</span><span class="n">CompleteRingsOnly</span><span class="o">=</span><span class="kc">True</span>
<span class="n">params</span><span class="o">.</span><span class="n">Threshold</span><span class="o">=</span><span class="mf">0.8</span>
<span class="n">mcs</span> <span class="o">=</span> <span class="n">rdFMCS</span><span class="o">.</span><span class="n">FindMCS</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">synscreen</span><span class="p">[</span><span class="n">synscreen</span><span class="o">.</span><span class="n">scaffold</span><span class="o">==</span><span class="n">scaff</span><span class="p">]</span><span class="o">.</span><span class="n">ROMol</span><span class="p">),</span><span class="n">params</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">mcs</span><span class="o">.</span><span class="n">smartsString</span><span class="p">)</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">synscreen</span><span class="p">[</span><span class="n">synscreen</span><span class="o">.</span><span class="n">scaffold</span><span class="o">==</span><span class="n">scaff</span><span class="p">]</span>
<span class="n">tmp</span><span class="p">[</span><span class="n">tmp</span><span class="o">.</span><span class="n">ROMol</span><span class="o">>=</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmarts</span><span class="p">(</span><span class="n">mcs</span><span class="o">.</span><span class="n">smartsString</span><span class="p">)]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<pre>[#6]-&!@[#8]-&!@[#6](=&!@[#8])-&!@[#7]-&!@[#6]
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<th></th>
<th>mol_name</th>
<th>cdk2_ic50</th>
<th>cdk2_inhib</th>
<th>scaffold</th>
<th>sourcepool</th>
<th>cdk_act_bin_1</th>
<th>ROMol</th>
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<tbody>
<tr>
<th>946</th>
<td>mol_947</td>
<td>None</td>
<td>11.1111</td>
<td>Scaffold_18</td>
<td>synscreen</td>
<td>0</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAAABmJLR0QA/wD/AP+gvaeTAAAfq0lEQVR4nO2daVxUR9r2r9Mbm8qmEZBFW0DcIohGELFBlgCCGo1GVJQYZ2JcEqPm0ZnkfeNM5v1NjIkzyZNksk0iaBSXKAoRZBEQRFRURImyNbsCgihrA9193g+nbRAFgT6nEaz/p/TpOlXVeKXqrrvuuouiaRoEAtvwBroDhKEJERaBE4iwCJxAhEXgBCIsAicQYRE4gQiLwAlEWAROIMIicAIRFoETiLAInECEReAEIiwCJxBhETiBCIvACURYBE4gwiJwAhEWgROIsAicQIRF4AQiLAInEGEROIEIi8AJRFgETiDCInACERaBE4iwCJxAhEXgBCIsAicQYRE4gQiLwAlEWAROIMIicAIRFoETiLAInNAfYVVUVDx8+JD1rhCGEv0R1q5du0xNTcPDw1nvDWHI0B9hJSUlKRSKKVOmsN4bwpCB6ms67vLycisrK0NDw9raWj6fz1G3CIOdPo9YSUlJACQSCVEVoQf6LKyUlBQAEomEg84Qhg79HLE8PDzY7wthCNE3G6usrMza2trIyKimpoZMhYQe6NuIRQwsQi/pm7CIgUXoJYI+lV5RWTlcIvHz9OSoN4QhQ19srLIyWFvDyAg1NSBTIaFH+jIVnj0LABIJURXhmfRFWCkpAEAcDYRe0BdhJScDRFiEXtFrG4sxsExMcO8eeCSKi/AMei0RxsCaO5eoitAbeq0SMg8S+gIRFoETemdj1dZiwgTQNDGwCL2k18Y7TavsdwKhF/Ri+ImMhLc3AgLw7bdQKrnvEmEo8CxhlZfj88/x+++IiYFCgbAwrfSKMOh5lrBOnkRICHR0AGDjRhw9qoU+EYYAzxLW3bswN1f9t7k57t7lukOEocGzhNVZTJ1FRiD0yLNWhWVlCA5GQgJ0dbF9OxwcoKuLVau01T3CYOVZI5aVFbZuhb8/fHzA4yEtDSEheO899PE0IuFFo48HVk+fxpIlkMnwpz/hu++Is5TQHX0+CY3kZAQFobERK1YgLAyCvgU3E14Q+i4sAKmpmD8fDQ1YtgwHDkAo5KBjhMFNv+Yyd3ckJsLEBEeOYPFitLay3StkZEBXF1VVALBpE7KyWG+BwC39NZJmzkR8PExNER2N115DSwurvQIAsRj/+Q/rtRK0hAbW9/TpSEjAyJFISNj/7rvNzc3s9QoA/P2RmMjFaEjQBpot6xwdkZz8o5fX6p9+CggIaGxs1KQymkZWFj79FBs2AABFITgYBw9q1EHCgEFrTG5urqWlJYAZM2bU1tb29fXGRvrUKfrPf6atrGiABmgejz59mt62jW5spCUSeuNG+to1zbtJ0CosOKLs7e3T0tLEYnFmZqaPj09tbW0v1KwanCQSGBlhwQL88APKymBpiXXrcPQo9PUBwMAAs2apou2Tk9HernlnCdqCLYWWlJTY2dkBmDRp0p07d55apq6u7siRI2vXrp0zJ4AZnABaKKQ9POjdu+nr1ztKXrhAb9tG0zRdWkoLBPSePTSfTy9cSMtkbPW3G+RyuqCATk+n09LorCz64UOO2xuy9MuP1Q2VlZU+Pj43b950cHBITEy0sLBgnufk5ERHRyckJJw7d66trQ0ARVHTppXPnGnh5wdvb4wY8Yyar12Dry9qauDnh+PHoafHVpc7QdO4cgUJCQDQ3g6lEiIRlEqMH4/AQAwbxkGTQxk2hQWgurrax8cnOzvb1tZ269atly9fjomJqaysZL4VCoVz5szx8/Pz9/efOnVqn2q+dQteXrh7F3PnIjoaw4ez2GuApnHiBHJz0dbW9SseDzo6eOstmJqy2uQQh2VhAaitrfX29r5x44ZCoWCejB492tfXNygoyNfX19DQsN815+bCywsVFXBzw+nTzx7n+kBcHC5dwqMOP4URI7BpE9lj6D3sCwtAVFTUggULTE1Nd+7c6efnx2Li7uJizJuHoiI4O+PMGc0GkfZ2FBUhLw95eWhoeEZhoRBz52LOHA3aG0zs3r376tWrW7dunTVrVv9q4CQ8ITMzE0BoaOj27ds1V1VJCSQSFBYCwNixSE6GrS2uXIG3N2pq+l5dXR0uXsT+/di9G4cO4coVNDRklFvq/uOjqkYDAJtOB2RVmmWUW246HcC8sSXWL6NoNC5f1vCHDCKOHz9+5MiR+vr6ftfAibCSk5PBXuK/Dz7AuXOYNw8FBQBgbY2kJNjbIysLGzbcqmI2FHumvR15eYiOxr/+ha++QmwspNIuE5/YuO4/mTN7qqSh4QVxeDQ0NFy9elUkEs2ePbvflbAf9CKTyS5dusTn893d3QE0NDTk5uY6OTn1O23pL7+gpgZJSZgzBwkJmDIFlpZIScHmzQXHjzveuCFOTEy0MDPD9eu4dg337kGpxPDhcHCAgwMqKpCXh5KSnuwnAIC/bX6idNxf5qR2W4KmER+PgID+/YpBxLlz5+Ry+Zw5cwwMDPpdCfvCOn/+vEwmc3Z2NjIyAhAfH79kyZLAwMCoqKj+VWhggOhoLFqE+Hh4eSEuDtOmwcwMP/xgWlIy7fLly24uLmdDQ8fp6XWs6WprkZ6O8+d7rvnWvZGn8+3iCsd/4HaeohA89ebBGx1r1RO3HG5WvwSg4L7J8ik3AeDyZQiF8PHp3w8ZLDCZZjXMuM6+sJhueT7KU8pMi/22ARn09XHyJJYsQUwMvLwQHy9zctI1NjaOi4vz8/a+eOWKx1dfJa5ZY2ti0vFON4uS5nZheplVVK79yVyHkgeqJarH2GIAa6ZlzT+4cspL1czD1ybe/jrgNIAtsX4d76eno60NAQGgKE1+0fMMK5YM+8Lq0i3mo+YXDujpITISwcEoLr7v5+cUGRnh6upqZGR0ZuXKgOrq9LIy959/Tli9evJLLz319Zzq6rQyh6M5gakl1m0K1aQ8eliTn22Bn23BSP3m2AJbA1H7rDHlUXkT1k2/2uV1hZICTavElJkJpRKBgSxrq74e5eVoaYGBASwtB8olW19fzxhYrq6umtTDsrBaWloYA2vOnDkAamtrc3Jy9PX1Z87s0S7uHSIRIiKwdu3Oq1dLX3311ejo6Lk2NoYyWVxIyMKIiESp1Cs8PD4kZOro0Uz55vb29LKyqNzck7m5JQ8ezBqz8WLFZj6Pdra46y2WBtrnzbYq41E0gIyyMcwrm165tPfC439Qmr51b+TqE6+dX/tfs+FNqodXr6KtDa+9xk7Uf3U1oqJQWQk+H0oleDwoFLC2RmAgjI1ZqL8vpKamKhQKV1dXTQwssC6s9PT01tbWGTNmMAZWcnKyUqmcPXu2DnOWWmOEQuzb9x+BoH3fvn3+/v6RH37oI5cbiESngoNfi4iIKyyU7Nv37fz55fX1Mfn5aaWlbY9sdrNhwxzNbr7vesxnvNREr2tYootVhYtVBQArw/r2//uJ6qFlOYB2Jb+iYYS0zth7/5qE1eFmwx6FBt28CZrG4sWaauv2bRw/DrkcNA25vON5URG++w7BwRg7VqP6+wgzw3hqnHGdZWFdvOji5lYwf34R85GtebAzfD7/559/NjAw+Oabb4I+/vjw668vdHDQFwpPBge/fuTI73l5oSdOtCoUAPg8nrOFhbdYHGhvP9vKikdRQE639erpwdwcDx6AuTz2kSKFfOXZNWE+4SHZVaMlv4Qmrgm3HPHIu5OTg/Z2LF3a/xMlFRX47bfH9KSGptHWhkOH8M47MDLqZ/19hy1XEcued3d3pKUhOhrz5wPA0qX5lZUpn33m7OrqxGIrAGia3rx58zfffCPi86++/TZjWjW0tpp+9hkFvDF5cuCECT5isbGeXodt9CQUBTMziMUQizF2rGrskclQVYW0NBQUqN+ta9H1O7DqUsUYG6OHiavDxpvUdVRia4s33uibturqIJUiLw/5+c84oUlRsLNDcHAfKteAhw8fmpqa8vn8uro6fSZ0qb+wKazmZpiYQC5HTQ2MjHDvHkaPhp4e6uogErHVyGN8tHJlo1T6bz/Vqu1cSYnkl1+mmZllrV/f02t6ehg3DmIx7O273c2maZw5g4sX1Q8eyHQDfl15oczSyrA+cXWYnen9jsI2Nlix4hk/sr0dZWWQSpGb+/Qdg9u3cekSeDyYmcHLq+N/Bj4f27dDV7enylkiOjo6KCjI3d393LlzGlbF5lSYno7WVsycqRq5U1JA03Bz40pVAP6xezcdHq52iCcXFwPweKpR8tTBqQcoCq++CopCRgbzwEhXFh8SHnRoRVLRWPdf1iasDlc7JlBSgl9/xYoVeNKUVA9OhYU9OWnr65GejtWrIRAgLg5ZWXB6NMbTNCoqMH78MzrMBiyaLmwKq0uaUua+AW4vdLK0pMzMUFHBZIRLKS4GILGxeawMjwdPTzg69nkBz2hLR0f1SwADUXtU8MEFh4JTSy22nTm6x9fh5UcrUJSWYs8ejBoFOzu8/DLq65GXh9u3VRZbD9y/j4ICNDbi5ZdV8+krr+D33zuEpVQiJQXjxmnh3PngEJaW0uEuXYrvvoNM1trWdqG8nEdR7p2FJRQiMBAvv9z/+j08IBSqAgABA1F79IqD78fe+P5K0pWwi2dWrXJ+FM8IhQKVlaiqQmoqgJ5sO/W0KJXizh0AsLGBemNu2LCu0RZlZTh8GMuWcXrZzMOHD7OyskQikYuLi+a1sSas5mZcvgw+H25uAHDvHnJyoK8PNhxYPTJ8OP78Zxw6lHH1akt7u6OZ2UjG6hQKQVFYtAgTJ2rahJsbKArx8cwnPaH8qwD76uaJJ27d8goPj1m50tXKqqOw2mZ9UlX37yM/HwUFKC7u2M/W18f48dDV7RBTYyOGD4dSiebmjlE2L0+lLc5yGpw7d06hULi5uWlotjOw1svz59HWhldeARPJpwUDqwNDQ7z9dvJ77wHwcHDA8OEwMsKkSXB0ZM3mnT0bfD5iY5lPIj7/6NKloZGRB7KzffbvPxUcPG/cuKe+19LenlxcHFNQsKumxoQJ/QFAURgzBra2sLODhQV4PDx8iN9+g6MjBAJcuoSJExEZiZISrFkD9T5Vfj4iIrB8OUfaYtc3xFoXu9zg9NpruHz56Q4aTqColJs3AUjeew+LFnHSxKxZEAoRHc2MSXweb9+iRXweLywrK+jgwcjly3062dfSuroEqTRBKo3Jz29sawMQNHasj75+t6tRQ0O4uuLAAfB4MDfH5Mm4dg0PH2LfPqxZ0xHQWFiIgwcRHMxFLCu7wmLN3bB1K77/HkePDkxcSWtrq7GxcWtra3V1tSmnwek3biAyUp09mqbpTadPf3v5so5AsH/xYgOhMCY/P6agoPC+yhnBoyhnCwt/W9ul9vZTzM37YIC3tSEiAlIphg1DSAjUqwQAVlZYufIpK1ANYDxYAoGgrq5Oj43DKmz6sdraQFEDExeeltb817/uNzeXHj68m/PGcnLw22/MuKWk6TUnTpQ8eJBaWjpMJGp8FLczUl/fd/x4fzu7V8ePH/XMTTdDQ9A0mppAUY8N8u3tiIhAYSH09LBqFcaM6fjK3BwhISweVzp16tTChQslEgkzbmmOplNhRgY8PFBSgtGjsXUr1q2DoyMrHesbCQn6qalvv/++VhqbPBmFhbh2DcD1ysoD2dliY+P/5+Ulk8tP3LplNWLEWienxZMm8XqOfRAKMW4c7O1hZ6c6FtLUhIoKXL+OP/7oKLNiBY4exe3b2L8fq1bB0lL11d27CA9HSAjYMLTBUgxWZ1hwjTwPaWG04TPrzIQJjAWtdsn+1d39756eSyZNiikoSC8v71ZVo0bBzQ0hIdixA8HBcHbuOGxkYAB7eyxdCl/fjvJ8PpYuxaRJkMkQHo7i4o6vKivxyy/PPgbSO5KSkvC8CWvA08K0tiIjAzyeFg/RjB3LmFmMsCSPfP1Pd/2LRLC3R2Ag3n8fGzbA2xticU8eKVfXx4K9+Hy8/jqmTEFbmzwi4npJSUfJmhqEhUGDIw8MN2/ezM7O1tHR0TAeszMsrAoHPC3MhQuQyeDkpMUjpTo6GDlSWVWVWlqKR75+mVx+kfHQqm8csrODi0uvdpC64OwMilKvQMHjYfFipVAYWlt7/MCByOXLfdUr0Npa/O//wtwctraYOrX38VtyuTwjI4M5oX716lUrK6sHDx4UFhaydVaPHXfDmjWYPx/sHR/sGwNz451CkVVZWdfSMt7ExMbICMCFsjKZXD7d3NxYTw98PsRiBAf3P8p0+nSIRDhxQrUC5fGoBQuMY2JaSkuDDh48/Prri9SOX7kcZWW4cwepqXB0hJ9fD8NhSUlJTExMbGxsYmKiOu0Uc4q4vr7ey8srPj7+ZU02Kh7BzvZT57Qw4eGqc1paYwCE1diI2touE99jH8VivPGGprHLU6ZgyRL1aEdR1Ff+/ltcXNoUimXHjh1T2/gMCgXkcly/jv37u9ylJZfL09LSdu7cOWPGjHHjxr3zzjsnT55sbGwUi8XvvvtufHx8dXV1Xl7eggULqqurPTw8Ll26pFG3AbDoIN20CXv3IiUFW7fCzAyJiXBwYKvunpDJcPEieDy4u2ujORVFRXhiz7tDWEIhli9nZ8940iTweDh2jImMoCjqX35+w3V0PklJWX7s2M8LF66eNu2x8u3tuHMHycmYN6+4uDguLi4hIeHMmTPqo6cGBgaenp5BQUH+/v5WnXeigKNHjy5fvvzEiRO+vr4xMTEaxryzHOjX3IyFC5GQgJdeQny8Rpu/vSQpCfPmYfp0XLnCeVsdREUpMjNH7dlT19JS8v771oaGMrnc+NNP25XKmv/5H6OpU7FiBZvNFRTg8OHOLq7daWk7ExL4PN4PQUFrnTqCKOVKZUZ5eXRBQUJj45WrHUdCxGJxYGBgUFDQ3LlzRd3vsikUitDQ0AMHDhgYGJw6dWrevHn97jLLu076+oiOxrJlOHUKHh6IjcUrr7DbQlcKCiAUatHRwFBcXF5dbULTJiYm1oaGANLLymRyubOFhZGuLvtR6ra2WLUKBw+qD07umDOHoqgd8fHrTp5samsLtLePl0oTpNIzBQX1j9bn6sEpICDAUu0A6xE+n79v3z4+nx8WFhYUFBQZGenT30OUnCQFaWvD8uU4cQKGhoiJgWZjKpqacPYssrPx4YfdFmhpwciRGrXSBxob8cUXuHABcXENs2YN9/MD8I/k5P+TnLxt9uzPfX3xpz9BHUvDIqWl+PXXzomW9l64sP3MGR6Pp3hkVFGAo7m5n62t/+LFrps2Cfq1XU3T9KZNm7799lsdHZ0jR44sWLCgH5Vwsk8uEuHoUYSG4sAB+Pjg1Cn0Y0yVSpGQgKgoxMejtRUUhbVrO24f6+zx37ED69ZpUVhFRQBQXAyaHv5IQB8VFW0bObJGLIaODld3pFlbY/58REaqI3O2urrqCgQ1zc17zp/3GDs2aMKEADs7S8bjOnFiv4MgKIr6+uuvBQLBV199tXTp0kOHDi1evLivlXAV3MPnY98+8PkIC0NQECIje3UwvbGx8ezZs+fPlx05slHtZObz4eoKf/+ufyjG479rF9tdfybFxVAqwTgqGcu9vR0VFXpKpZWlJaytOTwkbWsLiup8/mLDzJlKmv7Q3Z3fea0gEGjo06Mo6t///rdAINi7d++yZcv27du3qo9XvnF4Ew6fj59/hkCA//4Xn356XS6/4+/v/9SSUqk0ISEhKioqPj6+tbWVxxOYmLwzciTP0xPe3liwAGZmT3mL8fj/5S/c/YJuKC5GZSVkMpiYqKLPysogl8PCAlwYWJ3R14eREe7f7/yMR1FPkbLGMfIURX3xxRfDhw//29/+FhoaKpfLQ0NDe/86t1cs8Xj48UdYWub9/e/Tz58XHj58eOHChZ0LFBcXe3h4lDzapuDz+bNnz/b3958//+G0acZPLthv3EBMDGJj8eabA+Txb2jA/fuqPTu1hpiPTKwf1+dLPTwQHf2UlJZq+HzY2LB1hHrXrl16eno7d+5cu3ZtU1PTxo0be/ki53d3URQ+/tiuoWHL3r17lyxZ0mVQtbKyamxsHDVqlIeHR2BgYGBgoEnnxB4AgKYmXLiAqChERqK0VPXQwQH6+gPh8VcbWHhCWDY2HBpYaqZOxc2bKCrqNlmXSMRuqOOOHTv4fP4HH3ywefNmuVz+3nvv9eYtbVwK12VQVSgUa9asYb7i8/mZmZk2NjbUE4N5djYdE0PFxuL8+Y6/oYUF/Pzg7w8jI8TGqjz+UVFYt04LvwMAUFICpVIlcEZYjE+Sx4O1NWxstJGFZtkynDiB/Pyu45ZIBH19hISwnlBk+/btw4YN27hx45YtWxobGz/sbn3eCe3dNqgeVN98882mpqYNzMUmwNhOc0dTU9PZs2ejo6NjYmJGjLiVk2MAgM+HszMCAxEUhOnTVf9wj077qTz+2qOoSGVgmZqqIl4YA2vMGM4NLDVMvENREdLSVK1TFExNMWMGnJ05Osmzfv16Pp+/fv36jz76SC6Xf/zxx894Qct55ffs2QOAoqgvv/xS/TArK+vTTz+VSCSd/S4LFsS/9RZ97Bj94IGW+9g9OTn0rl20ry8N0M7O9K5d9K5d9Ny5NEC7udG7dtHd3JzALW1tWmvq119/Zf6NduzY0XNJbd+PygyqGzZs2LJlS1ZWFo/Hi42NraioYL4VCAQSicTf39/Pz29al12wASc7G6dOAYCNDVxdO5ZdantLR+fpy1eu0WIw+IoVKwQCwapVq3bv3q1QKD777LMnbRiGAbh4d/369RRFvfPOO9HR0ffu3QPw0ksvvfrqq0FBQT4+PkZazKzSB6qrER2tOiM/Zsxj4efW1pDLYW0NY+MhnOZPzbJlywQCQXBw8Oeff+7k5LSim11RTrZ0esOoUaNqamq2bdu2evVqVgKAuCUsDCUlHZ7JpybwEImwc+eLoC0Av//++7Fjx3766afuchYPjLByc3MdHBzMzMzu3LnT3Vj6HCGT4fPPO1J61Nfj2LGOBB6jRqnyLIhEWLkS6vDRFxvO80w8FXXo/iBQFYCqqsfsmNu3H0vgoQ64Uyhw9+4AdO+5ZGCExfphI27p4opkciswdE7gQdMvyA0DvWFghMXk9Ro0wmJSdKjpLKbOIhMI2L6UbBAzAMK6ffv2nTt3zM3NJ0yYoP3W+0OXFN8TJiA7WxXPeekSJk1SPVcqtZyI9nlmANwNXGS85RaKgosLLlxQzXRdEngw/jYeD2PHQoNL84YYRFi9w90dOTmoq1PNiRMndk27JRQiKGhAuvZ8MgBTYWpqKgadsAQChIbCxESV76uzj0YggL4+QkNZvZlz0KNtP9atW7cmTZpkbm5+h0mROLhQKHD5MtLT0dKiukWCz4eTE+bO1U5W40GEtqdCti4+GBj4fLi4wMUFzc1oaICeHhmlumNghDXI5sEn0ddnK3/QUEWrUyFN0+bm5lVVVXl5eXZ2dlprl6B9tGq837p1q6qqytzcnKhqyKNVYTHzoCYHtwmDhQEQFluXkBOeZ7RnY6kNrPz8fFtbW+00ShgotDdi/fHHH1VVVRYWFkRVLwLaExYxsF4otCcsJgaLGFgvCFqysdQGVkFBwXitXL1HGFi0NGLl5ORUVVVZWloSVb0gaElYP/zwg0gkeoXr9H6E5wZt7BX++OOPX3/9NYDWAbxlgKBdOLexwsPD33zzTaVSCcDY2Li6urp/+QsJgwtup8LffvvtrbfeUiqV//znPx0cHOrq6jLU2TwIQxoOhRUZGRkcHCyXyz/55JOdO3cy6fxiYmK4a5HwHMFRWpIzZ87o6OgA2LZtG/MkLi4OgJOTE0ctEp4rOBFWQkKCrq4ugC1btqgfymSyYcOGURRVUVHBRaOE5wr2p8L09PRFixbJZLJ169bt7ZQTTUdHx8PDg340dBGGNiwLKyMjw8/Pr7GxMTQ09Pvvv++SmoGYWS8QLI5+165dY1LThoSEKBSKJwtIpVIAxsbG7e3tLLZLeA5hTVjZ2dnM9fGLFy/uQTfMsfq0tDS22iU8n7AzFebl5fn6+tbW1i5cuDAiIqIHFyiZDV8QWBBWQUGBp6dnZWWlr69vRESEsMeUmERYLwiabumUlpZKJJLi4mIvL6+oqCg9Pb2ey7e2to4cObKpqamiosKc61z7hIFDoxGrvLzc09OzuLh49uzZkZGRz1QVAB0dHYlEQhOnw1Cn/8Kqrq729fWVSqWzZs2KjY0d1uvbEMhs+CLQz6nw3r17np6eOTk5jo6OiYmJT16A0wNFRUVisZhEOgxt+jlibd68OScnZ8yYMd99912fVAVg3Lhx9vb2dXV1rNyWTng+6aewgoODAVRUVLi4uJiZmS1btuzLL7+8cuVKL8c/MhsOefo5FVZWVkZGRiYnJ6ekpFRWVqqfW1hYeHp6enh4SCSSHhI0nDlzxs/Pz9nZOTMzsz+9Jjz3sBBBytyPmpaWlpycXFZWpn5uZmbm7u7u7e3t5uY2efLkzq/IZDJTU9OWlpa7d++OHj1aww4QnkNYDk1WiywpKam8vFz9/EmRzZ8///Tp02FhYatXr2axA4TnBA5j3nsWGZ/Pj4iIWL58+aFDhzjqAGEA0caBVZqm//jjj6SkpJSUlJSUFObGLwaxWFxYWMh1BwjaR9vJbWmazsnJSU5OTk5OzszMLCws7O76KMKgZsCulSMMbQbmLh3CkIcIi8AJRFgETiDCInACERaBE4iwCJxAhEXgBCIsAicQYRE4gQiLwAlEWAROIMIicAIRFoETiLAInECEReAEIiwCJxBhETiBCIvACURYBE4gwiJwAhEWgROIsAicQIRF4AQiLAInEGEROIEIi8AJRFgETiDCInACERaBE4iwCJxAhEXgBCIsAicQYRE4gQiLwAlEWARO+P9G00BIUUkGMwAAAABJRU5ErkJggg==" alt="Mol"/></td>
</tr>
<tr>
<th>1528</th>
<td>mol_1529</td>
<td>None</td>
<td>12.9218</td>
<td>Scaffold_18</td>
<td>synscreen</td>
<td>0</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
</tr>
<tr>
<th>1529</th>
<td>mol_1530</td>
<td>None</td>
<td>6.47714</td>
<td>Scaffold_18</td>
<td>synscreen</td>
<td>0</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
</tr>
<tr>
<th>4923</th>
<td>mol_4924</td>
<td>None</td>
<td>0</td>
<td>Scaffold_18</td>
<td>synscreen</td>
<td>0</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
</tr>
<tr>
<th>4924</th>
<td>mol_4925</td>
<td>None</td>
<td>0.905505</td>
<td>Scaffold_18</td>
<td>synscreen</td>
<td>0</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
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<div class="prompt input_prompt">In [11]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">scaff</span> <span class="o">=</span> <span class="s1">'Scaffold_12'</span>
<span class="n">params</span> <span class="o">=</span> <span class="n">rdFMCS</span><span class="o">.</span><span class="n">MCSParameters</span><span class="p">()</span>
<span class="n">params</span><span class="o">.</span><span class="n">BondCompareParameters</span><span class="o">.</span><span class="n">CompleteRingsOnly</span><span class="o">=</span><span class="kc">True</span>
<span class="n">params</span><span class="o">.</span><span class="n">Threshold</span><span class="o">=</span><span class="mf">0.8</span>
<span class="n">mcs</span> <span class="o">=</span> <span class="n">rdFMCS</span><span class="o">.</span><span class="n">FindMCS</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">synscreen</span><span class="p">[</span><span class="n">synscreen</span><span class="o">.</span><span class="n">scaffold</span><span class="o">==</span><span class="n">scaff</span><span class="p">]</span><span class="o">.</span><span class="n">ROMol</span><span class="p">),</span><span class="n">params</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">mcs</span><span class="o">.</span><span class="n">smartsString</span><span class="p">)</span>
<span class="n">tmp</span> <span class="o">=</span> <span class="n">synscreen</span><span class="p">[</span><span class="n">synscreen</span><span class="o">.</span><span class="n">scaffold</span><span class="o">==</span><span class="n">scaff</span><span class="p">]</span>
<span class="n">tmp</span><span class="p">[</span><span class="n">tmp</span><span class="o">.</span><span class="n">ROMol</span><span class="o">>=</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmarts</span><span class="p">(</span><span class="n">mcs</span><span class="o">.</span><span class="n">smartsString</span><span class="p">)]</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt"></div>
<div class="output_subarea output_stream output_stdout output_text">
<pre>[#6]-&!@[#7]1:&@[#6](=&!@[#8]):&@[#6]:&@[#6]:&@[#7]:&@[#6]:&@1=&!@[#8]
</pre>
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</div>
<div class="output_area">
<div class="prompt output_prompt">Out[11]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
<div>
<style scoped>
.dataframe tbody tr th:only-of-type {
vertical-align: middle;
}
.dataframe tbody tr th {
vertical-align: top;
}
.dataframe thead th {
text-align: right;
}
</style>
<table border="1" class="dataframe">
<thead>
<tr style="text-align: right;">
<th></th>
<th>mol_name</th>
<th>cdk2_ic50</th>
<th>cdk2_inhib</th>
<th>scaffold</th>
<th>sourcepool</th>
<th>cdk_act_bin_1</th>
<th>ROMol</th>
</tr>
</thead>
<tbody>
<tr>
<th>1202</th>
<td>mol_1203</td>
<td>None</td>
<td>19.7348</td>
<td>Scaffold_12</td>
<td>synscreen</td>
<td>0</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
</tr>
<tr>
<th>3236</th>
<td>mol_3237</td>
<td>None</td>
<td>32.6813</td>
<td>Scaffold_12</td>
<td>synscreen</td>
<td>50</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
</tr>
<tr>
<th>3318</th>
<td>mol_3319</td>
<td>None</td>
<td>18.6061</td>
<td>Scaffold_12</td>
<td>synscreen</td>
<td>0</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
</tr>
<tr>
<th>3769</th>
<td>mol_3770</td>
<td>None</td>
<td>16.5904</td>
<td>Scaffold_12</td>
<td>synscreen</td>
<td>0</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
</tr>
<tr>
<th>3787</th>
<td>mol_3788</td>
<td>None</td>
<td>5.70459</td>
<td>Scaffold_12</td>
<td>synscreen</td>
<td>0</td>
<td><img data-content="rdkit/molecule" 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y3wvA4+H7NmYciQnu2JiwtefhnHjvnYlgDIvGcH4OGFulqxwepml/cUX331VW5u7uzZs9mCtra2KhZkGGbJkiUBAQFvvvlmYWFh1zDHb/VFsLy9vTU0NK5cuaKhoVFSUtKdUz8A9+7d8/f3z8/PHzx48JkzZ1JTU5ctW6ZUKiNnzYr29ASfD0KgVEJfH1paBpWVwW5uwW5Fn/hvuV5hkSJ2SRU7X7tv8XWuq7mwdaDJg9fcbyXfGRw3PQUAGAZz5/ZghOJxTk5gmDHW5VoaipxK84YObaP6ejQ1wcCgN9V+R01NDR47YvXaf//73yNHjgwdOpQN1lN6rh6xt7c/e/ZsaWmpkZHR772nL4IlFAqHDRt27dq1S5cu+f3hNQgAoKioyN/fXyKRjBgx4tSpU4cPH2bvM8bExISHh6O5GaWlUChgZQX2ApM9v5ZIeAUFXgMqvAZUiPzO32s0PCNxvN+s//gYAQDo6PQyVexnzcy0q6tHDrj/wz2bH0utXxpUgNLSHg/SPpW6jlhddX7Vt6qOz+c/5XCFPnvE3tLSksfjLVmyJCYmJi8v7+lvzsvLGz9+vEQimTBhwrlz5+Lj48PCwgDExsaGh4cDgL4+XF0xdCi6hi0MDeHlheBgrF6NkBCMGQNdXVvDxgWe1wEsGnEt/rrnzxuwtlZpZ2xtAXDXGyqVygcPHjAM04tBmV9hj3xdwVI9qd3XF8GKj48/deqUtra2WCyOiIhwdXV1dHRcsmRJSkrKE2+XWllZWVlZTZo06cSJExs3blyzZg2fz++K1x/Q0ICjI6ZOxapVXaPhOhryvFqzUwVOAMAwmDBBpf2xswMw0T7fw/JQZUsSANy7p1LBX3rw4IFCoTA2NlZ9rlHXgUpdfWsPcD1FddeuXexyDKtWrWIfkRgwYEDX1nV1dYOCgj777LPy8vLHP1VbW9va2rpo0SIAAoHg66+/7s22KyrIP/9JRKK6yE08RqmjIetY+yERiUhHh0q71NRERKKGNWv4PJ6Whkb72rVEJCIqz7ruwh7RXVxcVC8lEAgAdHR0DB06FEBOTo7qNbuJ22Cx41UMwzw+S12hUFy5ciU6Otrb25thmK6Qubq6RkZGZmZmKpVKuVw+b948NnlpaWm9b0FzM5stt/7VAPlh4T4iEpFHD8r2XmwsEYmGW1gA+P6tt4hIRH76SdWaj7DDMd7e3irWaWhoAGBgYEAIYZ93un//vjoa2C1cdYWEkIiICLYX++KLL1auXNn1Vzwez8vLKzIyMisrq6SkZM+ePYGBgTo6Orm5uZs2bfLx8bG1tXV1dU1ISDA0NExLS5syZUrv2yEUwsUFgK9dCYCHI0+qnxLZ2QHwfTT7FACSk9U1WV5d3VZXHUJIXV0d1HdV2B2cBIsQsnLlypiYGIFAcOjQoQW/P6vExsYmNDQ0NTW1rq4uPT09LCzMxsamrKzMzMzMzMwsLS1tgornQ3jSubbqp0SNjQAe3gtnq7GTTsvLVa3MwSVhY2OjXC43NDRke8a+8bTTw/3791tZWU2cOFFLS6v7FRUKxaJFi7788kstLa2kpKSZM2d251M6Ojr+/v7+/v6xsbE3btzg8/kODg69Hmv+hYdHlxIAWfdsFUqGX16Ozk70+tS4tRVlZQDYafI/lpYqlEo+jwe5HKdP4623VGxv16Uc+8e2tjbdXs3J6arzq4J943ePWHK5fNWqVQEBASYmJtOnT4+Liyvvxq+jTCabM2fOl19+qaenl5qa2s1UPY5hGE9Pz2HDhqknVQD69YOurpVBs4Nxg4K03K7RhkKh0qHl0QHPQii00Ndvkkqjs7KU7C1XdRyx2G6LzYFcLvf19e3dgxJdl4RqH8Tqjt8NVkdHxzvvvOPp6dne3p6amrpkyRJbW9sxY8Z8+OGH169fJ0+6dd3W1jZjxoxjx44ZGxunp6ezK2A9ewzDzj6Y7jynXdb/++LzgGq9oVjMzt/9x5kzlc3NegLB2nPnhu7effT27a673b2jVCpTUlIOHz6sr6+fkZEhk8mys7Nv3brVuwclRo0a9emnnwYHB/f9IBbQjeGGqqqqAwcOBAcHP34I6d+/f0hIyJEjR7oemW9oaGDPh8zNzW/cuMHh9UYvXLhARKLPZ8wA8KqbGxGJer+wTFYWEYmUUVErx44FoMHjLR892vHRBM7Jzs69e/xfKpV+/vnngwYNYuuw5x7Dhw+/efPmrVu3RowYAYDH44WFhXX0fKxk3759AN56661eNKzXejDc0NbWlpqaGhoaav3YyLWOjk5gYODWrVvZnbe1tb179y53ze2lsjIiEt155x0AFkIhEYmISESuXu1ZEaWSnD5NRKLO9esXeHoC0NLQ+GbOHCISdaxdu2XKFBMdHQAMw6xadVgi6W7VlpaObdu2df1IBw4c+Pnnn2dmZjo7OwPQ1NSMiopqb2+Piopib9u7u7tfu3at+60uLi5ml7lbvXp1z/ZXNb0cx7p161Z0dLS/v3/X6LCOjo6pqenSpUvV2z71kMnIhg3KqKj+enoA8sPCiEhEPvqIZGV1t4JSSU6eJCKRdN26YDc3AHoCwemQkCuLF5964w0iEpGPP246ejQqKkooNHRy6tDUJIsXk6qqp5VsaiKxscTaWmZlZQdg6NChBw4c6FpIp62tLTIykh1bHjt2rFgszs7OfjxtnZ2dT29ybm7u/Pnz2QfQAwICGhoauruz6qDqAGlmZuagQYMsLS2PHz/OMIyenp46F3RUlx9/JBs3PoiMDHR2BhA/c+bDg9aGDeTGDfJH/0JEqSTJyeyRaebgwQCMtLV/WLgwc8ECQ21tHU3Nax9/3LVMzb17jfPnEx6PAMTQkHzyyRPG5CsqSEQEMTAgAAHI/PnfpKSkKJXK32759OnT7MFMV1c3Nja2tbU1LCyMHVUeN26cWCx+Yntv3LgREhLCHuF4PF5wcPDt27d7+jNTkarBqqmpYRhGV1dXKpWyq9NevHhRLS1Tp927q1av9rCwMNPTA+Brb18ZHv4wWyIR+fhjkpBAsrPJE5dY6uwkSUlEJGp5//3Jjo4A+uvpXQ8NPTd/vlBLC8CcOXN++7uUk0OmTn2YG2trcvr0w9fr6siyZURb++FfTZlCvvvuD9re0NCwePFitlsICAgoKys7efIke1ssLi7uV2/OzMwMCgpikycQCEJCQn4vfFxTwy2dIUOGAMjOzn777bcBbNmyRfWa6qRUlkZEuPTrB8DR2Dj40fwWVzOzyAkTMhcsUEZF/RyyXbtIejopKSH19eT0afL55yQ6mohEDyIjx1pbA7DU1/9p2bKU11/X1tQE8MYbbzxlFbgffiDjxxMejyxbRrZtIxUVZPt20r8/4fFIUBDJzu7BThw5coQdNzcyMkpMTKyrq9u8efPjB7n09PTxj2buC4XCsLCwsrIyVX5sKlLDnHd2gnZMTIy5ufm8efNmzpyZnJysYk21IaR43z7/yMjCBw/c+vdPDwmJzc7+/Nq1Drm8/dEjv9YGBoHOzkHOzpMdHHTYJXHYx34Yhh07qGppCTh4MKey0t7I6My8eZfKy+cnJ8sViuXLl+/YsePp33hACC5exJkzUCoxdy7OnoW1NZycejx3FUBVVdXbb7+dkpICIDg4eO/evSYmJkql8ttvv924ceOVK1cA9OvXb/ny5WFhYapPuVGV6tlkHzCaMWNGUVERABMTE4VCoXpZNejszN22zcrAAMAoK6vaiAiRnx8ABjgSHJw+b17YmDE2j63KrK2h4e/oGDt16r333us6hpW8956zqSmAwf36la1atTcoiMcwACIjI7vfkA8/JOXlZM0asmePqvt04MABoVAIwMLC4t133+1aFNja2nrbtm0t/zMriqshWMXFxV15srGxmWJvX9nnp4pP0NlJPv640t7eSENjsqNj0/vvh48fD4DP4+3rOnkXiYhIdGvZsmh/f29bW97jUy3MzCInTEh85RUHY2MAIywtq1ev3vHSSwzDMAyzefPmHrVl/XrS3k7Wr1dDsAghEomEXcCCHUx3cHCIjY1tb29XQ2n1Uc/jX8smTvRnmKm7dmlu3aq5fz/27EFoqOple08ux4YN2LIFUmnJyJH9p06NTE/fefGigM//96xZs4cOhZ4ehgxBczMKC7vWwapta/uuqChFLD5+925jRwcAPo+nUCq9bW2/ff312Oxs0fnzPB5v165doT3cuz178N57iIzEhg3q2T+FQhEfH29mZtbR0REcHNydJ1P6mHrmvO+2tsZXXyEjA6NHY/9+ZGY+y2B1dGDdOuzYAZkM7u52AQEF587tuXRJV1Pz2NKlU9esgb7+z89sKZUoK4NYjDt3+gHBbm7Bbm7Szs6MkpJUsTjp9u2qlpYIb29Dbe1h5uZampp74+LefPPNnrboxg1IpVDjaQ+fz2cvlf5nqemB1b17sXQp5s7FBx/A3R3W1igtVUPZHpFKIZGgvh5ffon9+9HZieHDERSEY8dw585tJ6e64GBfkQhPmalRXw+xGNnZaGhgX9iUlbXmzJlJDg7n5s+HQFA2bpx19x4G+ZUxY3DpEr77Dr369J+SmoJ1+zbc3WFlhdJSmJmhrg7FxX36LW1Xr+L0aQCorsbOnVAoMG4cJk1CUhIKC6Gjg9Wr8cEH6M6EpJYW7NzJrqjWJJXax8bWt7dnLVzo7eGBZcvQ82+9UyhgYID2dtTVPUffOq2miX6urjAzQ3k5Skrg7Q0AalrYuFtu3EBaGmQyyGQwMsKLL8LPDxMn4uBBFBZCKIRIhHXrupUqAEIhQkKgrQ1NTQMtreWjRgHYdPEi5s3rRaoAiMVoa4Od3XOUKqgtWAzzcF2NjAz4+AB9GCylEqdO/fzN4QDGjsX48ThwAPfuwcgImzdj9eqeTeuztsbKlfD3h7v7ykWLhLq6qbdvXy8s7F0D2cfQhw/v3af/rNQ3NdnXFwCuXsX06di+HepYx7ZbutaerK7GuXP4/ntIJBAI4OQEY2Ns2YLQUDw2jtBdWloYPRqzZpmGhCxavJgQEhMT07sG5uQAgIdH7z79Z6W+1WaqqtDQgMe+zLKPFBbi66/R0YH0dPj7g2GQloaAABACuRwffKD6WntlZWVOTk4KhSIvL69rylT3TZ2KtDR88w3+9jcVG/Jnor4jlrk5XFzUtZhzD/Tr94v1WLswDIRCqPxdGACsra1DQkLYVct68XF6xFKHiAhER4PHw6pV+PRTdVZ+ivh4lJejogK3b4PPh40NHBwgEGDkSKjy6NhjCgsLXVxceDxeQUHB763Z0tiIoiIUFYHPx4wZD1+sroa5OQwM0NDQmw75z+tZfou92syejbg4DBgAc/OHr2hqol8/vPCCurbg5OQ0e/bspKSk7du3b926FUBmZmZubq5EIikqKiorK8/Pz6qtfRgcT8+fg9XaitmzIRA8X6mC+o9YP/2EpCQIBBg/Hn35MEV7O86dQ24uOjogFGLkSIwf39MVpJ8uJyfH09NTV1e3uLi4X79+kyZNOn/+fNffmptXNDdbODjA0RHDhuGjj9S45T+lvlgq8i8jMDDwxIkTUVFRIpFo69ateXl5Do84OnqZmwtkMhQXP+wQJZKH65i+9hqSk5/xvdO+R4PVA9nZ2ePGjTMxMSkuLm5ubi4qKmK7QolEwjB/P3v2xfJyKJU/v3/+fDg6PpyG9bwF6y9xjtVXxo4d6+vrm5GRYWpqKn98SBbw83MqLX1RQwN2dmA7RAcH1NRg0SLs3NmnN7f+R9Bg9czWrVu///778PBwMzMzR0dHBwcH9r/OzuNtbGBj84sR/o8+Qv/+3b2T9BdDu8Ie6+jo6OzsFHKwWPJfCQ0WxYk+WoOUet7QYFGcoMGiOEGDRXGCBoviBA0WxQkaLIoTNFgUJ2iwKE7QYFGcoMGiOEGDRXGCBoviBA0WxQkaLIoTNFgUJ2iwKE7QYFGcoMGiOEGDRXGCBoviBA0WxQkaLIoTNFgUJ2iwKE7QYFGcoMGiOEGDRXGCBoviBA0WxQkaLIoTNFgUJ2iwKE7QYFGcoMGiOEGDRXGCBoYhHYUAAAB8SURBVIviBA0WxQkaLIoTNFgUJ2iwKE7QYFGcoMGiOEGDRXGCBoviBA0WxQkaLIoTNFgUJ2iwKE7QYFGcoMGiOEGDRXGCBoviBA0WxQkaLIoTNFgUJ2iwKE7QYFGcoMGiOEGDRXGCBoviBA0WxQkaLIoTNFgUJ2iwKE78P+ftUjmzhAlYAAAAAElFTkSuQmCC" alt="Mol"/></td>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">defaultdict</span>
<span class="n">params</span> <span class="o">=</span> <span class="n">rdFMCS</span><span class="o">.</span><span class="n">MCSParameters</span><span class="p">()</span>
<span class="n">params</span><span class="o">.</span><span class="n">BondCompareParameters</span><span class="o">.</span><span class="n">CompleteRingsOnly</span><span class="o">=</span><span class="kc">True</span>
<span class="n">params</span><span class="o">.</span><span class="n">Threshold</span><span class="o">=</span><span class="mf">0.8</span>
<span class="n">scaffs</span> <span class="o">=</span> <span class="n">defaultdict</span><span class="p">(</span><span class="nb">list</span><span class="p">)</span>
<span class="k">for</span> <span class="n">scaff</span> <span class="ow">in</span> <span class="n">synscreen</span><span class="o">.</span><span class="n">scaffold</span><span class="o">.</span><span class="n">unique</span><span class="p">():</span>
<span class="n">subset</span> <span class="o">=</span> <span class="n">synscreen</span><span class="p">[</span><span class="n">synscreen</span><span class="o">.</span><span class="n">scaffold</span><span class="o">==</span><span class="n">scaff</span><span class="p">]</span>
<span class="n">subset</span><span class="p">[</span><span class="s1">'ROMol'</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">Mol</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">list</span><span class="p">(</span><span class="n">subset</span><span class="o">.</span><span class="n">ROMol</span><span class="p">)]</span>
<span class="c1"># if len(subset)>100:</span>
<span class="c1"># continue</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s1">'Doing </span><span class="si">{scaff}</span><span class="s1"> with {len(subset)} mols'</span><span class="p">)</span>
<span class="n">mcs</span> <span class="o">=</span> <span class="n">rdFMCS</span><span class="o">.</span><span class="n">FindMCS</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">subset</span><span class="o">.</span><span class="n">ROMol</span><span class="p">),</span><span class="n">params</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">mcs</span><span class="o">.</span><span class="n">smartsString</span><span class="p">)</span>
<span class="n">matches</span> <span class="o">=</span> <span class="n">subset</span><span class="p">[</span><span class="n">subset</span><span class="o">.</span><span class="n">ROMol</span><span class="o">>=</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmarts</span><span class="p">(</span><span class="n">mcs</span><span class="o">.</span><span class="n">smartsString</span><span class="p">)]</span>
<span class="n">mol</span> <span class="o">=</span> <span class="n">matches</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">ROMol</span>
<span class="n">scaffs</span><span class="p">[</span><span class="s1">'scaffold'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">scaff</span><span class="p">)</span>
<span class="n">scaffs</span><span class="p">[</span><span class="s1">'ROMol'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mol</span><span class="p">)</span>
<span class="n">scaffs</span><span class="p">[</span><span class="s1">'smarts'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mcs</span><span class="o">.</span><span class="n">smartsString</span><span class="p">)</span>
<span class="n">scaffs</span><span class="p">[</span><span class="s1">'timed_out'</span><span class="p">]</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mcs</span><span class="o">.</span><span class="n">canceled</span><span class="p">)</span>
<span class="n">scaffs</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">scaffs</span><span class="p">)</span>
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<pre>/other_linux/home/glandrum/anaconda3/envs/rdkit_blog/lib/python3.7/site-packages/ipykernel_launcher.py:8: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
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<pre>Doing Scaffold_05 with 265 mols
[#6]-&!@[#7]-&!@[#6]1:&@[#6]:&@[#6]:&@[#7]:&@[#6](:&@[#7]:&@1)-&!@[#7]-&!@[#6]
Doing Scaffold_10 with 502 mols
[#6]12:&@[#6]:&@[#6]:&@[#7]:&@[#7]:&@1:&@[#6](:&@[#6]:&@[#6](:&@[#7]:&@2)-&!@[#6])=&!@[#8]
Doing Scaffold_09 with 344 mols
[#6]-&!@[#6]1=&@[#7]-&@[#7](-&@[#6](-&@[#6]-&@1)-&!@[#6])-&!@[#6]
Doing Scaffold_16 with 269 mols
[#6]-&!@[#8]-&!@[#6](=&!@[#8])-&!@[#6]1(-&!@[#6])-&@[#7]-&@[#6](-&@[#6]2-&@[#6]-&@1-&@[#6](-&@[#7]-&@[#6]-&@2=&!@[#8])=&!@[#8])-&!@[#6]
Doing Scaffold_07 with 76 mols
[#6]-&!@[#6]1=&@[#7]-&@[#7]-&@[#6](-&@[#6]-&@1)=&!@[#8]
Doing Scaffold_02 with 106 mols
[#6]1:&@[#6]:&@[#6]:&@[#6]:&@[#6](:&@[#6]:&@1)-&!@[#6]1:&@[#6](:&@[#16]:&@[#6](:&@[#7]:&@1)-&!@[#7])-&!@[#6]1:&@[#6]:&@[#6]:&@[#6]:&@[#6]:&@[#6]:&@1
Doing Scaffold_06 with 273 mols
[#6]-&!@[#6](-&!@[#6]1:&@[#6]:&@[#6]:&@[#6]:&@[#6]:&@[#6]:&@1-&!@[#8])-&!@[#7]-&!@[#6]
Doing Scaffold_01 with 204 mols
[#6]-&!@[#6]-&!@[#7]1:&@[#6]:&@[#7]:&@[#6]:&@[#6]:&@1-&!@[#6]
Doing Scaffold_13 with 17 mols
[#6]-&!@[#7]-&!@[#6](=&!@[#8])-&!@[#6]1:&@[#6](-&!@[#8]):&@[#6](=&!@[#8]):&@[#6]:&@[#6](:&@[#8]:&@1)-&!@[#6](=&!@[#8])-&!@[#7]-&!@[#6]
Doing Scaffold_19 with 256 mols
[#6]-&!@[#6]-&!@[#7]-&!@[#6]1:&@[#7]:&@[#6]2:&@[#6](:&@[#16]:&@1):&@[#7]:&@[#6]:&@[#7]:&@[#6]:&@2-&!@[#7]-&!@[#6]-&!@[#6]1:&@[#6]:&@[#6]:&@[#6]:&@[#6]:&@[#6]:&@1
Doing Scaffold_18 with 38 mols
[#6]-&!@[#8]-&!@[#6](=&!@[#8])-&!@[#7]-&!@[#6]
Doing Scaffold_04 with 78 mols
[#6]-&!@[#7]-&!@[#6](=&!@[#8])-&!@[#7]-&!@[#6]-&!@[#6]
Doing Scaffold_17 with 157 mols
[#6]-&!@[#7]1:&@[#6]:&@[#6](:&@[#6]:&@[#7]:&@1)-&!@[#6](=&!@[#8])-&!@[#6]1:&@[#6](:&@[#6]:&@[#6]:&@[#6]:&@[#6]:&@1)-&!@[#8]
Doing Scaffold_12 with 61 mols
[#6]-&!@[#7]1:&@[#6](=&!@[#8]):&@[#6]:&@[#6]:&@[#7]:&@[#6]:&@1=&!@[#8]
Doing Scaffold_21 with 558 mols
[#6]-&!@[#6]-&!@[#7]-&!@[#6]1:&@[#6](-&!@[#6]):&@[#7]:&@[#6]:&@[#7]:&@1
Doing Scaffold_15 with 11 mols
[#6]1:&@[#6]:&@[#6]:&@[#6]:&@[#6]:&@[#6]:&@1
Doing Scaffold_14 with 12 mols
[#6]1:&@[#6]:&@[#6](-&!@[#9]):&@[#6]:&@[#6]:&@[#6]:&@1-&!@[#6]1:&@[#6]2:&@[#6](:&@[#7]:&@[#6]:&@1-&!@[#6]1:&@[#6]:&@[#6]:&@[#7]:&@[#6](:&@[#6]:&@1)-&!@[#7]-&!@[#6]):&@[#7]:&@[#6]:&@[#6]:&@[#7]:&@2
Doing Scaffold_08 with 13 mols
[#6]1:&@[#7]:&@[#7]:&@[#6](:&@[#6]:&@1)-&!@[#6]
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">counts</span> <span class="o">=</span> <span class="p">[</span><span class="nb">len</span><span class="p">(</span><span class="n">synscreen</span><span class="p">[</span><span class="n">synscreen</span><span class="o">.</span><span class="n">scaffold</span> <span class="o">==</span> <span class="n">scaff</span><span class="p">])</span> <span class="k">for</span> <span class="n">scaff</span> <span class="ow">in</span> <span class="n">synscreen</span><span class="o">.</span><span class="n">scaffold</span><span class="o">.</span><span class="n">unique</span><span class="p">()]</span>
<span class="c1">#scaffs['count'] = [x for x in counts if x<=100]</span>
<span class="n">scaffs</span><span class="p">[</span><span class="s1">'count'</span><span class="p">]</span> <span class="o">=</span> <span class="n">counts</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">scaffs</span>
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<th>scaffold</th>
<th>ROMol</th>
<th>smarts</th>
<th>timed_out</th>
<th>count</th>
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<th>0</th>
<td>Scaffold_05</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
<td>[#6]-&!@[#7]-&!@[#6]1:&@[#6]:&@[#6]:&@[#7]:&@[...</td>
<td>False</td>
<td>265</td>
</tr>
<tr>
<th>1</th>
<td>Scaffold_10</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
<td>[#6]12:&@[#6]:&@[#6]:&@[#7]:&@[#7]:&@1:&@[#6](...</td>
<td>False</td>
<td>502</td>
</tr>
<tr>
<th>2</th>
<td>Scaffold_09</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
<td>[#6]-&!@[#6]1=&@[#7]-&@[#7](-&@[#6](-&@[#6]-&@...</td>
<td>False</td>
<td>344</td>
</tr>
<tr>
<th>3</th>
<td>Scaffold_16</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
<td>[#6]-&!@[#8]-&!@[#6](=&!@[#8])-&!@[#6]1(-&!@[#...</td>
<td>False</td>
<td>269</td>
</tr>
<tr>
<th>4</th>
<td>Scaffold_07</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAAABmJLR0QA/wD/AP+gvaeTAAAYBElEQVR4nO3deVyU1f4H8M9sjCigKCCgJogiiIiKhsvFLTRzKe/1R5ZJ1i3xij9JX/0Kl5sOmoXaNUXRSMutxdxxXy4pmuQygCGLyBYgIIMCAwKzMef3x5NkqTCDnibu/b5fvJCY5zzzHeYz5zlznjNPIsYYCHnaxJYugPxnomARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4oGARLihYhAsKFuGCgkW4kLasWXp6ekZGhq+vb8+ePaXSFu6E/AdrSSbCw8PT0tLOnj0LQCaT9erVy8fHp0+fPsJ3b29vsZg6wv92IsaYWQ2ysrL69u0LYNSoUbm5uT///PPv9tC+fXsfH5++ffsOGzZs5syZT7NY8sc6qj7KwDpKOmZrs9/o9IZZbc3usRYuXGgwGMLDwz/99NO33nrr7bfftre3r6+vV6lUwvExLy8vMTExMTHx0qVLbdq0mTZtmrl3QZ6Wq1ev2tvbazQaoS8wV6GuMMwxDEC2Ntvctub1WFevXg0ICGjbtm1ubm5tba2Hh0fjTXZ2dj4+Pr6+vm5ubk5OTjk5OVFRUd7e3hkZGebWRJ6cWq1eunRpTEyMj49PWlra1KlTP/roo549e5rSVs/0a1Vrb2puvtThJRFEjlLHG5ob5vZYYOYIf+01AEuWLGGMVVRUbN26dcGCBUFBQc7Ozg/uU6FQ1NXVicViuVxuMBjMugvy5Pbs2ePi4gLAyspq2LBhcrlc+Hnu3LmlpaVNtz1fc9473RtJECWJ0urTWlyDOcE6c4YBh557rqqq6uEby8vLv//++w0bNoSGhp47d44x1rVrVwD5+fktLo6Yq7i4+G9/+5vw8h4+fPjRo0cZY4WFhaGhocKb97Zt20ZERFRUVDzctqKiYl7uPFGSCEnwSvc6V3PuSSoxOVhGIwsIYABbvdrEFqNGjQJw+vTpFpZGzNHQ0BAbG2trawugffv2y5cvnzFjhlQqTU5OFjbIzMwMDg4WiUQA7O3tly1bVl1d3dj88OHDXbp0CfxXoCxZFnErQmPUPGE9Jgdr3z4GMBcXVltrYotZs2YB2LRpUwtLIyZLSUkZPHiw0FFNnDjx448/7tChAwAbG5vvvvvuwS2vXLkSFBQkbOno6BgVFXXjxo3x48cLvxk1etSNmhtPpSTTgmUwsD59GMA++8z0Xa9evRrAggULWliaRRUUFCQmJqrVaksX0oza2tqIiAiJRAKgS5cuMTExo0ePbkyYMBn0sH//+9/PPvussJm1tTWAjh07fvHFF0aj8WkVZlqwamrYnDnM25vpdKbv+sCBAwAmTZrUwtKe0MGDLC7OrFdCo6ioqKlTpwp/dxcXl6CgoPDw8NjY2AsXLtSa3GH/MXbu3AlAKpXOnz9/8eLFwjjd2dl5x44dzbY9c+aMs7Ozg4NDYGBgWVnZ0y3MhGA1PkNarVm7vn79OoDevXu3sLQnFBPz63dzFBYWtmnTRiQSeXt7C6/mB0mlUm9v7+Dg4OXLlx8/fvzpl22OiooKAHK5PCUlJTIyEoBYLJ4zZ84j3109UlhYGICNGzc+9dpMmCAtKUFYGDZtgpVV8xs/wEOjEYtE+Tk5DZs3S8aMQe/eZjUHAI0G165BpUKnTvDzg42NGW1dXbF/P27fxrffYswYdO5sYjuFQqHRaF577bWvvvoKQElJSVJSUkZGhjD9m5aWlpmZmZmZuXfvXj8/P5lM1jhk+eO1bdsWAGOsf//+Hh4ely5d+uCDD4YOHdpsQ71e7+XlBUB4C1lfX//UazMhWK6uOHIEEglOnoSrK/r1a7bFnTt3quPje+TmdrGzK1KrC7Oy3CsrMXYs7g8wTaJSYds2NDRAr4dUivPnMX06unc3qW16OmbNQvfucHTEihU4fBiTJ5vS7ubNmzt37pTJZAEBASNGjFizZk1AQICrq+vk+821Wq0Qsu+//37btm3h4eEWnAGWy+USiUSn0xkMBltb2+PHj5vYUCaTFRQUNDQ0CIfOurq6p16bCWeLp0zB5Mno2xcvvYSxY5GZ2cS25eXlCoWip4fHvKVLodf36tgRQHZFBfR6nDoFrdbUuhjDN99Ao4FeDwAGA3Q67N4Ng8Gk5s7OuHMH2dno1QsAsk09I7FkyRKDwfDWW29t2rTpwoULwtH8QXK5fMCAARMmTFi6dKlYLM7Ly2toaDD1QXEgHKxb0OUIDYXJLR49lsnLEAYPxrhxUKkwevQjs1VaWrpgwQI3N7fIyMjqmhqxWHw2Pz+3osJOLn/v9Olrt2+joQGxsTDl9V1djYMHoVYDWBz/3JXiLp8pBwFAQwMKCkyqtlMndOyI6moIpwRMC1ZSUtL+/futra3d3d1v3Ljh6en5xhtvPLzZ9u3bO3XqpFAoXF1dtVptUVGRSSXxIRwNW5AMoaEQLAv1WAIrK+zbhxdeQFkZxo1DXl7jLWVlZQsXLvTw8Fi3bl1dXV1QUFD00qVqrXbMjh0FarVIJEotK/OPjX394MH8vDzs3Ysvv3xsPtRqnDyJDRt01zJilf6Tvpnu3qEyudTll1v1etP7Hgjnxdq0AcCKTXruFy9ezBibO3fuxo0bAXz44YePXGrm5uYGICcnRzj1lm16SRwIHU8LkiEES5insGiwAMjl2LcPo0YZRKJXp0//+eefAZw4caJ79+6rVq3SarVTp06NjY29d+/evMjIC/n5ndq2XTZqlDI0NOIvf7GSSHb99FPvjRtnHzlSmpGB7duxaxeEbqyqCkYjqqqESGkuJsUkDugZHf6Po5OO3ex1XdX5Ofe8pBKXNw+9dKHgGVy9iqwsU4qtDRtZvnZ45Thx2rmOaSvTmt3+woULp0+f7tChg62tbVFRkZ+fX+OMw+/06tULQE5OjvCDZYMl5ONPGCwzl820bYvDh2dPm7b7xAnl2LEJCQnDhw+3sbEZO3bshAkTtm/fPnv2bAAODg5zX3llgb19e6kUjEUFBc0dPPjD8+e/TEn5PCnpq9TUeQEBEcOH2+fnA4BUKoyc6nS6bSmZUT98cau6AwDfzqp/jjj/P30yxCI20KU07NjE/Zl9Ts3YNXTvXrz8Mjw9m660ely7kpKLLh1H6nT3mK7KaNSIxW2a2H7hwoUA3nnnnZiYGACrVq163HJFV1fXdu3alZWVdevWDUBOTo55f8OnqsWHQqGrEx6jRcdYjWxt13777eDBg3NyckaPHn3v3r2YmJjbt2+HhYVduXLF0dFx2bJlubm5ig0b2oeHY9Ag9OgBb+9unp6xkydfDwsL9vGp1+tX/fCDR3S04uzZGo0Gen2tVrv+xx97RUf/7/ED3dpv8+2s+mTcmSFdbmXedRLLZQBmD0qa0S+1Rmv1wtczrhZ2xp49aO7plMt7AtBocuRyN8Co0+U1sXFcXFxiYqKTk5NGo1GpVIGBgc8///zjNhaJRMJ6IeFJbdWHQgGPHsu8ZTON7t69279/fwDt2rUT9uPq6iqMsR7bJjeXbd7MFIors2YF9eghtHKxtQ328XG8v5PBXbpsmvTKq31TxSIjwBwdjBoNYwYD++orw9LIV32vA6xDm3plaCxbuZI1uW7i3r3LSiUyMgZmZ09QKlFZeejhbYxGY2FhYXx8vDBaio6O9vT0BHDx4sWmH75wlFyzZg0sOAPMGGNs3LhxAE6ePGluw7CwMH9//9jYWACBgYFPvbAWBosxVl5eHhAQ8OKLL3br1q2ZSDUyGll6Olu/nikUP/z974HduwPoamcHYGi3bhsmTAjp108iFgd477CyYiEhLDv7fkOdjm3fblga+bJPGsAc29VeD9vEli9nsbHshx8eeUrAYKjIzAwoKJitVp+8e/drna5Yp7tVXX22vHzLrVvv/9//hfr6+jZOrD/zzDNyuTw1NbW2tvZ3Z20fKSIiAoAw42BlZWXBNWdTpkwBcODAAcbYp59+unXrVr1eb3rzy5cvA/D393/qhbU8WIwxo9FYVVWlNfNUDzMY2IkTLDLSuGzZ635+AKb26XPm9ddFAIA2bdr8859ri4oeaqXTsW3btB+smOh5E2DONjW576xnCgVbsYKtXcvu3fvttsXV1ecqKw+Wl2+pqjpSV5eWnNxWqUTj18svDxci1blz58DAQGEY3q1bt7y8PFMewdatWwHMnDlTGGaZ2IoHYd5/1apVt2/fFo4eXl5ee/fuNeV0clZW1rBhw5ycnD755JOnXtgTBavlDAa2ejVTKA5MmwZgcu/ehqVL+7q4hIaG3rp167GtNBr2+efaD1ZM6HVzfM/suiUfMoWiauHH59/e8eWcK4sWseBglpISkpxsI6QnJcVOrT5dWrpKo/k5Kcnq2jWnGzeG5+e/UVq6MinpUHJycuOCpNraWmH12DPPPGPKysTr16+/++67Bw8eHDNmDIBTp0614G+waBG7fJlt3sw2b2aM/fLddBUVFaGhoSKRqGvXrmKxODg4uPFQDsDX13fPnj2Pa1tXV7dkyRKZTCaMYbJ/PTQ8NRYKFmOstJStWJE6Zw4ALwcHplAYfvyx+Vb19Wz16tolKzX/XLEo8MLlWVve6J8CsMav+PgQpRLXrjlmZg7Nz5+pVp+5c2dHTU2CRnOz6R1XV1cLZ9kmTgzQ6YpNeQRZWVmenp4SiWTMmDHFxSY1edCWLb+kavVqduIEW76cmX4Q27Vrl6Ojo9DBBwYGNi4+njNnTnR0tDDTBmDYsGEJCQm/a5uQkCCcKBSJRCEhIXfv3jW3clNYLliMsRMn6pYsEYtEVhKJYelSdvasSa3272cKBVMotrx4ePOko4v+cmFI16IQv5+WL2fffMMKCkoNhkphQ7X6TE7OX8vK1qek2CmV4oaGZkaBVVVVISHPJye7paV56XS3m9gyPT19+vTpwiSQvb09AGtr6/fff9/EJ+nyZRYayj7/nN28yUJDf+mroqNZ794sPJypVE21zc3NbXzHOmLEiMzMTPbQ4uN33303MjKyU6dOAITzTkLbxk5O6NISExNNqbZlLBospZIpFMLgPe+dd9j+/Sa1OnqUKRTfBe/dOOHYzXnRof5KFhnJvvzy4Q3v3v1aqURubnBamqdSibq65j8aYDBUZmT4K5VIT/fV68sf3qCuLnXdurnC9I+VlVVoaGh8fHxISIjwGxsbm4iIiCaWB6rVbN48JhYzgO3c+Zub4uKYSMQA1qEDW7nyd4NGxhjT6/Xr1q0TBlL29vaxsbG/G0g9uPjY1tZ2/vz577//fmhoqHDrnj17hE7O2tp62bJlZo+MzWTRYOXlMYVitLs7gNMhIWzLluabGAxs3brLs7aIYHS3r9R9sJwpFOyjj9ij1qndu3dFqURGRv/s7IlKJSorD5pSlF6vSk/3USqRnu6n1//aA9XVpebnhyQlSS5dGmhlZRUSEpKTk9N46/Xr14ODg4WOxMHBISoqqr6+/nd7Pnr0UpcuDGAyGVu4kD38Njo1lQUHCxvo/PwCHtxJUlLSwIEDhf0HBwerHt+tpaamNlYiLD7OyMgYO3as8JuRI0feuPF0Fh83zaLBUquZQhHq7w8gZuJEFhXVfJNLl5hC8VyPPIAtDjzPFAr2xRes/BFdC2PMYKhSKpGc3K6wcL5Sidu3Tf0YiE5XmpbmmZRkpVafYozV1V3Pzp6sVIqUSiQnWxcWhpeWPvodxsWLF0eMGNE4hREbGyvMRJSUlAhP9pAhRQMHMqWyqXs/e5bNnLlb2Imbm9tnn302b9484bDr4eFh4huF+Pj4gIAA3H+jDcDJyWnXrl0m/gWenEWDZTSyDz9cPXYsgPlDhjCF4hGv4gdptWzNmjOv7xSmSe9GrGLfftv0PdzaPeLuyhF1h9frXnjW8MG7ppem1Rap1SeF2YrKysP3Axpuyrg+Li6u8ZPHPj4+s2bNEj48Y2dnFxv7TUODSQUcOnRI2IlwkJXJZIsWLXq4F2zamTNnBgwY8N5777355pucBumPY9FgMcZiYg6+8gqASZ6eTKFgmZlNbZyQwBSKIV2LABYVdIZFRrLbTQ2xGWNs6FAGsH/9iwFs9GhzqxNmK7TaIpUqRq9vclD9Ww0NDXv27BEm9IXPV06aNKmwsNCse4+Li5NIJF5eXsL7g8ym/zh/Mha9LAxjqKv7dTEggH37oFI9euP6eiQmHrpxgyFqSNeseQFX4Ovb/IJjYaGfsDzQ/JN6YrFcJnPW6fIdHcOkUkdzGoqDg4PT09M3bty4e/fuuLi4I0eOCLOpppNKpQ0NDe7u7sL0AZczetxY9NJWWVnQaDw6dhSLRPmVlQajUQpg82bY28PR8ZcvV1c4OKC4GIcONdTXL4mPzygv3zypoK38WYwa1fxdCKuyKiogl6O4GHV1eODka7NsbEbY2Ixo2YPD/U+1t7h548qFFi9hsCCLBis7Gw0NbaTSrnZ2ZbW1Pdev9+3c2cfJqY+jo7+Li5eDg0RYuCIWgzEwtis1NaO83N3e/u8DBsDfH/b2zd9Fr14Qi1FVhddeg1yO+nqzgmVZjSsX7OzsQD2WGSQS4d/s8HCP9esL1OoCtfrozZvCL61lsj6Ojn2dnHwcHft17uzZqdOKhAQAK8eMsZJIEBho0l1MmYK6Opw4AbEYpaXo1InPI+GicRGfcM0VCpbJvLzw00/Q6awkkpzw8Mw7d9JVqusqVZpKlaZSFVRVJZWUJJWUNG4uFomcbWyulpS86ucHrRa2ts3fRZs2wAOfYGtV3Nu1Kxo6VNa+PdzcvvX2NgqfK2klLBqsHj3g5YXMTOj1cqm0v4tLfxcX3L9eV7VWm333bnp5eUZ5+bXbty8WFk7y9NybkRF9+fKcgIBeRqMZd9T4CbZWxcba2ubHH+HsDBcXZGaiutrSFZnB0telnTIFnp5IToZWix49MHQoxGJUVEClsisv9y8v9y8uRm3ttpSUUzk5qWVlM/r123Ht2poLFz5fs8a8e2mNhOVijeNCOhSaQSSCjw98fH7zSxcXuLj8+p8nT84AVpw/n1Fe/o9Bg75OTd1+7driU6fcJk78g4v9ozXmSfihVb0rbA2XNx4/XjZ1asTIkQBik5Km9e2rb2jYt3YtzDoatkZWVpBKoddDLgdaWY9l9lWTLUVXVNTT17dIrd42cWJQamrX0lIcO4Zx4yxdF2dz5sBoxNSpSEjAuHEYOdLSBZmq1QQLwLHQUP/jx52NRnTsiPR0jBiBs2fxH39N+UOHfpkrmT3b0qWYoTU9KxMXLXKuqUFpKbp3h0iExEScO2fpovgrKcGLL8KiV4hogdYULLi7/3LRmJQUeHnBYIBCYerVHFqv1jlX0poOhQCQm4sBA1BTgwkTcPIkBgzAX/8Kf3/cv4om+ZNoVT0WAA8PjB4NsRhaLRYswOTJMBiQnPzgRUrIn4Gl57FaYNAguLujrAwlJaipwaBB0Ovx00+4/+lq8mfQ2nosAFIp7O1RU4PevRvP/7SuycP/Bq0wWF27AoCtLbKyIBIBgEyGB/6vPuTPoLUN3gGoVNi6FY2n+iUS2NoiLAwymUXLIr/RCoMFoKwMx46huBgSCby8MH58K1q+91+idQaL/Om1wjEWaQ0oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWIQLChbhgoJFuKBgES4oWISL/wfyVwy9unwt5QAAAABJRU5ErkJggg==" alt="Mol"/></td>
<td>[#6]-&!@[#6]1=&@[#7]-&@[#7]-&@[#6](-&@[#6]-&@1...</td>
<td>False</td>
<td>76</td>
</tr>
<tr>
<th>5</th>
<td>Scaffold_02</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
<td>[#6]1:&@[#6]:&@[#6]:&@[#6]:&@[#6](:&@[#6]:&@1)...</td>
<td>False</td>
<td>106</td>
</tr>
<tr>
<th>6</th>
<td>Scaffold_06</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
<td>[#6]-&!@[#6](-&!@[#6]1:&@[#6]:&@[#6]:&@[#6]:&@...</td>
<td>False</td>
<td>273</td>
</tr>
<tr>
<th>7</th>
<td>Scaffold_01</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
<td>[#6]-&!@[#6]-&!@[#7]1:&@[#6]:&@[#7]:&@[#6]:&@[...</td>
<td>False</td>
<td>204</td>
</tr>
<tr>
<th>8</th>
<td>Scaffold_13</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
<td>[#6]-&!@[#7]-&!@[#6](=&!@[#8])-&!@[#6]1:&@[#6]...</td>
<td>False</td>
<td>17</td>
</tr>
<tr>
<th>9</th>
<td>Scaffold_19</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
<td>[#6]-&!@[#6]-&!@[#7]-&!@[#6]1:&@[#7]:&@[#6]2:&...</td>
<td>False</td>
<td>256</td>
</tr>
<tr>
<th>10</th>
<td>Scaffold_18</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
<td>[#6]-&!@[#8]-&!@[#6](=&!@[#8])-&!@[#7]-&!@[#6]</td>
<td>False</td>
<td>38</td>
</tr>
<tr>
<th>11</th>
<td>Scaffold_04</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
<td>[#6]-&!@[#7]-&!@[#6](=&!@[#8])-&!@[#7]-&!@[#6]...</td>
<td>False</td>
<td>78</td>
</tr>
<tr>
<th>12</th>
<td>Scaffold_17</td>
<td><img data-content="rdkit/molecule" 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caMhX5+Jt5qyBDExsI4sN4Rd+7cWbt27S+//ELTtL219dJJk1ZFRDAhWrNavfPy5U+Tk0e5uh6aM2ccey5oyhSTJfymdCwsvp2gkfr6em9v7+rq6uTk5ClTpnB5aprGb7/BMKcpK3eK+9un+TWVn02bttrYkFB4xsrqzb177xcWArASCGKGD18SGDhr2DBjAJtbVXUgK+tQVla5qQGbNmTI9//6FzNuZCsSfTx1qvm40aBBiIvrTC9dT10dUlNx/To7SGc81Krz5/Oqq/vb2T1uaprr6/v17Nlu7NBNIkFUVNfGzTMzM1evXp2QkACgf58+22bOfMlQCUd66FByYeGFhQsjvb31R4vFmDmznTJxHQuLbyfI5pNPPlm7dm1UVBTz53FGcjKSkpjNqmbJ5P1LFJqSZ0ev2DkrSP+gCwRYsACDB9M0nZaWduTIkSNHjjDJGgPs7ef7+b0eGGic+lBptf+8e3c/y4D179OH8RdzRo36evZsT3YpYmtrREZi8uSuDGFXVCApCTIZe59Kq4354YeE/PzFAQH7n37a5PgxYxAT083i3mlpaatWrUpJSfntxRefHT2a2Rnzww8nc3PjX3klZsQI/XFCId5+u52iyx0Iq2ecoJG6ujpvb++ampqUlJSItv33k3HnDo4eZR59tVYw4/v5iQVDAjxKU//7kK3IMNoUF2c2MFNTU3P06NGdO3cyuRsCiprm7f1aYOAzo0aJDL7sUX39D7dubc7IEAoEcrV6+6xZC/z8TJoeMQIxMd0teV1cjB9/ZCfArEtKWp+UtFYqXccORseNa6l91W3O//TTtNxcyuCO5x09ejQ7+ydW2IDx4/Hss+2cwWrdunVtfadUKmNiYsrLy1euXDl//nyuLrodbGxs5HJ5cnJycW7ufHd3XL6sr+zr7NzFjI6yMvztb8Z45a2Ts3+RjfGwb7yw8HA/W8O0WmgowsPNfieRSAIDA5cuXRoXFwdAJpPdq6g4KpPtv369srnZx8nJSSJxsLYO9/ISW1n9IpO96u+/iv0kUBTmzUNkJAcvgHBwwIABkMmMI7qZxcUJ+flBAwc+NXQoTdMPamtr1GqnhQvRiR5rJ/EZO5ZSqWAY1TuRm3uzrCxmxIgJzNCdQIBXX20/WGwv0W/16tUymWz06NEfffQRV1fcIe/Pn+8okZy9ePHib7/h/n3cvo3Tp7Fli9mkfcdotcjPx5EjxkHwHZeD9l6daCPU/POlv3s66PuGGDYM06e3c5rAwMB9+/YVFhUx8UBZY+MXFy8O37HjekkJc0C9UgnAhe2AKAp+fjD4EQ7w9mZPh7OXRTSoVD7bt0/Ys8dCJchuEhJi3LQzyz61tu5wXrxNYV26dGnbtm1CofC7777jO7RqoaKi788/vzVpEoANycn6nSoVVCocO4bDh5GSgsxM/OtfyM3Fw4eoqEBDg/kyBJUKZ87giy/www9GD3Luvs/yM9EU6INzjk0eaBgcd3HBCy90JvpxcXFZtmyZTCa7evXqa6+95uvhYZxuq1UoAJgMjguF4LbzAWDuXKOFYKdw6bf5WOzKmjG0NcuX78S7Dtr0LzKZTCAQvPnmmz0QWumhafz971Cp3g8J2XH58um8vMzi4snGwT2dDgUFKCiw/FsrK9jY6Et9VlToa00buFfZ78Vf5mp0grXS5JfHGfJdJRK8/PKTuirGgGnv36e+/565ZnNhURRmzeLefohEcHREVRVMl0UIBQKxlZVKq1UqldbcvneplbBaLJZabXEOgE2bFiszM1OtVos7zATikPx8ZqWKs0Ty1uTJAD4xGq0O0WrR1ISqKpSU6P9sFnuuTqqR27wwRrZ2apJ+l0CAefNMSnQ+CVbGHj5F1SgUAJyMwrK356xWuxmGe2F2m/XLfjhf5MOq4mx5IUa7tCmsJUuWUBS1e/fuSkPqI+/k5BjnNJaFhNhbW5/IydmakZFWVJRdXl5cX9/cyZV3rfq5W2ac2THr1LfP/LPlGYuJ6VYSH2v1gbnFMgyrco/pmmazBWTNrG4j3y0CHS92bdMVBgYGzpgx4/Tp09u3b+9mmmhnYZXP72drG+zpebu8fJnpwhWxlZWTROJoY+NoY+PXf2u9cpSjjcJJonC0UTjaKJxs5MbtvGrn/z42595bO+2tVU//7eX4V35sOUtQUIdZHx3AStQ0FxZ/8WgbVRjseBWWXN66RaAbwgKwdu3a06dP79ixY9myZU6cBw2tYfWWc6qqLhYWyjWakEGDBBRVq1DUyOW1CkWzWv24sZEZjdToRl4rGWvxTHZi9S/zfp45LG/jxfANURdMvrO25mChBMti1cjlAJyMF8+fsAyu0M7iOh8+1ru20SLQPWEFBwc/9dRT586d2759ezvDXZzh44N796BS6Wh68e+/yzWaV/39D86Zwz5EpdUyCqtRKBqVKZXynFqFTY3cplZhU6uwqVFImG2RlQ7A5IHFV0sGFNWZvtXDwaGdZI/O0o4r7CmL1WS2dp5PV2jeK0THRZc6GHVcv379uXPntm3b9u677/JutMaMYRbmbs3ISC0sHGBvv7mVaRFbWfXv08eQ4lIJtBn/nc4bBmDt1KRPkqeafMFJiQSDsHQ0Xa9UCiiqJauOu1FKc9pyhTwF7223CHT839jBsxsSEhIVFVVXV7dz585uXWJnEIsRG5tTV/dRYiKA3TExTt2+SYP61rv3aaxXiu9VGqb6a2vNstG7gkFYdQqFjqb72tgYM214fNfy/1fBe8dOYf369QC2bt1a2/qFd1yj8/VdnJQkV6tf9fefM2pUyxdWVnB3x7RpCAtDYCB8feHjgwED4OwMW1uLrm3msLw/TbwKIHro/Ztl7vP/8ZxWZ7j3J050d1G54ecWRkd73mL1lCvkLMZiCAsLk0qlSUlJu3btWr16ddevshNs27YtNStrgLv75hUrUFoKuRxCIQYOREgIhg1r75cqFRQKKBQoL9e/stYwsxY4oLSfrfxK8YCdmUHvBV8CgMZGJCZi1qyuX6ihV2g+iIWeiLFaBkU1Gmuh8D8zeO9UGPvxxx8D2LJlSwNrlRzn5OTkrFmzBsDuvXudXnkFy5djzRqsXIn58ztQFQCxGA4OcHPD2LF4+22MHGk0Y7Yi9a7ZJwCsPj+toMYwyHTlinGGtSu0Y7H4FxZMK3zw6AoNBUstBO+myTyt6ZSwIiMjp0yZUl1dvWvXri5eYkfodLrFixfL5fJFixbNMe0JPjF2dpg3D8uXw5CqO2t43jzf7Ga16M2TMfpjaBrx8Uar9sT8W4TFmgVhz0PzFbxXVBjX9FpwhXl5uHevnV93tuPNGK2vvvqKJ6O1bdu21NRUDw+PLVu2cHNGW1u88ILRbm2fddpJojiVO+znbENGUXk5urxww+gKzQaxwGeMlZNj3DSZhxYKATQbX6jJBXfv3t32/vvGOQwLwbtWi/j4dgo/dVZYUVFRERERVVVVe/fu7fr1tkF+fj4j3N27d3M5qNG/vzH3w71P48bpCQDePTWzRm4wKsnJeMIJK7VanRYfX224i3qLxe4J8tQrLC1FXp7xE3seeunkyffffXeFuzsn9b2USuX69esn+Pkt+/HHVIPFYqI6rU6nZE8RqlQw5A615gmGCpkAaNOmTY0cvmwd0Ol0ixYtampqWrRo0TPPPMPhmQFAKjUmGiwJuDbNu6Cssc/KBEMCVkePneEo7bVr17Zv3z5v3jxXF5fwuLhTubnMV3phsa3UtWsc/wkMKSlsx822WM4SiY+Tk6NKhW4v901LSwsICFi3bp1Krf6v8ePHGJL071ZWMjnc5tO1bZvJJxBWdHR0eHh4ZWXlvn37unLVbcC9E2QjFMKQGE5R2BN7wkao2X8t4EKBYVFAYSFu3mz9O50ON25g61a88UZBv379Jk6c+N577x09erS+vt7Xzc04amWhV5iczEeBPBQUsB8AxjfVsBvS6ZCQgIMHu1aytq6u7t13350yZYpMJhvm7JywYMHhZ5/tZ2ur0mrXJyVN2LtXqdEcnDPHxOnTNAeukGHVqlUAvvzyS676IHw5QTZDhmD8eGZzRL+qD8LTaFBvxMcoNIahlrNnjYVl79zB11/j+efh5gZ/fyxbhl++719f3+Dj5DTfz29fXFzRsmW3ly592fDSSgvBO0W1H9V2BZqGUsm+i3NGjhzRr9/iY8e2X7pk4p4ePsShQzhyBI8fd/70x48fH+vru2PHDiuK+iA8/PbSpdO8vQGkFRUF7Nu3LilJpdHM9/N7euRIk58JBLBUhILhiavNBAUFZWZmbt269b333nuiH7ZGp9NJpdLU1NRFixYdOnSom2drD7kcX3/NZJOqtFb+e1+XVbiukyatlRryveztER2N4cNDQnApSx8kDXGsjfR+EDmkINL7kqeDydIXlVabWVx8oaBgx6VLVXK5yfIVAMHBmDGD4z9hwwZ2CpRSowk/eJCpszDM2Xl9ZORLvr4C9kAxRWH0aERFtZ9zVlJS8s477/z6668Awry8vomLY9xfnULxcWLi15mZOpoe5uy8NzY2qvUaXYkEK1a0NfH6xMI6ceJEbGysu7t7fn6+pHs9oK1bty5btszDwyM7O5v3icgbN3DsGLOZWug19dAikZUu60/7xrgaogSKAk1/lR56s6w/oydvJ5OZBq1Od6Os7GJRUdrDh2fv368zuKEds2YtDggwKf0zaRJmz+b4+r/9ll0BC52ssyAQwN8fU6fC3t7sfDqd7q9//euK5csbGhv72tisl0rfDgpiXPzxe/eWnjjxqL5eZGW1LCRkvVRqofKMSITZszFhQlvX25XCa5MnT75y5cr06dOjoqIcHR2dnJwcHR3ZG53JO83Pzx8/fnxTU9M//vEP7mN2ixw5gvx8ZvP147HfXAuMGFyUvOhQOxm2Opq6WdY/s9gqPufLlMLCekMeIgX4urlN8/aO9PaWDhli4gpFIkRHY+JEji8+Lw9Hj7bOKWBWsa5MSMivqQEQ5Om5Ydo0c+siEiEgABERMCS+3rp167XXXrt8+TKA2BEj9sTGMmshSxoa3jl16leZDKYGzDwRWSSCnx9iYtA2XRHWyZMnDx8+/NNPP7V1gK2tbWu1GTcYPvzww8zMzIULF3777bdPegFdpLoae/YwDqVWbh39/YLVESlzRlmIh/JrnBLyfRLyfS4UeFc1S5xsmuuUDjpa6+PkNN3HJ8zLa5q3t2dbqwVFIrz9dmsLwQFHjyInx2JOsEqr/fbGjXVJSaUNDQDCvLw+j4qKMKsQwVRfsrXNuHJlyurVGq3Wq2/fXTExsSNGANDR9F+vX19x9myDUmlmwPQwmcoUpX9yDGFrW3SxuO2jR49++OGH2tpaY71k9oaqE4tGPDw8KIq6fft2T6QQGklNxYWWvL/TecOMWaZR3y2Y6ytLLBiS9GBIeVPLonVvp9rIIQXRQ3dFDHYc0KFcRCKMHw+eCvXqdDh2DHfutDVP16RSfZ2Z+UVaGjNsO93H58vo6AmtqhDqaHrqoUNjXF2/io62t7YGcOvx49eOH7/86BFMDZgeGxsEB8PFBTodXFzg7t6ZdU1dFFb7NDc3t1abcYPZfv7552fOnDnUrL4P31RX4+uvjd2r03nDfs729ejTsCHqwqidb92r0qfWuPdpjBhcNN0nf7pPvo9TjeVTtV6mIhKhf38sXMhhtXQLPHyIS5fw8CHUaojFoGmYzoXUyOXbL1/ekpHRoFQy5a/+EhU1zDSE1+h0QoEAgFyt/iIt7fPUVJVWO8Defufs2c+ZLYccPx4zZnRh2T4vwvrPpaQE331njFRO5w17UOt4tWTAx1OT/9+vz3k61DNh+/B+1e2dRCyGmxtqa6FQ6OutMVU6g4MxdSonRdKfAJpGbi4uXDAbX6hoatqckbH90iWFRsOUv1orlZpZ3OQHD16Pj79XWSmgqMUBAUYDpsfREbGx6OqT38uEVVuL3buNroQRVszwnE+Sp5Y22pssuDBDJMKgQfDxwaBB8PTU97GrqlBcDLUajo7w8upKZWWuoGncuYOEBHZJUgBFdXUbUmeBascAAAPcSURBVFKYytu2ItHigIDVU6a42dm1rt8c5OnZ8jOBAJMmYdq0jsuAt00veye0oyNEIrMYhckyLaxrtWxLKISHB7y84OODwYMtmKJ+/Tpfg4pfKApjxmDUKGRl6V+BAQDw6tt3X1zcu8HBHycm/iaT7bh8+bubN5/y8Ul68KCyudlWJFonlb4fEiJkj0W5uyMuDuzKXl27ot5lsQBkZJhVNTbHxgaBgfDxgZcXv9EST6jVyMzExYtmM0v/evz405SUo9nZbnZ25U1NFuo3M+/jCA3t5jspGHqfsHQ6HDiA8nIL/XaKgkSCN97o+nts/nNoo67pJ0lJa5OSgj09MxYvNjl++HDExMC0Inx36H3vKxQIsHAhvL3NAwixGM7OWLz4j6AqAGIxwsLwzjtm9WcnDRwIs8lNAC4ueOUVDlWFXhdjMYjFeOUVFBXh+nUUF+uHZ5jCQz3/EjJeYbIdt26F2ZIediQgFrdfyKlr9EphMXh58fVewv8oJBI89RTOnQM7j9koLGatCnsGnSN6nyvshUyahNBQZkLGxGKJRPD0xEsv8WGne7HF6lVIpRg5EklJdo2NYITl7o6wMPj68uT9e1+vsHdTUVHh5ubm4uJSwenii9YQV9i7sLW1BU+LEE0hFqt3QdO0lZUVAI1GI+h+1Z22ITFW74KiqK2LQ9WKRq1KIbDp1qsG2oe4wl6Hqqm2qaZcrZR3fGg3IMLqdYhsbAEQYRE4RmQtAaBS8hu/E2H1OhhhqRXEYhE4Rax3hcRiEThFb7FIjEXgFpG1LQCVglgsAqf0jMUiA6S9jlHBM+379e8/hLu33lmCTOkQeIFYrF5E3rXE1J+39/f2bagqfeb97da29gBoWkdR3EdERFi9i9GhMcFzliR8t6GmrKjyUV5O5llXrxER897lvCEirN7F3UunqksKqsseTH1pWeWjPB//KROiXuSjISKs3sWo4FnBc5bcTvnnjYS/S+ydbey4XJnDhgird3En/UR16YP6yuKoBR8+ftDtdwq1DekVEniBDJASeIEIi8ALRFgEXiDCIvACERaBF4iwCLxAhEXgBSIsAi8QYRF4gQiLwAtEWAReIMIi8AIRFoEXiLAIvECEReAFIiwCLxBhEXiBCIvAC0RYBF4gwiLwAhEWgReIsAi8QIRF4AUiLAIvEGEReIEIi8ALRFgEXiDCIvACERaBF4iwCLxAhEXgBSIsAi8QYRF4gQiLwAtEWAReIMIi8AIRFoEXiLAIvECEReAFIiwCLxBhEXiBCIvAC0RYBF4gwiLwAhEWgReIsAi8QIRF4AUiLAIvEGEReOH/AM/6ehsrRdfzAAAAAElFTkSuQmCC" alt="Mol"/></td>
<td>[#6]-&!@[#7]1:&@[#6]:&@[#6](:&@[#6]:&@[#7]:&@1...</td>
<td>False</td>
<td>157</td>
</tr>
<tr>
<th>13</th>
<td>Scaffold_12</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
<td>[#6]-&!@[#7]1:&@[#6](=&!@[#8]):&@[#6]:&@[#6]:&...</td>
<td>False</td>
<td>61</td>
</tr>
<tr>
<th>14</th>
<td>Scaffold_21</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
<td>[#6]-&!@[#6]-&!@[#7]-&!@[#6]1:&@[#6](-&!@[#6])...</td>
<td>False</td>
<td>558</td>
</tr>
<tr>
<th>15</th>
<td>Scaffold_15</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
<td>[#6]1:&@[#6]:&@[#6]:&@[#6]:&@[#6]:&@[#6]:&@1</td>
<td>False</td>
<td>11</td>
</tr>
<tr>
<th>16</th>
<td>Scaffold_14</td>
<td><img data-content="rdkit/molecule" 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" alt="Mol"/></td>
<td>[#6]1:&@[#6]:&@[#6](-&!@[#9]):&@[#6]:&@[#6]:&@...</td>
<td>False</td>
<td>12</td>
</tr>
<tr>
<th>17</th>
<td>Scaffold_08</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
<td>[#6]1:&@[#7]:&@[#7]:&@[#6](:&@[#6]:&@1)-&!@[#6]</td>
<td>False</td>
<td>13</td>
</tr>
</tbody>
</table>
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<h2 id="The-measured-data">The measured data<a class="anchor-link" href="#The-measured-data">¶</a></h2><p>Finally, let's look at the measured data that's present.</p>
<p>Every compound has measured % inhibition values and an assignment to "active", "inactive", and "gray" bins in the cdk_act_bin_1 column (the assignment scheme for this is in that PDF).</p>
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<div class="prompt input_prompt">In [113]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'cdk_act_bin_1'</span><span class="p">)[</span><span class="s1">'mol_name'</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
</pre></div>
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<div class="prompt output_prompt">Out[113]:</div>
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<pre>cdk_act_bin_1
0 15276
50 1906
100 368
Name: mol_name, dtype: int64</pre>
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<div class="prompt input_prompt">In [118]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="o">.</span><span class="n">groupby</span><span class="p">([</span><span class="s1">'sourcepool'</span><span class="p">,</span><span class="s1">'cdk_act_bin_1'</span><span class="p">])[</span><span class="s1">'mol_name'</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
</pre></div>
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<div class="prompt output_prompt">Out[118]:</div>
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<pre>sourcepool cdk_act_bin_1
divscreen 0 11972
50 1180
100 207
simscreen 0 824
50 80
100 47
synscreen 0 2480
50 646
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Name: mol_name, dtype: int64</pre>
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<p>There are also a smaller number of measured IC50 values:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">cdk2_ic50</span> <span class="o">!=</span> <span class="s1">'None'</span><span class="p">]</span><span class="o">.</span><span class="n">groupby</span><span class="p">(</span><span class="s1">'sourcepool'</span><span class="p">)[</span><span class="s1">'mol_name'</span><span class="p">]</span><span class="o">.</span><span class="n">count</span><span class="p">()</span>
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<pre>sourcepool
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simscreen 26
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Name: mol_name, dtype: int64</pre>
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<p>I'll be using this dataset in future blog posts, but I figure it's worth pointing it out here.</p>
<p>Enjoy! :-)</p>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com1tag:blogger.com,1999:blog-684443317148892945.post-90919674732466210482019-11-15T00:26:00.001-08:002019-11-15T00:26:32.134-08:00Why the RDKit isn't available on PyPiThis keeps coming up and is an important question with a not-particularly-satisfying answer, so I want to capture the answer and explanation in one easy-to-point-to place...<br />
<br />
Let me start with an important point: it would be <b>awesome</b> if we could distribute the RDKit via PyPi so that people could do <span style="font-family: "courier new" , "courier" , monospace;">pip install rdkit</span> and have things just work. Unfortunately, I don't think this is possible.<br />
<br />
The core problem is that the RDKit is not a pure python package; it's a mix of python and some compiled extension modules (shared libraries). On the Mac and Linux those compiled extension modules have a dependency on another set of shared libraries containing the core RDKit functionality. On all platforms the extension modules also have a dependency on some shared libraries containing functionality from the boost libraries. All of these libraries, in the proper versions, need to be installed in the appropriate places in order for <span style="font-family: "courier new" , "courier" , monospace;">from rdkit import Chem</span> to work from Python. It's complex and not something that you would want to have to deal with on your own. It is, unfortunately, also not something that pip was designed to handle. Thus, you can't just "<span style="font-family: Courier New, Courier, monospace;">pip install rdkit</span>"<br />
<br />
Fortunately, we do have a solution to this whole mess: use conda to install the RDKit. conda was designed to solve exactly the cross-platform dependency management problem described above. And it does it pretty well. If you have misgivings about conda, please take a look at this post to make sure that your concerns aren't based on one of the common myths or misconceptions about conda: <a href="https://jakevdp.github.io/blog/2016/08/25/conda-myths-and-misconceptions/">https://jakevdp.github.io/blog/2016/08/25/conda-myths-and-misconceptions/</a><br />
The post is long, but it's an easy read. Jake even points out how you can install conda inside of a standard python install (<span style="font-family: "courier new" , "courier" , monospace;">pip install conda</span>) and then create conda environments there. So you don't even need to install either the anaconda or miniconda distributions yourself.<br />
<br />
I would be really happy if it turns out that I'm wrong about this and it is, in fact, possible to get things set up so that the RDKit can be made "pip installable" and distributed through PyPi. But I've spent a fair amount of time on this and have not found anything that seems stable and supportable over the long term on all three supported operating systems (note that the "easy" solution of bundling all the RDKit and boost binaries into one giant wheel and just distributing that is neither stable nor supportable).<br />
<h2>
Answers to some hypothetical questions:</h2>
<h3>
Can't you just statically link the RDKit code in the extension modules?</h3>
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Theoretically, but it would increase the size of the packages pretty dramatically (the statically linked Windows conda packages are about twice as big as those for Linux or the Mac). Plus we still have to worry about the Boost dependencies</div>
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Can't you just statically link the Boost libraries?</h3>
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All of the libraries except Boost::Python could be statically linked. But Boost::Python needs to be dynamically linked if you want to share types across extension modules. And we definitely want to be able to do that. <a href="https://www.boost.org/doc/libs/1_71_0/libs/python/doc/html/building/choosing_a_boost_python_library_.html">Here's a reference for this</a>.</div>
<h3>
Can't you just switch to using one extension module?</h3>
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Theoretically, but that module would be huge and, I assume, take a very long time to load. This is a price that you would pay <i>every time </i>you do <span style="font-family: "courier new" , "courier" , monospace;">import rdkit</span> for the first time in a python interpreter. It's also horrible. And we'd have to rewrite a bunch of wrapper code.</div>
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How about just not using Boost?</h3>
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I'm pretty conservative about adding mandatory external dependencies to the RDKit, but when it comes to solving difficult problems like doing good C++-Python wrappers, I'm a huge fan of using high-quality libraries that other people write and support. </div>
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There's some chance that we could use <a href="https://github.com/pybind/pybind11">pybind11</a> as a replacement for Boost::Python - the project looks <i>great</i> and seems quite active - but even doing a test to see whether or not that's possible is a seriously non-trivial amount of work, so it's hard to even find the time to get started with that. <sigh> it's hard.</sigh><br />
<br /></div>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com0tag:blogger.com,1999:blog-684443317148892945.post-70435288833153904582019-11-09T20:35:00.001-08:002019-11-09T20:35:13.580-08:00Additive Fingerprints<div class="cell border-box-sizing text_cell rendered">
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The goal here is to evaluate one strategy for enabling similarity search of virtual libraries defined via molecular fragments that can be combined using simple rules. One obvious approach is to just enumerate the molecules in the library, generate their fingerprints, and the calculate similarity. This is clearly impractical if the libraries are really big, so I want to see if it's possible to make things faster by skipping the enumeration step and approximating the fingerprint of a molecule with the sum of the fingerprints that make it up plus constant correction terms.<br />
For example, suppose I form a molecule by combining the two fragments <code>c1ccccc1-[16*]</code> and <code>CCCCO-[3*]</code> to make <code>c1ccccc1-OCCCC</code>, I want to see how closely I can approximate the fingerprint of the final molecule by adding the fingerprints of the two fragments together with a correction term that is characteristic of the bond type being formed (in this case the <code>16-3</code> combination rule). This obviously won't work well with fingerprints like Atom Pairs that include terms encompassing the entire molecule, but may be useful for more "local" descriptors like the Morgan fingerprint, topological torsions, or the RDKit fingerprint.<br />
This isn't a really new idea - it's inspired by the idea behind <a href="http://pubs.acs.org/doi/abs/10.1021/ci0504871">Truchon and Bayly's GLARE algorithm</a> and I've explored it a bit in the past - but I haven't seen it published and never really dug into it. That's what this post (likely a series of posts) is for.<br />
Demonstrating that this really works requires a fair amount of effort, but we can get a good initial readout by comparing the calculated similarities between a bunch of real molecules calculated with their "real" fingerprints and then with the "additive" fingerprints. I'll start here with the Morgan fingerprint, since that's one of the more popular ones in the RDKit (and one where the approach seems likely to work). If that goes well, I'll try another fingerprint type or two and maybe do some more comprehensive validation.</div>
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<pre><span class="kn">from</span> <span class="nn">rdkit</span> <span class="k">import</span> <span class="n">DataStructs</span>
<span class="kn">from</span> <span class="nn">rdkit</span> <span class="k">import</span> <span class="n">Chem</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">rdFingerprintGenerator</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="k">import</span> <span class="n">IPythonConsole</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.MolStandardize</span> <span class="k">import</span> <span class="n">rdMolStandardize</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">Draw</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">BRICS</span>
<span class="kn">import</span> <span class="nn">rdkit</span>
<span class="n">rdkit</span><span class="o">.</span><span class="n">__version__</span>
<span class="o">%</span><span class="k">pylab</span> inline
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<pre>Populating the interactive namespace from numpy and matplotlib
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<pre>RDKit WARNING: [17:03:45] Enabling RDKit 2019.09.1 jupyter extensions
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Start by reading in the set of molecules we'll work with. This dataset was described in a recent <a href="https://rdkit.blogspot.com/2019/10/a-new-lessel-and-briem-like-dataset.html">RDKit blog post</a>.<br />
Bring in the molecules and do salt stripping:</div>
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<pre><span class="kn">from</span> <span class="nn">rdkit</span> <span class="k">import</span> <span class="n">rdBase</span>
<span class="n">rdBase</span><span class="o">.</span><span class="n">DisableLog</span><span class="p">(</span><span class="s1">'rdApp.info'</span><span class="p">)</span>
<span class="n">ms</span> <span class="o">=</span> <span class="p">[</span><span class="n">rdMolStandardize</span><span class="o">.</span><span class="n">FragmentParent</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">Chem</span><span class="o">.</span><span class="n">SmilesMolSupplier</span><span class="p">(</span><span class="s1">'../data/BLSets_selected_actives.txt'</span><span class="p">)]</span>
<span class="nb">len</span><span class="p">(</span><span class="n">ms</span><span class="p">)</span>
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<pre>6359</pre>
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In order to fragment the molecules we're going to be using the <a href="http://doi.wiley.com/10.1002/cmdc.200800178">BRICS fragmentation rules</a> as implemented in the RDKit. Note that the RDKit implementation has differs from the published BRICS rules in that atom types 2 and 5 (both Ns) have been combined with each other. This is, unfortunately not (yet) documented outside of the code itself, but there's an explanation here: <a href="https://github.com/rdkit/rdkit/blob/master/Code/GraphMol/ChemTransforms/MolFragmenter.cpp#L101">https://github.com/rdkit/rdkit/blob/master/Code/GraphMol/ChemTransforms/MolFragmenter.cpp#L101</a><br />
For the purposes of this evaluation as to whether or not additive fingerprints work, we will only be using the ten most common BRICS bonds in the dataset. Let's start by finding those.<br />
This is what the list of BRICS bonds for each molecule looks like:</div>
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<pre><span class="nb">list</span><span class="p">(</span><span class="n">BRICS</span><span class="o">.</span><span class="n">FindBRICSBonds</span><span class="p">(</span><span class="n">ms</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
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<pre>[((2, 1), ('1', '5')),
((8, 7), ('1', '5')),
((6, 7), ('4', '5')),
((15, 20), ('5', '12')),
((8, 10), ('6', '13')),
((6, 30), ('8', '15')),
((23, 24), ('8', '16'))]</pre>
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The first element of each tuple contains the indices of the atoms defining the bond, the second element has the BRICS atom types of those atoms. For example, the first BRICS bond in the molecule above is between atom 2 (BRICS type 1) and atom 1 (BRICS type 5).<br />
Loop over all the molecules and count the number of times each bond type occurs:</div>
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<pre><span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">defaultdict</span><span class="p">,</span> <span class="n">Counter</span>
<span class="n">cntr</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">()</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">:</span>
<span class="n">bbnds</span> <span class="o">=</span> <span class="n">BRICS</span><span class="o">.</span><span class="n">FindBRICSBonds</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="k">for</span> <span class="n">aids</span><span class="p">,</span><span class="n">lbls</span> <span class="ow">in</span> <span class="n">bbnds</span><span class="p">:</span>
<span class="n">cntr</span><span class="p">[</span><span class="n">lbls</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">freqs</span> <span class="o">=</span> <span class="nb">sorted</span><span class="p">([(</span><span class="n">y</span><span class="p">,</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">cntr</span><span class="o">.</span><span class="n">items</span><span class="p">()],</span><span class="n">reverse</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">freqs</span><span class="p">[:</span><span class="mi">10</span><span class="p">]</span>
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<pre>[(7337, ('4', '5')),
(4619, ('1', '5')),
(3488, ('8', '16')),
(2599, ('3', '16')),
(1817, ('5', '16')),
(1507, ('3', '4')),
(997, ('6', '16')),
(853, ('5', '15')),
(790, ('8', '14')),
(714, ('8', '15'))]</pre>
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The top two bond types are <code>4-5</code>(alkyl C - amine) and <code>1-5</code> (the amide bond).<br />
Keep those top 10:</div>
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<pre><span class="n">bondsToKeep</span> <span class="o">=</span> <span class="p">[</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">freqs</span><span class="p">[:</span><span class="mi">10</span><span class="p">]]</span>
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In order to clarify what the more complex code below does, here's a worked example demonstrating fragmenting a single molecule along two bonds, both between BRICS atom types 1 and 5 (these bonds and the types come from the example above where we found the molecule's BRICS bonds):</div>
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<pre><span class="n">tm</span><span class="o">=</span><span class="n">Chem</span><span class="o">.</span><span class="n">FragmentOnSomeBonds</span><span class="p">(</span><span class="n">ms</span><span class="p">[</span><span class="mi">0</span><span class="p">],[</span><span class="n">ms</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">GetBondBetweenAtoms</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">GetIdx</span><span class="p">(),</span><span class="n">ms</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">GetBondBetweenAtoms</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">7</span><span class="p">)</span><span class="o">.</span><span class="n">GetIdx</span><span class="p">()],</span>
<span class="n">dummyLabels</span><span class="o">=</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">5</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">5</span><span class="p">]])</span>
<span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolToSmiles</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">tm</span><span class="p">]</span>
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<pre>['[1*]C(=O)C(=O)C(NC(=O)[C@@H]1CC[C@H]2CN(S(=O)(=O)Cc3ccccc3)CC(=O)N21)[C@H]1CC[C@H](N)CC1.[5*]NC',
'[1*]C(=O)[C@@H]1CC[C@H]2CN(S(=O)(=O)Cc3ccccc3)CC(=O)N21.[5*]NC(C(=O)C(=O)NC)[C@H]1CC[C@H](N)CC1']</pre>
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To make it visual, here's a drawing of the two fragmentations:</div>
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<pre><span class="n">fms</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">FragmentOnSomeBonds</span><span class="p">(</span><span class="n">ms</span><span class="p">[</span><span class="mi">0</span><span class="p">],[</span><span class="n">ms</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">GetBondBetweenAtoms</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span><span class="mi">1</span><span class="p">)</span><span class="o">.</span><span class="n">GetIdx</span><span class="p">(),</span><span class="n">ms</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">GetBondBetweenAtoms</span><span class="p">(</span><span class="mi">8</span><span class="p">,</span><span class="mi">7</span><span class="p">)</span><span class="o">.</span><span class="n">GetIdx</span><span class="p">()],</span>
<span class="n">dummyLabels</span><span class="o">=</span><span class="p">[[</span><span class="mi">1</span><span class="p">,</span><span class="mi">5</span><span class="p">],[</span><span class="mi">1</span><span class="p">,</span><span class="mi">5</span><span class="p">]])</span>
<span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">(</span><span class="n">fms</span><span class="p">,</span><span class="n">subImgSize</span><span class="o">=</span><span class="p">(</span><span class="mi">300</span><span class="p">,</span><span class="mi">250</span><span class="p">))</span>
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Out[7]:</div>
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<img 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Let's define some functions that we'll use for the rest of this:</div>
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In [8]:</div>
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<pre><span class="k">def</span> <span class="nf">splitMol</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">bondsToKeep</span><span class="p">):</span>
<span class="sd">''' fragments a molecule on a particular set of BRICS bonds. </span>
<span class="sd"> Partially sanitizes the results </span>
<span class="sd"> '''</span>
<span class="n">bbnds</span> <span class="o">=</span> <span class="n">BRICS</span><span class="o">.</span><span class="n">FindBRICSBonds</span><span class="p">(</span><span class="n">mol</span><span class="p">)</span>
<span class="n">bndsToTry</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">lbls</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">aids</span><span class="p">,</span><span class="n">lbl</span> <span class="ow">in</span> <span class="n">bbnds</span><span class="p">:</span>
<span class="k">if</span> <span class="n">lbl</span> <span class="ow">in</span> <span class="n">bondsToKeep</span><span class="p">:</span>
<span class="n">bndsToTry</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mol</span><span class="o">.</span><span class="n">GetBondBetweenAtoms</span><span class="p">(</span><span class="n">aids</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">aids</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">GetIdx</span><span class="p">())</span>
<span class="n">lbls</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">lbl</span><span class="p">])</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">bndsToTry</span><span class="p">:</span>
<span class="k">return</span> <span class="p">[]</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">FragmentOnSomeBonds</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">bndsToTry</span><span class="p">,</span><span class="n">dummyLabels</span><span class="o">=</span><span class="n">lbls</span><span class="p">)</span>
<span class="c1"># We need at least a partial sanitization for the rest of what we will be doing:</span>
<span class="k">for</span> <span class="n">entry</span> <span class="ow">in</span> <span class="n">res</span><span class="p">:</span>
<span class="n">entry</span><span class="o">.</span><span class="n">UpdatePropertyCache</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="n">Chem</span><span class="o">.</span><span class="n">FastFindRings</span><span class="p">(</span><span class="n">entry</span><span class="p">)</span>
<span class="k">return</span> <span class="n">res</span>
<span class="k">def</span> <span class="nf">getDifferenceFPs</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">bondsToKeep</span><span class="p">,</span><span class="n">fpgen</span><span class="p">):</span>
<span class="sd">''' generates the difference fingerprint between the molecule</span>
<span class="sd"> and each of its fragmentations based on the BRICS bond types passed in.</span>
<span class="sd"> </span>
<span class="sd"> This calculates the sum of the fragment fingerprints by just generating the</span>
<span class="sd"> fingerprint of the fragmented molecule, so whole-molecule fingerprints like</span>
<span class="sd"> atom pairs will not work here. That's fine since they are unlikely to be useful</span>
<span class="sd"> with this approach anyway</span>
<span class="sd"> '''</span>
<span class="n">frags</span> <span class="o">=</span> <span class="n">splitMol</span><span class="p">(</span><span class="n">mol</span><span class="p">,</span><span class="n">bondsToKeep</span><span class="p">)</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">frags</span><span class="p">:</span>
<span class="k">return</span> <span class="p">[]</span>
<span class="n">res</span> <span class="o">=</span> <span class="p">[]</span>
<span class="c1"># get the fingerprint for the molecule:</span>
<span class="n">molfp</span> <span class="o">=</span> <span class="n">fpgen</span><span class="o">.</span><span class="n">GetCountFingerprint</span><span class="p">(</span><span class="n">mol</span><span class="p">)</span>
<span class="c1"># now loop over each fragmentation</span>
<span class="k">for</span> <span class="n">frag</span> <span class="ow">in</span> <span class="n">frags</span><span class="p">:</span>
<span class="c1"># generate the fingerprint of the fragmented molecule</span>
<span class="c1"># (equivalent to the sum of the fingerprints of the fragments)</span>
<span class="n">ffp</span> <span class="o">=</span> <span class="n">fpgen</span><span class="o">.</span><span class="n">GetCountFingerprint</span><span class="p">(</span><span class="n">frag</span><span class="p">)</span>
<span class="c1"># and keep the difference</span>
<span class="n">res</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">molfp</span><span class="o">-</span><span class="n">ffp</span><span class="p">)</span>
<span class="k">return</span> <span class="n">res</span>
<span class="k">def</span> <span class="nf">getSumFP</span><span class="p">(</span><span class="n">fragMol</span><span class="p">,</span><span class="n">fpgen</span><span class="p">,</span><span class="n">delta</span><span class="p">):</span>
<span class="sd">''' Constructs the sum fingerprint for a fragmented molecule using</span>
<span class="sd"> delta as a constant offset</span>
<span class="sd"> </span>
<span class="sd"> note that any elements of the fingerprint that are negative after adding the</span>
<span class="sd"> constant offset will be set to zero</span>
<span class="sd"> </span>
<span class="sd"> '''</span>
<span class="n">ffp</span> <span class="o">=</span> <span class="n">fpgen</span><span class="o">.</span><span class="n">GetCountFingerprint</span><span class="p">(</span><span class="n">fragMol</span><span class="p">)</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="n">delta</span><span class="o">.</span><span class="n">GetNonzeroElements</span><span class="p">()</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">tv</span> <span class="o">=</span> <span class="n">ffp</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">+</span> <span class="n">v</span>
<span class="k">if</span> <span class="n">tv</span><span class="o"><</span><span class="mi">0</span><span class="p">:</span>
<span class="n">tv</span> <span class="o">=</span> <span class="mi">0</span><span class="p">;</span>
<span class="n">ffp</span><span class="p">[</span><span class="n">idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">tv</span>
<span class="k">return</span> <span class="n">ffp</span>
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<pre><span class="c1"># demonstrate what splitMol returns:</span>
<span class="n">frags</span> <span class="o">=</span> <span class="n">splitMol</span><span class="p">(</span><span class="n">ms</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">bondsToKeep</span><span class="p">[</span><span class="mi">2</span><span class="p">:</span><span class="mi">3</span><span class="p">])</span>
<span class="n">Draw</span><span class="o">.</span><span class="n">MolsToGridImage</span><span class="p">(</span><span class="n">frags</span><span class="p">)</span>
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Now walk through the analysis with a single bond type: <code>1-5</code><br />
The first thing we need to do is determine the constant offset term for this bond type. We will do that by generating difference fingerprints for all the fragmentations it generates and then averaging those difference fingerprints.<br />
Start by generating the difference fingerprints for all the fragmentations using that bond type:</div>
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<pre><span class="c1"># for this analysis we will use a 4096 "bit" count-based Morgan fingerprints with a radius of 2</span>
<span class="n">fpgen</span> <span class="o">=</span> <span class="n">rdFingerprintGenerator</span><span class="o">.</span><span class="n">GetMorganGenerator</span><span class="p">(</span><span class="n">radius</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span><span class="n">fpSize</span><span class="o">=</span><span class="mi">4096</span><span class="p">)</span>
<span class="n">dfps</span><span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">:</span>
<span class="n">dfps</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">getDifferenceFPs</span><span class="p">(</span><span class="n">m</span><span class="p">,</span><span class="n">bondsToKeep</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">2</span><span class="p">],</span><span class="n">fpgen</span><span class="p">))</span>
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Now find the average value of the difference fingerprint, being careful to keep only integer values:</div>
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<pre><span class="n">delta1</span> <span class="o">=</span> <span class="n">dfps</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">for</span> <span class="n">dfp</span> <span class="ow">in</span> <span class="n">dfps</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
<span class="n">delta1</span> <span class="o">+=</span> <span class="n">dfp</span>
<span class="n">nfps</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dfps</span><span class="p">)</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="n">delta1</span><span class="o">.</span><span class="n">GetNonzeroElements</span><span class="p">()</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">delta1</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">v</span><span class="o">/</span><span class="n">nfps</span><span class="p">)</span>
<span class="n">delta1</span><span class="o">.</span><span class="n">GetNonzeroElements</span><span class="p">()</span>
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<pre>{226: -2, 264: -1, 1831: -1}</pre>
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To quickly summarize that this is telling us: We can simulate the MFP2 fingerprint for a molecule by adding the MFP2 fingerprints of its bond type <code>1-5</code> fragments and then substracting one each from the values for bits 264 and 1831 and two from the value for bit 226.<br />
Let's look at how well that works across all the molecules. We start by generating the full FP and the <code>1-5</code> fragment FP for every molecule that has a <code>1-5</code> fragment:</div>
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<pre><span class="n">allfps</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">:</span>
<span class="n">frags</span> <span class="o">=</span> <span class="n">splitMol</span><span class="p">(</span><span class="n">m</span><span class="p">,</span><span class="n">bondsToKeep</span><span class="p">[</span><span class="mi">1</span><span class="p">:</span><span class="mi">2</span><span class="p">])</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">frags</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">fp1</span> <span class="o">=</span> <span class="n">fpgen</span><span class="o">.</span><span class="n">GetCountFingerprint</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">ffps</span> <span class="o">=</span> <span class="p">[</span><span class="n">getSumFP</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">fpgen</span><span class="p">,</span><span class="n">delta1</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">frags</span><span class="p">]</span>
<span class="n">allfps</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">fp1</span><span class="p">,</span><span class="n">ffps</span><span class="p">))</span>
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<pre><span class="nb">len</span><span class="p">(</span><span class="n">allfps</span><span class="p">)</span>
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<pre>2801</pre>
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And calculate the similarity of each molecule to itself by calculating the Tanimoto similarity between the full fingerprint and the fragment fingerprint:</div>
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<pre><span class="n">self_tanis</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">fp1</span><span class="p">,</span><span class="n">ffps</span> <span class="ow">in</span> <span class="n">allfps</span><span class="p">:</span>
<span class="n">self_tanis</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">DataStructs</span><span class="o">.</span><span class="n">TanimotoSimilarity</span><span class="p">(</span><span class="n">fp1</span><span class="p">,</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ffps</span><span class="p">])</span>
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Visualize those:</div>
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<pre><span class="n">hist</span><span class="p">(</span><span class="n">self_tanis</span><span class="p">,</span><span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">);</span>
<span class="n">title</span><span class="p">(</span><span class="s2">"self similarity, fusion 1-5"</span><span class="p">);</span>
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src="data:image/png;base64,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Though that's interesting, and good to see that the self-similarities aren't all too far from 1.0, we're actually more interested in using the fingerprints for comparing different molecules to each other.<br />
Look at a bunch of random pairs of molecules that are at least 0.3 similar to each other (the <a href="http://rdkit.blogspot.com/2013/10/fingerprint-thresholds.html">noise threshold</a> for count-based MFP2 is around 0.3), and then look at how different the similarities between their normal and fragment fingerprints are.</div>
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<pre><span class="kn">import</span> <span class="nn">random</span>
<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mh">0xf00d</span><span class="p">)</span>
<span class="n">ps1</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">allfps</span><span class="p">)))</span>
<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">ps1</span><span class="p">)</span>
<span class="n">ps1</span> <span class="o">=</span> <span class="n">ps1</span><span class="p">[:</span><span class="mi">500</span><span class="p">]</span>
<span class="n">ps2</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">allfps</span><span class="p">)))</span>
<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">ps2</span><span class="p">)</span>
<span class="n">ps2</span> <span class="o">=</span> <span class="n">ps2</span><span class="p">[:</span><span class="mi">500</span><span class="p">]</span>
<span class="n">nCompared</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">other_tanidiff</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ps1</span><span class="p">:</span>
<span class="n">fp1</span><span class="p">,</span><span class="n">frags1</span> <span class="o">=</span> <span class="n">allfps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">ps2</span><span class="p">:</span>
<span class="n">fp2</span><span class="p">,</span><span class="n">frags2</span> <span class="o">=</span> <span class="n">allfps</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
<span class="n">refTani</span> <span class="o">=</span> <span class="n">DataStructs</span><span class="o">.</span><span class="n">TanimotoSimilarity</span><span class="p">(</span><span class="n">fp1</span><span class="p">,</span><span class="n">fp2</span><span class="p">)</span>
<span class="k">if</span> <span class="n">refTani</span> <span class="o"><</span> <span class="mf">0.3</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">nCompared</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">ltanis</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">f1</span> <span class="ow">in</span> <span class="n">frags1</span><span class="p">:</span>
<span class="n">ltanis</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">DataStructs</span><span class="o">.</span><span class="n">BulkTanimotoSimilarity</span><span class="p">(</span><span class="n">f1</span><span class="p">,</span><span class="n">frags2</span><span class="p">))</span>
<span class="n">other_tanidiff</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">refTani</span><span class="o">-</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ltanis</span><span class="p">])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">"considered </span><span class="si">{nCompared}</span><span class="s2"> fingerprint pairs from {len(allfps)**2}"</span><span class="p">)</span>
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<pre>considered 15382 fingerprint pairs from 7845601
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<pre><span class="n">hist</span><span class="p">(</span><span class="n">other_tanidiff</span><span class="p">,</span><span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">);</span>
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<pre><span class="nb">len</span><span class="p">(</span><span class="n">other_tanidiff</span><span class="p">)</span>
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<pre>62781</pre>
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That's looks pretty good. For at least the <code>1-5</code> fragmentation, the similarity calculated with the fragment fingerprint is quite similar t othat calculated with the full fingerprint. Even better: most of the deviation happens to the high-similarity side, so we should be able to safely use the fragment-fingerprint similarity as a filter to identify interesting molecules.</div>
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<h1 id="Validation">
Validation<a class="anchor-link" href="https://www.blogger.com/blogger.g?blogID=684443317148892945#Validation">¶</a></h1>
Repeat that analysis for each of the top 10 fragmentations:</div>
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<pre><span class="kn">import</span> <span class="nn">random</span>
<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mh">0xf00d</span><span class="p">)</span>
<span class="n">results</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">btk</span> <span class="ow">in</span> <span class="n">bondsToKeep</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">"Doing </span><span class="si">{btk}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">fpgen</span> <span class="o">=</span> <span class="n">rdFingerprintGenerator</span><span class="o">.</span><span class="n">GetMorganGenerator</span><span class="p">(</span><span class="n">radius</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span><span class="n">fpSize</span><span class="o">=</span><span class="mi">4096</span><span class="p">)</span>
<span class="n">dfps</span><span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">:</span>
<span class="n">dfps</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">getDifferenceFPs</span><span class="p">(</span><span class="n">m</span><span class="p">,[</span><span class="n">btk</span><span class="p">],</span><span class="n">fpgen</span><span class="p">))</span>
<span class="n">delta1</span> <span class="o">=</span> <span class="n">dfps</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
<span class="k">for</span> <span class="n">dfp</span> <span class="ow">in</span> <span class="n">dfps</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
<span class="n">delta1</span> <span class="o">+=</span> <span class="n">dfp</span>
<span class="n">nfps</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">dfps</span><span class="p">)</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="n">delta1</span><span class="o">.</span><span class="n">GetNonzeroElements</span><span class="p">()</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="n">delta1</span><span class="p">[</span><span class="n">k</span><span class="p">]</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">v</span><span class="o">/</span><span class="n">nfps</span><span class="p">)</span>
<span class="n">allfps</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">:</span>
<span class="n">frags</span> <span class="o">=</span> <span class="n">splitMol</span><span class="p">(</span><span class="n">m</span><span class="p">,[</span><span class="n">btk</span><span class="p">])</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">frags</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">fp1</span> <span class="o">=</span> <span class="n">fpgen</span><span class="o">.</span><span class="n">GetCountFingerprint</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">ffps</span> <span class="o">=</span> <span class="p">[</span><span class="n">getSumFP</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">fpgen</span><span class="p">,</span><span class="n">delta1</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">frags</span><span class="p">]</span>
<span class="n">allfps</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">fp1</span><span class="p">,</span><span class="n">ffps</span><span class="p">))</span>
<span class="n">self_tanis</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">fp1</span><span class="p">,</span><span class="n">ffps</span> <span class="ow">in</span> <span class="n">allfps</span><span class="p">:</span>
<span class="n">self_tanis</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">DataStructs</span><span class="o">.</span><span class="n">TanimotoSimilarity</span><span class="p">(</span><span class="n">fp1</span><span class="p">,</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ffps</span><span class="p">])</span>
<span class="n">ps1</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">allfps</span><span class="p">)))</span>
<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">ps1</span><span class="p">)</span>
<span class="n">ps1</span> <span class="o">=</span> <span class="n">ps1</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]</span>
<span class="n">ps2</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">allfps</span><span class="p">)))</span>
<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">ps2</span><span class="p">)</span>
<span class="n">ps2</span> <span class="o">=</span> <span class="n">ps2</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]</span>
<span class="n">nCompared</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">other_tanidiff1</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">other_tanidiff2</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ps1</span><span class="p">:</span>
<span class="n">fp1</span><span class="p">,</span><span class="n">frags1</span> <span class="o">=</span> <span class="n">allfps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">ps2</span><span class="p">:</span>
<span class="n">fp2</span><span class="p">,</span><span class="n">frags2</span> <span class="o">=</span> <span class="n">allfps</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
<span class="n">refTani</span> <span class="o">=</span> <span class="n">DataStructs</span><span class="o">.</span><span class="n">TanimotoSimilarity</span><span class="p">(</span><span class="n">fp1</span><span class="p">,</span><span class="n">fp2</span><span class="p">)</span>
<span class="k">if</span> <span class="n">refTani</span> <span class="o"><</span> <span class="mf">0.3</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">nCompared</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">ltanis</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">f1</span> <span class="ow">in</span> <span class="n">frags1</span><span class="p">:</span>
<span class="n">ltanis</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">DataStructs</span><span class="o">.</span><span class="n">BulkTanimotoSimilarity</span><span class="p">(</span><span class="n">f1</span><span class="p">,</span><span class="n">frags2</span><span class="p">))</span>
<span class="n">other_tanidiff1</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">refTani</span><span class="o">-</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ltanis</span><span class="p">])</span>
<span class="n">other_tanidiff2</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">refTani</span><span class="o">-</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">DataStructs</span><span class="o">.</span><span class="n">BulkTanimotoSimilarity</span><span class="p">(</span><span class="n">fp1</span><span class="p">,</span><span class="n">frags2</span><span class="p">)])</span>
<span class="n">other_tanidiff2</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">refTani</span><span class="o">-</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">DataStructs</span><span class="o">.</span><span class="n">BulkTanimotoSimilarity</span><span class="p">(</span><span class="n">fp2</span><span class="p">,</span><span class="n">frags1</span><span class="p">)])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">"considered </span><span class="si">{nCompared}</span><span class="s2"> fingerprint pairs from {len(allfps)**2}"</span><span class="p">)</span>
<span class="n">results</span><span class="p">[</span><span class="n">btk</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">self_tanis</span><span class="p">,</span><span class="n">other_tanidiff1</span><span class="p">,</span><span class="n">other_tanidiff2</span><span class="p">)</span>
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<pre>Doing ('4', '5')
considered 66995 fingerprint pairs from 18326961
Doing ('1', '5')
considered 63242 fingerprint pairs from 7845601
Doing ('8', '16')
considered 54070 fingerprint pairs from 6095961
Doing ('3', '16')
considered 95650 fingerprint pairs from 4260096
Doing ('5', '16')
considered 217114 fingerprint pairs from 2725801
Doing ('3', '4')
considered 112805 fingerprint pairs from 1537600
Doing ('6', '16')
considered 98275 fingerprint pairs from 790321
Doing ('5', '15')
considered 53490 fingerprint pairs from 640000
Doing ('8', '14')
considered 43370 fingerprint pairs from 504100
Doing ('8', '15')
considered 35148 fingerprint pairs from 352836
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Start, again, by looking at self similarities.</div>
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In [20]:</div>
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<pre><span class="n">figsize</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">18</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">btk</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">self_tanis</span><span class="p">,</span><span class="n">other_tanidiff1</span><span class="p">,</span><span class="n">other_tanidiff2</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">btk</span><span class="p">]</span>
<span class="n">hist</span><span class="p">(</span><span class="n">self_tanis</span><span class="p">,</span><span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">);</span>
<span class="n">title</span><span class="p">(</span><span class="n">f</span><span class="s2">"self similarity, fusion </span><span class="si">{btk}</span><span class="s2">"</span><span class="p">);</span>
<span class="n">xlim</span><span class="p">((</span><span class="mf">0.6</span><span class="p">,</span><span class="mf">1.0</span><span class="p">))</span>
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The differences here aren't huge, but we definitely aren't getting self-similarities of 1.</div>
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Still, we aren't really interested in self-similarity. How does the method do when we compute fragment-fragment similarities? This time we plot the difference between the similarities calculated with the full fingerprint and the fragment fingerprint.</div>
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In [61]:</div>
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<pre><span class="n">figsize</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">18</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">btk</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">self_tanis</span><span class="p">,</span><span class="n">other_tanidiff1</span><span class="p">,</span><span class="n">other_tanidiff2</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">btk</span><span class="p">]</span>
<span class="n">hist</span><span class="p">(</span><span class="n">other_tanidiff1</span><span class="p">,</span><span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">);</span>
<span class="n">title</span><span class="p">(</span><span class="n">f</span><span class="s2">"fragfp-fragfp comparison, fusion </span><span class="si">{btk}</span><span class="s2">"</span><span class="p">);</span>
<span class="c1">#yscale('log')</span>
<span class="n">xlim</span><span class="p">((</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.25</span><span class="p">));</span>
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Those look good! In general the difference in similarities is within 0.05.</div>
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And, finally, since we could also query the collection of fragment molecules with a full fingerprint, look at the difference between the full FP similarity and the full FP - fragment FP similarity:</div>
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In [62]:</div>
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<pre><span class="n">figsize</span><span class="p">(</span><span class="mi">12</span><span class="p">,</span><span class="mi">18</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">btk</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">results</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">self_tanis</span><span class="p">,</span><span class="n">other_tanidiff1</span><span class="p">,</span><span class="n">other_tanidiff2</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">btk</span><span class="p">]</span>
<span class="n">hist</span><span class="p">(</span><span class="n">other_tanidiff2</span><span class="p">,</span><span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">);</span>
<span class="n">title</span><span class="p">(</span><span class="n">f</span><span class="s2">"fp-fragfp comparison, fusion </span><span class="si">{btk}</span><span class="s2">"</span><span class="p">);</span>
<span class="c1">#yscale('log')</span>
<span class="n">xlim</span><span class="p">((</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.3</span><span class="p">));</span>
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Here the differences are larger than when we search with the fragment fps, but they still aren't huge. This form of the query also has the characteristic that the similarity calculated against the fragment fps is almost always lower than that calculated against the full fp.</div>
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<h1 id="What-about-if-we-don't-use-the-constant-offset?">
What about if we don't use the constant offset?<a class="anchor-link" href="https://www.blogger.com/blogger.g?blogID=684443317148892945#What-about-if-we-don't-use-the-constant-offset?">¶</a></h1>
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I went into this convinced that the constant offset was important. What if we just use the simple additive fingerprints without including that offset?</div>
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In [24]:</div>
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<pre><span class="kn">import</span> <span class="nn">random</span>
<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mh">0xf00d</span><span class="p">)</span>
<span class="n">noconstant_results</span> <span class="o">=</span> <span class="p">{}</span>
<span class="k">for</span> <span class="n">btk</span> <span class="ow">in</span> <span class="n">bondsToKeep</span><span class="p">:</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">"Doing </span><span class="si">{btk}</span><span class="s2">"</span><span class="p">)</span>
<span class="n">fpgen</span> <span class="o">=</span> <span class="n">rdFingerprintGenerator</span><span class="o">.</span><span class="n">GetMorganGenerator</span><span class="p">(</span><span class="n">radius</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span><span class="n">fpSize</span><span class="o">=</span><span class="mi">4096</span><span class="p">)</span>
<span class="n">dfps</span><span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">:</span>
<span class="n">dfps</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">getDifferenceFPs</span><span class="p">(</span><span class="n">m</span><span class="p">,[</span><span class="n">btk</span><span class="p">],</span><span class="n">fpgen</span><span class="p">))</span>
<span class="n">allfps</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">ms</span><span class="p">:</span>
<span class="n">frags</span> <span class="o">=</span> <span class="n">splitMol</span><span class="p">(</span><span class="n">m</span><span class="p">,[</span><span class="n">btk</span><span class="p">])</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">frags</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">fp1</span> <span class="o">=</span> <span class="n">fpgen</span><span class="o">.</span><span class="n">GetCountFingerprint</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">ffps</span> <span class="o">=</span> <span class="p">[</span><span class="n">fpgen</span><span class="o">.</span><span class="n">GetCountFingerprint</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">frags</span><span class="p">]</span>
<span class="n">allfps</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">fp1</span><span class="p">,</span><span class="n">ffps</span><span class="p">))</span>
<span class="n">self_tanis</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">fp1</span><span class="p">,</span><span class="n">ffps</span> <span class="ow">in</span> <span class="n">allfps</span><span class="p">:</span>
<span class="n">self_tanis</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">DataStructs</span><span class="o">.</span><span class="n">TanimotoSimilarity</span><span class="p">(</span><span class="n">fp1</span><span class="p">,</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ffps</span><span class="p">])</span>
<span class="n">ps1</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">allfps</span><span class="p">)))</span>
<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">ps1</span><span class="p">)</span>
<span class="n">ps1</span> <span class="o">=</span> <span class="n">ps1</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]</span>
<span class="n">ps2</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">allfps</span><span class="p">)))</span>
<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">ps2</span><span class="p">)</span>
<span class="n">ps2</span> <span class="o">=</span> <span class="n">ps2</span><span class="p">[:</span><span class="mi">1000</span><span class="p">]</span>
<span class="n">nCompared</span> <span class="o">=</span> <span class="mi">0</span>
<span class="n">other_tanidiff1</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">other_tanidiff2</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">ps1</span><span class="p">:</span>
<span class="n">fp1</span><span class="p">,</span><span class="n">frags1</span> <span class="o">=</span> <span class="n">allfps</span><span class="p">[</span><span class="n">i</span><span class="p">]</span>
<span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">ps2</span><span class="p">:</span>
<span class="n">fp2</span><span class="p">,</span><span class="n">frags2</span> <span class="o">=</span> <span class="n">allfps</span><span class="p">[</span><span class="n">j</span><span class="p">]</span>
<span class="n">refTani</span> <span class="o">=</span> <span class="n">DataStructs</span><span class="o">.</span><span class="n">TanimotoSimilarity</span><span class="p">(</span><span class="n">fp1</span><span class="p">,</span><span class="n">fp2</span><span class="p">)</span>
<span class="k">if</span> <span class="n">refTani</span> <span class="o"><</span> <span class="mf">0.3</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">nCompared</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">ltanis</span> <span class="o">=</span> <span class="p">[]</span>
<span class="k">for</span> <span class="n">f1</span> <span class="ow">in</span> <span class="n">frags1</span><span class="p">:</span>
<span class="n">ltanis</span><span class="o">.</span><span class="n">extend</span><span class="p">(</span><span class="n">DataStructs</span><span class="o">.</span><span class="n">BulkTanimotoSimilarity</span><span class="p">(</span><span class="n">f1</span><span class="p">,</span><span class="n">frags2</span><span class="p">))</span>
<span class="n">other_tanidiff1</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">refTani</span><span class="o">-</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">ltanis</span><span class="p">])</span>
<span class="n">other_tanidiff2</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">refTani</span><span class="o">-</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">DataStructs</span><span class="o">.</span><span class="n">BulkTanimotoSimilarity</span><span class="p">(</span><span class="n">fp1</span><span class="p">,</span><span class="n">frags2</span><span class="p">)])</span>
<span class="n">other_tanidiff2</span><span class="o">.</span><span class="n">extend</span><span class="p">([</span><span class="n">refTani</span><span class="o">-</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">DataStructs</span><span class="o">.</span><span class="n">BulkTanimotoSimilarity</span><span class="p">(</span><span class="n">fp2</span><span class="p">,</span><span class="n">frags1</span><span class="p">)])</span>
<span class="nb">print</span><span class="p">(</span><span class="n">f</span><span class="s2">"considered </span><span class="si">{nCompared}</span><span class="s2"> fingerprint pairs from {len(allfps)**2}"</span><span class="p">)</span>
<span class="n">noconstant_results</span><span class="p">[</span><span class="n">btk</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="n">self_tanis</span><span class="p">,</span><span class="n">other_tanidiff1</span><span class="p">,</span><span class="n">other_tanidiff2</span><span class="p">)</span>
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<pre>Doing ('4', '5')
considered 66995 fingerprint pairs from 18326961
Doing ('1', '5')
considered 63242 fingerprint pairs from 7845601
Doing ('8', '16')
considered 54070 fingerprint pairs from 6095961
Doing ('3', '16')
considered 95650 fingerprint pairs from 4260096
Doing ('5', '16')
considered 217114 fingerprint pairs from 2725801
Doing ('3', '4')
considered 112805 fingerprint pairs from 1537600
Doing ('6', '16')
considered 98275 fingerprint pairs from 790321
Doing ('5', '15')
considered 53490 fingerprint pairs from 640000
Doing ('8', '14')
considered 43370 fingerprint pairs from 504100
Doing ('8', '15')
considered 35148 fingerprint pairs from 352836
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In [60]:</div>
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<pre><span class="n">figsize</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span><span class="mi">18</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">btk</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">noconstant_results</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">self_tanis</span><span class="p">,</span><span class="n">other_tanidiff1</span><span class="p">,</span><span class="n">other_tanidiff2</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">btk</span><span class="p">]</span>
<span class="n">self_tanis_2</span><span class="p">,</span><span class="n">other_tanidiff1_2</span><span class="p">,</span><span class="n">other_tanidiff2_2</span> <span class="o">=</span> <span class="n">noconstant_results</span><span class="p">[</span><span class="n">btk</span><span class="p">]</span>
<span class="n">hist</span><span class="p">([</span><span class="n">self_tanis</span><span class="p">,</span><span class="n">self_tanis_2</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="p">[</span><span class="s1">'with constant'</span><span class="p">,</span><span class="s1">'no constant'</span><span class="p">]);</span>
<span class="n">title</span><span class="p">(</span><span class="n">f</span><span class="s2">"self similarity, fusion </span><span class="si">{btk}</span><span class="s2">"</span><span class="p">);</span>
<span class="n">legend</span><span class="p">()</span>
<span class="n">xlim</span><span class="p">((</span><span class="mf">0.6</span><span class="p">,</span><span class="mf">1.0</span><span class="p">))</span>
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Here the red histograms are clearly shifted to the left; the constant is helping.</div>
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In [63]:</div>
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<pre><span class="n">figsize</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span><span class="mi">18</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">btk</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">noconstant_results</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">self_tanis</span><span class="p">,</span><span class="n">other_tanidiff1</span><span class="p">,</span><span class="n">other_tanidiff2</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">btk</span><span class="p">]</span>
<span class="n">self_tanis_2</span><span class="p">,</span><span class="n">other_tanidiff1_2</span><span class="p">,</span><span class="n">other_tanidiff2_2</span> <span class="o">=</span> <span class="n">noconstant_results</span><span class="p">[</span><span class="n">btk</span><span class="p">]</span>
<span class="n">hist</span><span class="p">([</span><span class="n">other_tanidiff1</span><span class="p">,</span><span class="n">other_tanidiff1_2</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="p">[</span><span class="s1">'with constant'</span><span class="p">,</span><span class="s1">'no constant'</span><span class="p">]);</span>
<span class="n">legend</span><span class="p">();</span>
<span class="n">title</span><span class="p">(</span><span class="n">f</span><span class="s2">"fragfp-fragfp comparison, fusion </span><span class="si">{btk}</span><span class="s2">"</span><span class="p">);</span>
<span class="c1">#yscale('log')</span>
<span class="n">xlim</span><span class="p">((</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.25</span><span class="p">));</span>
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Here the differences aren't as pronounced. It looks like the constant term has the consequence of making the similarity a bit lower(the red histograms are shifted a bit to the left). The difference is, in any case, not large</div>
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In [64]:</div>
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<pre><span class="n">figsize</span><span class="p">(</span><span class="mi">16</span><span class="p">,</span><span class="mi">18</span><span class="p">)</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">btk</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">noconstant_results</span><span class="o">.</span><span class="n">keys</span><span class="p">()):</span>
<span class="n">subplot</span><span class="p">(</span><span class="mi">5</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span>
<span class="n">self_tanis</span><span class="p">,</span><span class="n">other_tanidiff1</span><span class="p">,</span><span class="n">other_tanidiff2</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">btk</span><span class="p">]</span>
<span class="n">self_tanis_2</span><span class="p">,</span><span class="n">other_tanidiff1_2</span><span class="p">,</span><span class="n">other_tanidiff2_2</span> <span class="o">=</span> <span class="n">noconstant_results</span><span class="p">[</span><span class="n">btk</span><span class="p">]</span>
<span class="n">hist</span><span class="p">([</span><span class="n">other_tanidiff2</span><span class="p">,</span><span class="n">other_tanidiff2_2</span><span class="p">],</span><span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="p">[</span><span class="s1">'with constant'</span><span class="p">,</span><span class="s1">'no constant'</span><span class="p">]);</span>
<span class="n">legend</span><span class="p">();</span>
<span class="n">title</span><span class="p">(</span><span class="n">f</span><span class="s2">"fp-fragfp comparison, fusion </span><span class="si">{btk}</span><span class="s2">"</span><span class="p">);</span>
<span class="c1">#yscale('log')</span>
<span class="n">xlim</span><span class="p">((</span><span class="o">-</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.3</span><span class="p">));</span>
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Here we again have a clear signal: leaving the constant term out causes a larger similarity difference (the red histograms are shifted to the right).</div>
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I'm going to wrap this one up here. I think the approach clearly works, but there's some more to do in a future blog post or two:<br />
<ul>
<li>look at how well the constants generated on this set work on a completely different set of molecules.</li>
<li>look at fragmenting the molecule multiple times (i.e. combining more than 2 fragments to form a molecule)</li>
<li>look at an asymmetric similarity measure like Tversky. This is what we'd want to use if we were searching a large fragment space and incrementally building up the results molecules (like in the <a href="https://dx.doi.org/10.1023/A:1011144622059">FTrees-FS paper</a>).</li>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com0tag:blogger.com,1999:blog-684443317148892945.post-66460055759477463392019-11-06T00:17:00.001-08:002019-11-06T00:17:26.691-08:00Constructing SAR tables in Jupyter<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt">
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<p>This one was inspired by a thread on the <a href="https://sourceforge.net/projects/rdkit/lists/rdkit-discuss">rdkit-discuss</a> mailing list: <a href="https://www.mail-archive.com/rdkit-discuss@lists.sourceforge.net/msg09068.html">https://www.mail-archive.com/rdkit-discuss@lists.sourceforge.net/msg09068.html</a></p>
<p>An SAR table is often a convenient way to summarize a dataset. These compact views of an SAR dataset have R1 structures in the rows, R2 structures in the columns, and measured values in the cells. I guess you probably know what I'm talking about even if I'm not describing it particularly well; if not, scroll down a bit and you'll see one. :-)</p>
<p>The question on the mailing list was how to build one of these with the RDKit and display it in Jupyter. That's what we'll do in this blog post.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">rdkit</span> <span class="k">import</span> <span class="n">Chem</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">Draw</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem.Draw</span> <span class="k">import</span> <span class="n">IPythonConsole</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">rdRGroupDecomposition</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">rdDepictor</span>
<span class="kn">import</span> <span class="nn">rdkit</span>
<span class="kn">import</span> <span class="nn">pandas</span> <span class="k">as</span> <span class="nn">pd</span>
<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="kn">from</span> <span class="nn">rdkit.Chem</span> <span class="k">import</span> <span class="n">PandasTools</span>
<span class="kn">import</span> <span class="nn">time</span>
<span class="nb">print</span><span class="p">(</span><span class="n">rdkit</span><span class="o">.</span><span class="n">__version__</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">time</span><span class="o">.</span><span class="n">ctime</span><span class="p">())</span>
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<pre>2019.09.1
Wed Nov 6 09:14:28 2019
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<pre>RDKit WARNING: [09:14:28] Enabling RDKit 2019.09.1 jupyter extensions
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<h1 id="Read-in-our-dataset">Read in our dataset<a class="anchor-link" href="#Read-in-our-dataset">¶</a></h1><p>This is data from ChEMBL for a set of compounds with EC50 values measured against S1P1.</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="o">!</span>head ../data/S1P1_data.csv
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<pre>"doc_id","molregno","standard_relation","standard_value","standard_units","standard_flag","standard_type","pchembl_value","canonical_smiles","compound_chembl_id"
5839,189018,"=",82,"nM",1,"EC50",7.09,"CCCCCCCCc1ccc(NC(=O)[C@@H](N)COP(=O)(O)O)cc1.OC(=O)C(F)(F)F","CHEMBL332050"
5839,188442,"=",322.1,"nM",1,"EC50",6.49,"CCCCCCCCCCCCCCONC(=O)[C@@H](N)COP(=O)(O)O.OC(=O)C(F)(F)F","CHEMBL115505"
5839,188375,"=",260,"nM",1,"EC50",6.58,"CCCCCCc1ccc(NC(=O)[C@H](N)COP(=O)(O)O)cc1.OC(=O)C(F)(F)F","CHEMBL115344"
5839,188376,"=",598.4,"nM",1,"EC50",6.22,"CCCCCCCCCCCCNC(=O)[C@@H](N)COP(=O)(O)O.OC(=O)C(F)(F)F","CHEMBL324358"
5839,188766,"=",12.7,"nM",1,"EC50",7.9,"CCCCCCCCCCCCCCNC(=O)[C@H](N)COP(=O)(O)O.OC(=O)C(F)(F)F","CHEMBL332472"
5839,188278,"=",58,"nM",1,"EC50",7.24,"CCCCCCCCc1ccc(NC(=O)[C@H](N)COP(=O)(O)O)cc1.OC(=O)C(F)(F)F","CHEMBL422074"
5839,188644,"=",130,"nM",1,"EC50",6.89,"CCCCCCCCCCCCc1ccc(NC(=O)[C@H](N)COP(=O)(O)O)cc1.OC(=O)C(F)(F)F","CHEMBL116953"
5839,188322,"=",8.6,"nM",1,"EC50",8.07,"CCCCCCCCCCc1ccc(NC(=O)[C@H](N)COP(=O)(O)O)cc1.OC(=O)C(F)(F)F","CHEMBL334038"
5839,188407,"=",1805,"nM",1,"EC50",5.74,"CCCCCCCCCCCCCCCCNC(=O)[C@@H](N)COP(=O)(O)O.OC(=O)C(F)(F)F","CHEMBL325408"
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">df</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">read_csv</span><span class="p">(</span><span class="s1">'../data/S1P1_data.csv'</span><span class="p">)</span>
<span class="n">PandasTools</span><span class="o">.</span><span class="n">AddMoleculeColumnToFrame</span><span class="p">(</span><span class="n">df</span><span class="p">,</span><span class="n">smilesCol</span><span class="o">=</span><span class="s1">'canonical_smiles'</span><span class="p">)</span>
<span class="n">df</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<tr style="text-align: right;">
<th></th>
<th>doc_id</th>
<th>molregno</th>
<th>standard_relation</th>
<th>standard_value</th>
<th>standard_units</th>
<th>standard_flag</th>
<th>standard_type</th>
<th>pchembl_value</th>
<th>canonical_smiles</th>
<th>compound_chembl_id</th>
<th>ROMol</th>
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<th>0</th>
<td>5839</td>
<td>189018</td>
<td>=</td>
<td>82.0</td>
<td>nM</td>
<td>1</td>
<td>EC50</td>
<td>7.09</td>
<td>CCCCCCCCc1ccc(NC(=O)[C@@H](N)COP(=O)(O)O)cc1.O...</td>
<td>CHEMBL332050</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
</tr>
<tr>
<th>1</th>
<td>5839</td>
<td>188442</td>
<td>=</td>
<td>322.1</td>
<td>nM</td>
<td>1</td>
<td>EC50</td>
<td>6.49</td>
<td>CCCCCCCCCCCCCCONC(=O)[C@@H](N)COP(=O)(O)O.OC(=...</td>
<td>CHEMBL115505</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
</tr>
<tr>
<th>2</th>
<td>5839</td>
<td>188375</td>
<td>=</td>
<td>260.0</td>
<td>nM</td>
<td>1</td>
<td>EC50</td>
<td>6.58</td>
<td>CCCCCCc1ccc(NC(=O)[C@H](N)COP(=O)(O)O)cc1.OC(=...</td>
<td>CHEMBL115344</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
</tr>
<tr>
<th>3</th>
<td>5839</td>
<td>188376</td>
<td>=</td>
<td>598.4</td>
<td>nM</td>
<td>1</td>
<td>EC50</td>
<td>6.22</td>
<td>CCCCCCCCCCCCNC(=O)[C@@H](N)COP(=O)(O)O.OC(=O)C...</td>
<td>CHEMBL324358</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
</tr>
<tr>
<th>4</th>
<td>5839</td>
<td>188766</td>
<td>=</td>
<td>12.7</td>
<td>nM</td>
<td>1</td>
<td>EC50</td>
<td>7.90</td>
<td>CCCCCCCCCCCCCCNC(=O)[C@H](N)COP(=O)(O)O.OC(=O)...</td>
<td>CHEMBL332472</td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
</tr>
</tbody>
</table>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [4]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">groups_to_df</span><span class="p">(</span><span class="n">groups</span><span class="p">,</span><span class="n">mols</span><span class="p">,</span><span class="n">include_core</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">redraw_sidechains</span><span class="o">=</span><span class="kc">False</span><span class="p">):</span>
<span class="sd">""" converts the results of an r-group decomposition into a humanly useful</span>
<span class="sd"> DataFrame</span>
<span class="sd"> </span>
<span class="sd"> """</span>
<span class="n">cols</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'Mol'</span><span class="p">]</span><span class="o">+</span><span class="nb">list</span><span class="p">(</span><span class="n">groups</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
<span class="k">if</span> <span class="n">redraw_sidechains</span><span class="p">:</span>
<span class="k">for</span> <span class="n">k</span><span class="p">,</span><span class="n">vl</span> <span class="ow">in</span> <span class="n">groups</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
<span class="k">if</span> <span class="n">k</span><span class="o">==</span><span class="s1">'Core'</span><span class="p">:</span>
<span class="k">continue</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">v</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">vl</span><span class="p">):</span>
<span class="n">vl</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">RemoveHs</span><span class="p">(</span><span class="n">v</span><span class="p">)</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">Compute2DCoords</span><span class="p">(</span><span class="n">vl</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
<span class="k">if</span> <span class="ow">not</span> <span class="n">include_core</span><span class="p">:</span>
<span class="n">cols</span><span class="o">.</span><span class="n">remove</span><span class="p">(</span><span class="s1">'Core'</span><span class="p">)</span>
<span class="k">del</span> <span class="n">groups</span><span class="p">[</span><span class="s1">'Core'</span><span class="p">]</span>
<span class="n">groups</span><span class="p">[</span><span class="s1">'Mol'</span><span class="p">]</span> <span class="o">=</span> <span class="n">mols</span>
<span class="n">frame</span> <span class="o">=</span> <span class="n">pd</span><span class="o">.</span><span class="n">DataFrame</span><span class="p">(</span><span class="n">groups</span><span class="p">,</span><span class="n">columns</span><span class="o">=</span><span class="n">cols</span><span class="p">)</span>
<span class="n">PandasTools</span><span class="o">.</span><span class="n">ChangeMoleculeRendering</span><span class="p">(</span><span class="n">frame</span><span class="p">)</span>
<span class="k">return</span> <span class="n">frame</span>
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<h1 id="Do-the-R-group-decomposition-to-get-something-to-work-with">Do the R-group decomposition to get something to work with<a class="anchor-link" href="#Do-the-R-group-decomposition-to-get-something-to-work-with">¶</a></h1><p>For the demo here I'm going to use the structures from <a href="https://pubs.acs.org/doi/abs/10.1021/jm5010336">this paper</a>, which is in ChEMBL as <a href="https://www.ebi.ac.uk/chembl/document_report_card/CHEMBL3352346/">CHEMBL3352346</a>.</p>
<p>To get the data in the form we need, we start with an R-group decomposition. This is another one of those topics where I need to do a blog post and/or write more documentation... fortunately, we're just doing the basics here:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># we need to provide a scaffold to use for the r-group decomposition:</span>
<span class="n">doc_scaffold</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmarts</span><span class="p">(</span><span class="s1">'[*:1]c1nc([*:2])on1'</span><span class="p">)</span>
<span class="c1"># filter the data to just include the rows from our document:</span>
<span class="n">doc_id</span> <span class="o">=</span> <span class="mi">89753</span>
<span class="n">docdf</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">doc_id</span><span class="o">==</span><span class="n">doc_id</span><span class="p">]</span>
<span class="c1"># align all the molecules to the scaffold:</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">Compute2DCoords</span><span class="p">(</span><span class="n">doc_scaffold</span><span class="p">)</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">docdf</span><span class="o">.</span><span class="n">ROMol</span><span class="p">:</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">GenerateDepictionMatching2DStructure</span><span class="p">(</span><span class="n">m</span><span class="p">,</span><span class="n">doc_scaffold</span><span class="p">)</span>
<span class="c1"># do an r-group decomposition:</span>
<span class="n">groups</span><span class="p">,</span><span class="n">unmatched</span> <span class="o">=</span> <span class="n">rdRGroupDecomposition</span><span class="o">.</span><span class="n">RGroupDecompose</span><span class="p">([</span><span class="n">doc_scaffold</span><span class="p">],</span><span class="n">docdf</span><span class="o">.</span><span class="n">ROMol</span><span class="p">,</span><span class="n">asSmiles</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">asRows</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="c1"># and look at the results:</span>
<span class="n">ms</span> <span class="o">=</span> <span class="p">[</span><span class="n">y</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">docdf</span><span class="o">.</span><span class="n">ROMol</span><span class="p">)</span> <span class="k">if</span> <span class="n">x</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">unmatched</span><span class="p">]</span>
<span class="n">res</span> <span class="o">=</span> <span class="n">groups_to_df</span><span class="p">(</span><span class="n">groups</span><span class="p">,</span><span class="n">ms</span><span class="p">,</span><span class="n">include_core</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">res</span><span class="o">.</span><span class="n">head</span><span class="p">()</span>
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<th>Mol</th>
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<td><img data-content="rdkit/molecule" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAAABmJLR0QA/wD/AP+gvaeTAAATzUlEQVR4nO3ce1RU1R4H8O+8eBvx0sVDEUhNfAEimfiAluKYuFpquNICrxlmdqVSaCoL7u2JlTnLCym3q0nqsiSF8JmxIjWfoIYhKOaEBoSIE4Igz9n3j60T8pwZ2Fjw+/zFmtl7n83Md/bZ55x9joQxBkK6m/R+d4D0ThQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwhBwSJCULCIEBQsIgQFiwjRF4NVV1d3v7vQ+8nvdwd6VGVl5VtvvXXu3LmcnBzPVtzd3WUy2f3uYy8hYYzd7z70EK1WO2PGjFOnTtna2t68ebN1AXNz88GDB3t5eT366KNvvvlmz/ewN+krwSorKwsJCcnJyRk8eHBGRoa9vb2mlcLCQp1OB2DEiBEWFhaHDh2ytra+3x3/22J9QFERCwuLBzB8+PCioqL2ilVXV587dy41NXXYsGEAVq1a1ZOd7GV6f7A0GubpySQS3fz571+7ds2QKllZWVKp1MzM7MKFC6K711v18mBduMDc3BjA/P1ZebkRFZ999lkA06ZNE9a1Xq6Xn26IjERREaZMwfffw8HBiIrx8fGBDz306e3bLDVVWO96s14+eS8qwr/+hXXrYGVldF326aeSF1/E4MHIy4OlpYDe9WbiR6y0NOzZgx07hG/orpgYFBRg82b8739wc8PEiaakCoDk+efh64vCQsTHd3cfez/xwSopQWgorl8XvqG7RozAjz8CQEUFDhxAdbWpDclkSEiARILVq1FQ0H0d7BPEB6t/f+zeDTs74RtqZsgQnD6N336DUokunYqaMAEREZg4EXRG3kiC51ixsbhwAcHB2LULc+bghRcEbuteFhaoq0NNTZdnR7W1OHAAcjlqajBvXvd0rg8QPGIdPoyUFOTnIyMDFy6I3da9+vcHgLKyLjdkYdHze/NeQHCw+B5EIgGAxkax27rXgAEAcO1ad7Tl6Njze/O/O8HBksuBv3mwNBosX47jx7FgQZfb6kMEL5uRN2u/Z4Pl7V1aUVFTVcUAry41lJyMsjIUF5tQNSYGkZE4dgyOjjh27M6LXg9cX1y59s9Cvr6wtOx9EzixwfrU1VXj6zvYwuLk8OF+NjavCN3YveTydUePfjBjxrvAKtNbYQxbtwLAwoUm1OYnPqRSfPstEhLuvBjko1j80wd/Fvr4Y6xcicRE0zv5lyQ2WBnl5alnz74SFLQ1P1/n6yt0Wy0MGDAAwLUu7gszM6HRwN0dQUGmNTB0KI4dw6xZcHa+84q7TRNuvfdnCReXXjmBExssuVwOgJ/RaGpqErqtFrolWLsOHvQZONAzIgJSUyajW7fCzg5qNVxdERKif9kBeOOechs24D//gZMTpk3rSm9Nk1aRJpfI3c3cc27nPGP/THc1K3byLm82x2rs2TmWk5MTgKtXr5rcQlVVVURCwpDi4uJ//MOE6sXF+P577NuHBx/srGhpKfLysHOnCVvpurLGslDb0FGWo7q3WbHB4kvICwsLAdTX1xtVV6vVvvTSS9HR0aNHj05JSTGqblZWVnR0tL29/ZkzZ55//vlbt24ZXvfKlSv8j5SUlOrq6kmTJrl6ehq1de7rr8EYlEpMn4733oNO137RuXMBIC0NPTuo/7f8vyerTzrJnfbc3JN7O7ebWxe3Iic7O9vPzw+Avb09AGdn56SkpKampk4rNjY2JiQk2NnZATA3N+f9fOyxx86cOdNp3dLS0oiICIlEAsDR0ZEPmZ6enpmZmR1X1Gg0arU6MDBQIpHk5OQwxiZPngxg06ZNhv27LU2axAAWHc0AFhDQWelhwxjADh0ybVsmiC2JxWk45ThpG7Ui2hcTrLKyi2+8IZVKAQwcODA2NtbHx4fnY+zYsT/88EMHVbOysgICAvRhysnJSUpK6t+/PwCJRBIWFvbrr7+2WbGhoUGtVtva2gIwMzOLioqqqqo6d+7c2LFjed0lS5ZUVla2qJWTkxMbGztixAj9L83W1nbr1q0JCQkSicTS0rJ1FUOUlt4YMSLbwoI99RQD2OrVnVV47TWdre3lf//bhG0xxurq6jQazdq1a3Nzczst3KhrXHplKU5DdkaWdD3JtC12qruD1dTEkpOZoyOTSpf6+ERFRfEvpqmpaceOHe7u7vzLmzp1auuPoLy8PCoqisfRzc0tOTlZ/5ZWq1WpVBYWFgCsrKxUKtXNmzeb1z106NDo0aP1jefn5+vfamhoiI+P5yOfu7v7wYMH9W9FRETo8+Tg4BAeHh4bG7tgwQIbGxveB7lcnpKSYsLHsGHDBgDz5y+aPl01cuSxS5c6KX8pO9vGzMzNzU2n0xm7raqqKqVSyeeUADw8PKKioo4cOdJmUzU1Nc+985zNWRurs1a7K3Ybuy3DdXewZs1iAAOYUtlUUNDizerq6vj4+AceeACAQqFYsmQJX4Te1NSUnJzs4ODQfLBp3faVK1fCw8P1uzm1Wt3Q0FBSUqJ/0cvLa/futj+s3NzccePG8Y8+LCzsxo0bjLH169c7ODjMmzcvKipKqVTqd7tSqXTixIlTpkwBIJPJkpKM/llPnToVQHR0NIAxY8YYUsXDwwPA8ePHjdpQaWkpn284OTnNnTtXHy8+AYiJiTlx4oQ+YTdu3AgMDAQwJWbK8VvGbchY3R2sL79krq6s2WDTWklJyeLFi/m83s7O7sUXXxw16s4hycyZM3/55ZeOt3D06NHx48fz8oMGDeJ3aFlaWr799tu3b9/uoCIfuviw5+zsvGXLlo0bN86cOVOhUPDWZDJZYGCgWq0uLi7mVeLj43lkVSqV4Z9BeXm5XC5XKBTPPPMMgLfffrvj8pWVlTU1NStWrOBZNHxDGo1myJAhPEOXLl2qqamZNWvWq6++GhkZ6erqqk+Ym5vbkiVLNm/ezD9nd3f35iO6IF0O1mefMcbY55+z1FS2ezf76ivW4berl5eXFxoaqp/at9j3dSo9Pd3Ly8vDw0OhUISGhmo0GgMr5uXl8Vza3T0nqc/T77//3rr85s2b+RHAokWLGhoaOm2/qanptddeAxASEhIeHm5mZnb+/PkOymdnZw8ZMmTZsmVHjx4F4OLiYvitRHzqOW7cOF5l165d+hE3MDBw5cqVK1as4Mnjs0wAo0aN0v9shOpysD76iO3fzxISWGIiY4wlJBhVe//+/fv27VuzZk3Hg02bamtrL126dPHiRWMrNjY2TpgwQSKRjBkz5osvvvjjjz86Lp+enm5lZQXgiSeeqKmpaa/NI0eOREVFubi48BHRzc3t3LlzLeaCzel0bN26bWZmZgD8/PxWr14tl8sdHR15LNRqdQe3QGZkZPAZxdSpU/WHF1qtdtOmTaGhoc336RMmTHjzzTdVKpWlpaVEIul0h9Bdum/ESklh6els27au96kH8EPFQwYf3p84ccLR0RHA+PHjr1+/rn+9rq5u3759ixcv5u/qp8/Ozs4ArK2tP+OfTyvXrrHHH2fDh98yN+/37LPP8lMbUql05MiRLWKxZs2aFgfCW7du5bvv8PDw+vr61o1XV1enp6eHh4f369cPwNKlSxljfMq4d+9eA//lLurl9xW26fbt22ZmZjKZrM1DhPbk5eUNGjQIgLe396VLl/g392Cz0+qenp76w7GqqqqFd69bP/fcWy3GxFOn2IABDGD9+7N33z3Nj1qcnZ0zMjJYq1hw3t7ecXFxeXl5arWaHzhHRUV1elKwuro6JSXlp59+YozFxMQAiIuLM/bjMk1fDBafzRh4sNZcUVHRyJEjAVg1u+/Hx8fnnXfeycvLa10+JSXFwcHJx0c7aBA7cuTP17VaNmgQCwpiixczqZT5+ammTZvWeoZXXV399ddfz58/n+/19FMlqVSqVquN7Ty/ejFjxgxjK5qmLwZr7dq1ACIjI02oq9Vq7e3t7ezshgwZEhcX1+k9+JcvVwYEMIDJ5ezdd1lj453XDx5ko0czgJmbs4SE2x2fvqqtrf3uu++ioqKsra2trKxeeeUVE3rOL5va29ubcKrMBH0xWPPnzwfQ3uynPVu2bJk3b15qaqpUKjU3N6+trTWwYn09U6mYVMoA9s9/MsZYYSGzsmIAGzaMGXCZ6k+xsbEAVqxY8fLLL8+cOdOo/jPG+IFFz8zfe/kt9m06deoUgEceecSoWt9+++2OHTsOHz6s0+l8fX31U+xOKRSIj0dGBoYORU0NCgqQmYmnn0Z4OLKzYdQqNX6x68yZM9u3b9+7dy+/um84for45MmTRtUyTZ8L1o0bNzQajbW19fDhw42qyL8PvvhHfzXTcMHByMvDpEl3bqZdvx5ffAEbG+MaGT9+vEQiyc7O9vf3x91fiOF4t42tZZo+F6yTJ08yxvz9/ZuvFetURUXFL7/8YmVlxWcqJgQLd29ZGjoUZWUm3gDr4ODg4eFx69Ytfv3H2IjwQbpngtW3nkEKU/eDPI5+fn5ZWVkmVNfjSwYnTjStNgAEBARoNBp+HsvYnVpAQIBUKj179mx9fT0/MStOnxuxeLB8fX3r6+vnzZt3+PBhw2t5e3uXlJTY29t7eXXtzp8u4INlRUWFRCI5ffp0Q0NDp1Xq6+sjIiL279/fr1+/wYMH19bWfvTRR5WVlWI72gMHCH8pGRkZdnZ2/v7+77//PgALC4udO3d2Wotf1ly5ciUApVLZA/1sz48//gjAx8dn6NChAM6ePdtx+YqKiuDgYACurq7bt29XKBT82NDc3Dw0NPTzzz//48YNEf3sc8E6f/68m5sbgFGjRi1duhSATCZL5Bc628dvzeDlY2Nje6arbaqpqVEoFDKZ7KmnngKwYcOGDgr//vvvvr6+AJydnaOjo/kp+5CQkKCgIP2Dx78bO5YFBjK1mpWUdGM/+1ywGGPFxcV8VaCLi4tKpdIvjGnvzKFOpzty5Mi6deuCgoIA7Nmzp4c73AJfgJWYmPjll1+Wlpa2V+zy5csPPfQQgIcffvjll18GIJFIVt9dzHrt2rWkpKS5TzzRYGl5ZwmdTMaCgphhays61ReDxRjTarWTJk0CYGdnt2rVKj4XXrhwYQcLY4qKivjaLwOXtYjDB84XXnihUX8iv5VTp07pF9U8/fTTAMzMzLa1uURAq2XJySwsjFlbM1dXtmvXneVPXdNHg8UYq62tffLJJ/k06/XXX+eh+fDDD1sUu3r1qlqtnjp1qlwud3JyWrZs2X3pbXP79u17/PHHcXc5dXp6el1dXfMCVVVVfLXFjBkzQkJCANjY2Ozfv7+Tdm/eZNnZpi1/aq3vBosx1tjYqJ9mqVSqOXPm6JeFFRQUxMfHjxs3ju8oAVhaWs6ePbu6uvr+9pnbvn07fxg9Z2dnt3DhwvT0dH3/09LSwsPD+ZLGAQMGZGdnG9r0V1+xjz9my5d3sYd9Olhc/N1HjKpUqsuXL/ObwPTfmZWVVWhoaHJysmm36wiVm5sbHx/fZm9//vlnftjo4eFR0Ormg45UVjKplFlYsHtHQWNRsBhjbMOGDTKZTD848TEgIiLim2++MWFpa88rKCj44IMP+HUefcIA+Pv7mzIjHD6cASwrqytd6uWP4zZcWlpafn7+2rVrlUplWFjY9OnTRZ+bFuHq1aupqal79uzh13w++eQTG2OvRwJYtAibNyMxEcuWmdwTCtY9dDqd1KTnf/zVdOkfWb8ey5Zh4UJs3mxyB3rDh9iNekeq0LV/pPaRR3InT06uqupKB2jEIi01NDTY2trW1tZqtdoHO39WTtt6yQ+UdCOFQuHr68sYy87ONrkRChZpA19D0ZW1phQs0oYxY8YA2LJlCz+zakILNMciLWm1WqVSmZ+fzx9YN3DgwNmzZ8+aNSsoKMjwZbcULHKPwsJCpVJ58eLFgQMHBgcHZ2Zm/vbbb/wtFxeX2bNnz507d/LkybJO11Z35ewq6WV+/vln/WI1/uQInU534sSJmJgYz2bPy3RycoqMjDxw4ECbN/hzFCxyR2ZmJn8eYnBwcJvPMsnNzY2Li/P29ubxUigUWm27j5mkXSEBgNTU1AULFtTW1s6ZM2fbtm38KWLtOX/+/M6dO8vLy9etW9duIYE/AfI3kZCQwM/UL1++3JCnDxuCgtWn6XS6uLg4ABKJpHsfREO7wj7t+PHjEydOlMlkGzduDA8P78aWKVh9XWJiopeXl1Kp7N5mKVhECLqkQ4SgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEYKCRYSgYBEhKFhECAoWEeL/QWBzir5n53QAAAAASUVORK5CYII=" alt="Mol"/></td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
</tr>
<tr>
<th>1</th>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
</tr>
<tr>
<th>2</th>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
</tr>
<tr>
<th>3</th>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAAABmJLR0QA/wD/AP+gvaeTAAAT4UlEQVR4nO3ce1RU1R4H8N+8eBvy0sVLXqELFAVEMkGFFuiYuFpqcMMcuGqYaU6p4FQadO2FlctZBim3q0LqsiTFUMmMFSn5ZMRABBWdUIEQYURwcHjNvn9snJDnzMDGgt/nL9bM3vtsZr6zzz7n7HM4hBBAaKBxn3YH0NCEwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQExgsxAQGCzGBwUJMYLAQE8MxWE1NTU+7C0Mf/2l3YFDV19e///77hYWFBQUFrl04OTnxeLyn3cchgkMIedp9GCQKhWLOnDkXLlwwNzd/8OBB1wKGhobOzs5ubm7PP//8xo0bB7+HQ8lwCVZ1dfWsWbMKCgqcnZ2zs7MtLS3lXZSVlanVagAYP368kZHRyZMnTU1Nn3bH/7HIMFBeTsLDEwHAw8OjvLy8p2JKpbKwsDAjI2PcuHEAsGHDhsHs5BAz9IMllxNXV8LhqCMjP7l79642VfLy8rhcroGBwdWrV1l3b6ga4sG6epU4OBAA4udHamp0qLh06VIACA0NZda1IW6In26IiYHycpg5E375BaysdKiYmJgY8OyzXz16RDIymPVuKBvik/fycvjgA9i2DUxMdK5LvvqKs2oVODtDcTEYGzPo3VDGfsQ6fBiOHoUDB5hv6LG4OLh+HVJT4X//AwcHCAzUJ1UAwHn9dfDxgbIySEwc6D4OfeyDVVkJYWFw7x7zDT02fjz89hsAQF0dHD8OSqW+DfF4kJQEHA5s3gzXrw9cB4cF9sEaNQqOHAELC+Yb6sDdHS5ehDt3QCiEfp2KmjYNoqIgMBDwjLyOGM+x4uPh6lUIDoZDh2DBAnjjDYbbepKRETQ1QWNjv2dHKhUcPw58PjQ2QkTEwHRuGGA8Yp06BenpUFIC2dlw9SrbbT1p1CgAgOrqfjdkZDT4e/MhgHGw6B6EwwEAaG1lu60njR4NAHD37kC0ZW09+HvzfzrGweLzAf7hwZLLYfVqOHsWFi3qd1vDCONlM/wO7Q9usDw9q+rqGhsaCIBbvxpKS4Pqaqio0KNqXBzExMCZM2BtDWfOtL/o9sy9ZfVb/yrk4wPGxkNvAsc2WF/Z28t9fJyNjM57ePiama1hurEn8fnbTp/+dM6cjwA26N8KIbB3LwBAdLQetemJDy4XfvoJkpLaXwzyFiz7/dO/Cn3xBaxbB8nJ+nfyb4ltsLJrajIuXVoTFLS3pETt48N0W52MHj0aAO72c1+YkwNyOTg5QVCQfg2MHQtnzsC8eWBr2/6Kk1kbPPz4rxJ2dkNyAsc2WHw+HwDoGY22tjam2+pkQIJ16MQJb0dH16go4OozGd27FywsQCoFe3uYNUvzshXAe0+U27EDvvwSbGwgNLQ/vdXP4brDfA7fycCp4FHBYsvFA9Us28k7v8Mcq3Vw51g2NjYAcPv2bb1baGhoiEpKcq+oqPj3v/WoXlEBv/wCWVkwcmRfRauqoLgYDh7UYyv9V91aHWYe5mXsNbDNsg0WXUJeVlYGAM3NzTrVVSgUb731Vmxs7MSJE9PT03Wqm5eXFxsba2lpmZ+f//rrrz98+FD7urdu3aJ/pKenK5XK6dOn27u66rR16vvvgRAQCmH2bPj4Y1Crey66cCEAwOHDMLiD+n9r/nteed6Gb3P0wdGiR0UD3Dq7FTkymczX1xcALC0tAcDW1jYlJaWtra3Piq2trUlJSRYWFgBgaGhI+/nCCy/k5+f3WbeqqioqKorD4QCAtbU1HTJdXV1zcnJ6ryiXy6VSaUBAAIfDKSgoIITMmDEDAHbt2qXdv9vZ9OkEgMTGEgDi799X6XHjCAA5eVK/bekhvjIeLoJNgY2iVcGifTbBqq6+9t57XC4XABwdHePj4729vWk+Jk+e/Ouvv/ZSNS8vz9/fXxOmgoKClJSUUaNGAQCHwwkPD//jjz+6rdjS0iKVSs3NzQHAwMBALBY3NDQUFhZOnjyZ1l2+fHl9fX2nWgUFBfHx8ePHj9f80szNzffu3ZuUlMThcIyNjbtW0UZVVe348TIjI/LKKwSAbN7cV4V33lGbm9/8z3/02BYhpKmpSS6Xb926taioqM/CrerWFbdWwEXg5fNS7qXot8U+DXSw2tpIWhqxtiZc7gpvb7FYTL+Ytra2AwcOODk50S8vJCSk60dQU1MjFotpHB0cHNLS0jRvKRQKiURiZGQEACYmJhKJ5MGDBx3rnjx5cuLEiZrGS0pKNG+1tLQkJibSkc/JyenEiROat6KiojR5srKyEolE8fHxixYtMjMzo33g8/np6el6fAw7duwAgMjIJbNnSyZMOFNa2kf5UpnMzMDAwcFBrVbruq2GhgahUEjnlADg4uIiFotzc3O7baqxsfG1D18zu2RmcsnkSN0RXbelvYEO1rx5BIAAEKGw7fr1Tm8qlcrExMRnnnkGAAQCwfLly+ki9La2trS0NCsrq46DTde2b926JRKJNLs5qVTa0tJSWVmpedHNze3Ike4/rKKioilTptCPPjw8vLa2lhCyfft2KyuriIgIsVgsFAo1u10ulxsYGDhz5kwA4PF4KSk6/6xDQkIAIDY2FgAmTZqkTRUXFxcAOHv2rE4bqqqqovMNGxubhQsXauJFJwBxcXHnzp3TJKy2tjYgIAAAZsbNPPtQtw3paqCD9e23xN6edBhsuqqsrFy2bBmd11tYWKxatcrLq/2QZO7cuTdu3Oh9C6dPn546dSotP2bMGHqHlrGx8aZNmx49etRLRTp00WHP1tZ2z549O3funDt3rkAgoK3xeLyAgACpVFpRUUGrJCYm0shKJBLtP4Oamho+ny8QCBYvXgwAmzZt6r18fX19Y2Pj2rVraRa135BcLnd3d6cZKi0tbWxsnDdv3vr162NiYuzt7TUJc3BwWL58eWpqKv2cnZycOo7ojPQ7WF9/TQghu3eTjAxy5Aj57jvS67erUVxcHBYWppnad9r39SkzM9PNzc3FxUUgEISFhcnlci0rFhcX01xaPD4nqcnTn3/+2bV8amoqPQJYsmRJS0tLn+23tbW98847ADBr1iyRSGRgYHDlypVeystkMnd395UrV54+fRoA7OzstL+ViE49p0yZQqscOnRIM+IGBASsW7du7dq1NHl0lgkAXl5emp8NU/0O1uefkx9/JElJJDmZEEKSknSq/eOPP2ZlZW3ZsqX3waZbKpWqtLT02rVrulZsbW2dNm0ah8OZNGnSN998c//+/d7LZ2ZmmpiYAMBLL73U2NjYU5u5ublisdjOzo6OiA4ODoWFhZ3mgh2p1WTbtn0GBgYA4Ovru3nzZj6fb21tTWMhlUp7uQUyOzubzihCQkI0hxcKhWLXrl1hYWEd9+nTpk3buHGjRCIxNjbmcDh97hAGysCNWOnpJDOT7NvX/z4NAnqoeFLrw/tz585ZW1sDwNSpU+/du6d5vampKSsra9myZfRdzfTZ1tYWAExNTb+mn08Xd++SF18kHh4PDQ1HLF26lJ7a4HK5EyZM6BSLLVu2dDoQ3rt3L919i0Si5ubmro0rlcrMzEyRSDRixAgAWLFiBSGEThmPHTum5b/cT0P8vsJuPXr0yMDAgMfjdXuI0JPi4uIxY8YAgKenZ2lpKf3mRnY4re7q6qo5HGtoaIh+fN36tdfe7zQmXrhARo8mAGTUKPLRRxfpUYutrW12djbpEgvK09MzISGhuLhYKpXSA2exWNznSUGlUpmenv77778TQuLi4gAgISFB149LP8MxWHQ2o+XBWkfl5eUTJkwAAJMO9/14e3t/+OGHxcXFXcunp6dbWdl4eyvGjCG5uX+9rlCQMWNIUBBZtoxwucTXVxIaGtp1hqdUKr///vvIyEi619NMlbhcrlQq1bXz9OrFnDlzdK2on+EYrK1btwJATEyMHnUVCoWlpaWFhYW7u3tCQkKf9+DfvFnv708ACJ9PPvqItLa2v37iBJk4kQAQQ0OSlPSo99NXKpXq559/FovFpqamJiYma9as0aPn9LKppaWlHqfK9DAcgxUZGQkAPc1+erJnz56IiIiMjAwul2toaKhSqbSs2NxMJBLC5RIA8uabhBBSVkZMTAgAGTeOaHGZ6i/x8fEAsHbt2rfffnvu3Lk69Z8QQg8sBmf+PsRvse/WhQsXAOC5557TqdZPP/104MCBU6dOqdVqHx8fzRS7TwIBJCZCdjaMHQuNjXD9OuTkwKuvgkgEMhnotEqNXuzKz8/fv3//sWPH6NV97dFTxOfPn9epln6GXbBqa2vlcrmpqamHh4dOFen3QRf/aK5mai84GIqLYfr09ptpt2+Hb74BMzPdGpk6dSqHw5HJZH5+fvD4F6I92m1da+ln2AXr/PnzhBA/P7+Oa8X6VFdXd+PGDRMTEzpT0SNY8PiWpbFjobpazxtgraysXFxcHj58SK//6BoROkgPTrCG1zNIQd/9II2jr69vXl6eHtU16JLBwED9agMA+Pv7y+Vyeh5L+52aWq1ubW319/fncrmXLl1qbm6mJ2bZGXYjFg2Wj49Pc3NzRETEqVOntK/l6elZWVlpaWnp5ta/O3/6gQ6WdXV1HA7n4sWLLS0tfVZpbm5+9dVXRSKRqamps7OzSqX6/PPP6+vr2XZ0EA4Q/lays7MtLCz8/Pw++eQTADAyMjp48GCftehlzXXr1gGAUCgchH725LfffgMAb2/vsWPHAsClS5d6L19XVxccHAwAI0eO/PLLLwUCAT02NDQ0DAsL27179/3aWhb9HHbBunLlioODAwB4eXmtWLECAHg8XjK90NkzemsGLR8fHz84Xe1WY2OjQCDg8XivvPIKAOzYsaOXwpWVZPbsh9bWnvb29uvXr6en7GfNmhUUFKR58PjPkyeTgAAilZLKygHs57ALFiGkoqKCrgq0s7OTSCSahTE9nTlUq9W5ubnbtm0LCgoCgKNHjw5yhzuhC7CSk5O//fbbqqqqnopdu0ZcXOjSOMWbb8YCAIfD2bJlC3337t27KSkpC196qcXYuH0JHY9HgoKIdmsr+jQcg0UIUSgU06dPBwALC4sNGzbQuXB0dHQvC2PKy8vp2i8tl7WwQwfON954o1VzIr87CxYQADJ1Klm8mPj4KMzMrPZ1u0RAoSBpaSQ8nJiaEnt7cuhQ+/Kn/hmmwSKEqFSql19+mU6z3n33XRqazz77rFOx27dvS6XSkJAQPp9vY2OzcuXKp9LbjrKysl588UV4vJw6MzOzqampa7H798mqVSQ0lACQESNITk5fN008eEBkMv2WP3U1fINFCGltbdVMsyQSyYIFCzTLwq5fv56YmDhlyhS6owQAY2Pj+fPnK5XKp9tnav/+/fRh9FRo6MroaJKZ+cQiy9pa8vzzBICMHk1kMq2b/u478sUXZPXqfvZwWAeLSnz8iFGJRHLz5k16E5jmOzMxMQkLC0tLS9Pvdh2mLl++/MEHH3h5efn5ZdFp0ogRJDKy/frjnTvE0ZG4uJAu9x70qr6ecLnEyIh0NwpqD4NFCCE7duzg8XiawYnOvaKion744Qc9lrYOPrmcSKUkIIBwOASA/Otf5No1sns3KSoiPU/ue+bhQQBIXl5/ujTEH8etvcOHD5eUlGzdulUoFIaHh8+ePZv1uWkWysogM7P9EiSXC3o9GwBgyRJITYXkZFi5Uu+eYLCeoFaruXo9/+NvJTUVnn0WzpyB9ev1qr99O6xcCdHRkJqqdx8wWKgzVX7+jTVrLlpbR/fjOSUYLNRZS0uLubm5SqVSKBQj+35WTvf+8cM+GnACgcDHx4cQIpPJ9G4Eg4W6QddQ9GetKQYLdWPSpEkAsGfPHplMpt9kCedYqDOFQiEUCktKSugD6xwdHefPnz9v3rygoCDtl91isNATysrKhELhtWvXHB0dg4ODc3Jy7ty5Q9+ys7ObP3/+woULZ8yYwetzbXV/zq6iIeby5cuaxWr0yRFqtfrcuXNxcXGuHZ6XaWNjExMTc/z48W5v8KcwWKhdTk4OfR5icHBwt88yKSoqSkhI8PT0pPESCAQKRY8rJnBXiAAAMjIyFi1apFKpFixYsG/fPvoUsZ5cuXLl4MGDNTU127Zt67EQw58A+odISkqiF7JWr16tzdOHtYHBGtbUanVCQgIAcDicgX0QDe4Kh7WzZ88GBgbyeLydO3eKRKIBbBmDNdwlJye7ubkJhcKBbRaDhZjASzqICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEmMFiICQwWYgKDhZjAYCEm/g80mKp69T4CvAAAAABJRU5ErkJggg==" alt="Mol"/></td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAMgAAADICAIAAAAiOjnJAAAABmJLR0QA/wD/AP+gvaeTAAARp0lEQVR4nO3de1CTZ77A8V9CAE2kQIoXkHK1WNApAlXbomgZ7FQHdRc22rWbls4gtbsuai/isc5Ez3ROaW1PU+pUbLU1sx1tYWiVdtwZBLSKtkCRaykokUXkJgoIhGvIc/54ajYr1kLy/t6Qnt/nrzSE53mbfH0vSd4XCWMMCBGa1N4LQH6fKCyCgsIiKCgsgoLCIigoLIKCwiIoKCyCgsIiKCgsgoLCIigoLIKCwiIoKCyCgsIiKCgsgoLCIigoLIKCwiIoKCyCgsIiKCgsgoLCIigoLIKCwiIoKCyCgsIiKCgsgoLCIigoLIKCwiIoKCyCgsIiKCgsgoLCIigoLIKCwiIoKCyCgsIiKCgsgoLCIigoLIKCwiIoKCyCgsIiKCgsgoLCIigoLIKCwiIoKCyCgsIiKCgsgoLCIigoLIKCwiIoKCyCgsIiKCgsgoLCIigoLIKCwiIoZPZegN+zS5cu5eXldXZ2BgUFBQcHBwUFBQQEuLi42Hu5xEBhYXnnnXfS0tJ8fHxaW1st7/f09AyyEBYWtnDhQg8PD3stJxIJY8zey/A79Pbbb+/atUsikaSkpAQGBl69elWv11+9evXatWtjY2PjH69UKvkqLTg4mN9YtmyZTObA/+wpLIExxl5//fX33nvPycnpyJEjL7zwguVPR0dHm5ubr/6nK1eu9Pb2Wj7MxcVl796927Ztk8vl4i6+YCgsITHGtm/fnpGR4eLicuzYscTExAn+YkdHh16v52s1vV5fWFjY0tLyxhtvvPnmm6gLjIgRgRiNbMeO/wGA6dOnnzp1ypahSkpKpFKpi4tLfX29UIsnMgpLGMPD7E9/Yg880BMZueL06dO2D5iUlAQATz/9tO1D2QWFJYDBQRYfzwCYhwe7eFGYMTs6Ovih4smTJ4UZUVwUlq0MBrZqFQNgSiUrLhZy5IyMDADw8/MzGAxCjisKCssmPT0sOpoBsDlzWFWVwIMbjcbw8HAA+O+9ewUeGh8dFdrk6afh9Gnw94f8fJg3T/jxi4qKSnbuTO3okJ0+DUFBwk+Ax95lO7ZLl1hEBNPrMedQqxkAW7cOcw7h0YfQE3X0KPzxjwAAe/ZAezscPQpFRRARAevWAe6nf/v3g4cH5ObCt99iTiMw0cPq74fNm2HrVkhJgcFBsWe3jYsLVFaKPuvs2aDRAABs3w5DQ6JPbyXRw8rMhBdfhAMHQKWCTz8Ve3bbpKTAxx//+z/ffRe2bBFlPbJ1K4SHg14P+/fjTyYM0cOqr4fwcACAiAiorxd7dtsoFPDQQ1BX98t/vvYaZGZCfDz+xDIZHDgAEgm89RY0NuLPJwDRwwoJgaoqAIDKSggJEXt2m/31r3DmjD0mXrYMNm2CwUE4edIe00+a6G839PXBjh2gUMDAAHzwATjgp/cdHZCWBs7O8Mkn4k7c1gZ1dfDUU+LOaiVx11hnz0JCAgQGwgcfwKOPQkwMHDsm6gIIQSYDnQ5yckSf2NsbFi92lEMfccPq7IT8/F+OrJqaoKwM2tpEXQAhKJXg7Aw9PTA8LPrcjnPoI25Y/CuRRuPdtx2KRAIzZwJj0Nkp+tyOc+hDYVlj9mwAgI4O0Sd2nEMfcb9VbRmTk9O/bzsau4W1di28/TZ88cUvhz5TmL3DuteZBVOf3cLatg1KS+HECYiJEX3uybFfWPw2hTVxLS1QUABOTrBggbgTW4P2sawRHHw+IuI1gyFT1Fl1Ohgbgz/8AR58UNR5rSJqWJddXVMWLvwvLy8A+MrNbdMjj/yvY546p1BcKy9/T68/J+qs//gHAEBSkqiTWkvU17UH4JOamiVyOQBcHx09Xlc3s79fzAUQyuzZswGgQ8xt4cWLUFcHc+bAqlXiTWoDUddY/NReo9F4122Hw8Nqb28Xb0qdDgDghRfAQdbxdgvLyckJHDws8dZYQ0OQlQUA8Je/iDSjzey8xrrnhQymPi8vL5lM1tXVNTo6KsJ0WSdOrH344TN//jMsXCjCdIIQNSzLtZRDbwqlUqmXlxdjrFOUj3UOf/rpt6WldcuXizCXUOywxuJrKdSwhoaGRkZGDAYDxuAA0NfXJ5FIACA/Px9pCrOWlpbCwsJp06Y9++yz2HMJyA5h9ff3Z2dnf/XVV4ATVn5+fkRERFJSUkhIyMcffyz41vabb75ZsGBBW1ubm5vbiy+++NJLL+EVDABHjx4dGxtbv369p6cn3izCE/OUoKampjVr1kgkEn51HrlcvnXr1r6+PqHGb2hoiL/zTeEH77yLGB4eLsjFFBhjdXV1q+4c7UdGRiYnJzs7OwNAUFDQmTNnbB+/urp637590dHRw8PD5jsfeeQRALDxKiPiEymskZERrVb7wAMPAMD06dO3bt2akJDAX6G5c+ceOnTIaDTaMv7AwIBGo5k2bRoAuLu7a7XakZGRrKyswMBAPktcXFxlZaXV4/f392s0Gn6VR6VSqdVqx8bGGGMVFRUREREAwK+xZt0/kpqaGo1GExoaav7Xbs6oqKgIAHx8fGx8fsQnRljXz541P2sJCQlNTU38/osXLz7xxBPmFUBhYaF14+fm5vKAJBKJWq1ub283/2h4eFir1bq7uwOAVCpVq9VtbW2TGtxkMmVlZfn6+ppHuHnzpuUDRkZG0tPTeXOBgYH5+fkTHJn3FGLx7RelUqlWq3Nzc81rLLVaDQBpaWmTWuapADmslhamVjOZTBUSMm/evPHrc/6yBQQEmNcr1dXVEx/+ypUra9as4b+7aNGioqKiez7s5s2baWlp/LVXKBQajWZgYGAi41dUVCxbtoyPv3jx4pKSkl97ZFVVVVRUlHnV1dvbe8+HjY2NnT9/PjU1lZfKzZw5k/c0MjLCH6bX67VabXR0tEKhcHV1PXTo0ESWdkpBC2t4mL31FlMoGABTKG5kZFjuN9xlYGAgPT2dbyhlMllKSorlWueeDAaDedvn4eGh1Wp/c2NRV1enUqkmuP3t7u5OTU3lRxtz5sw5dOgQ3/bdx+joaHp6uqurKwD4+/vn5eWNf8zmzZvNPQUEBLzyyisXLlwwj1xWVrZ79+758+ebH8N3Rl1cXD7//PP7zz7V4IRVUMDCwhgAA2Dx8exf/5rIL3V2dppfyxkzZmg0msHBwXs+Mjc3l6/k+Lavo6NjMotWwPeKACAqKmr8TrfJZNLpdPy9dZlMlpqa2tPTM/Hxq6urFy9ezMdXqVS3bt2y/GlOTk5gYGBqaur58+dNJhO/8z7bxKGhIY1Gw/9P09PTJ74YdocT1saNDICFhLB//nOyv/rzzz+b1yu+vr53rSouX768evVq/tOIiIgLFy5YsXT32f6Wl5dHR0fz+2NiYqqsujQRX3Xxtam3t/eJEycsp+Y37rlN9PLyumubyGVkZEilUgBITU39zRXnFIETVnMze/dd9p/PzqTk5+cvWrSIP92PPfbY2bNn+baPb2g8PT0nsu27P4PBYLn9TUpKSk5O5p8NeHt763Q6cwTWuXLlSsyd73mqVCq+y280GnlP3t7e5p78/Pz4Ouw+0eTk5PBSn3vuufvsVEwdNoT12Wfs/HnGGNNoWHMz6+tjycnsb39jmzezie0a35/RaDx8+LCPjw9/9s1HdikpKXcdl9nCcvsrlUr5tu/27duCDG40Gvfv3z99+nQAmD179po1a5RKpbmnkJCQXbt2lZaWTnC0wsJC/iTExsYKtYR4hAtr/37GN0x5eezAAWGW7s56xd3dPT4+PjIy8vvvvxdqZEvV1dXp6ekZGRk//fST4IPr9foVK1Y4Ozvz1W1YWFhaWtp5/tRNUlVV1dy5c/ne4W8e34xXXl7+5ZdfHj9+vLGxsa2t7fjx45ZZj7/HFraFtX49e+klFhXFmptZcjLr72eMsc5O9ve/C7JwZj09Pf39/Y6yezFeaWkpAPj4+NTV1dk4VGNjIz9sDAwMvHz58qR+t7y8vKmpqaOjo6CggDHW0tLCM7p27VpNTY3lPbaz7bNCy+utYJ7y5u7urlAo+A6sI/rxxx8BIC4uzvKtBOsEBATwN5YbGxuXL19eVlY2wV/kn5kWFxfn5OQ8+uijlj966KGHFgh9goZwX0fcsgV27HCIU97EV1JSAgBLliwRZDSlUnn69OkNGzacOnVq5cqV2dnZzzzzzK89eHBw8PLly7W1te3t7StXrly6dKlcLq+vr/fy8hJkYX4NXdxWDAsWLKitrS0pKTG/xWU7o9G4ZcuWI0eOuLi4fPbZZ5s2bbL8aXNzc3Nzc319fXNzM3+JnZycYmJiAgIC/Pz8cnNzo6OjCwoKDAbDU089JZVKe3t7fX198/Ly+D3m92KsRmGh6+vr8/DwcHZ2vn37Nt9/FwpjbN++ffv27eNvn+7cubOpqenEiRPZ2dkXL17k76zKZLKgoKCQkJDQ0FBR/+STIHtq5D4KCgoA4PHHH0ca//333+d7n5bvtSoUit27d9fU1NjrTS/HOOXDoQm7gzXe9u3bvb29k5OTu7u75XJ5bGysSqVKSEiYMWMG0owTQWGhww4LAPz9/fv7+x9++OGampop8qeBHfUA3oEghfXdd98lJCR88cUX5ilWrFgxRaoCCgvb9evXW1palErlPKH/IkphYeHXX39dUVEBd8IS8JDTdhQWLvPqip/VgzGy+fbSpUuFncIWFBYupO0gu/Mx0ZIlS7q6uhoaGuRyueDvntuCwsKFFJZer79165a3t7evry//wnRUVNSU+qv3FBYik8nEP8sTfO+nuLgY7mz7puB2ECgsVLW1tb29vYGBgbNmzRJ25PE7WFNqzx0oLFR46xLLkfnOFq2x/h+5a11iMpmys7NtH3Z0dLSyslIikURGRjY2Nt64cWPWrFn+/v62jywgCgtRbW0tAJhf8h07dmzYsOHll1+28XISlZWVg4ODoaGhHh4eljtbUwqFhWjPnj1OTk7btm2rqakBgLi4OLlcnpmZmZiYOGjDX8LhMVnuYKF+XmQdCgvRk08+GRsb29LSsnz58nPnzq1du7awsNDLy+vkyZOxsbG3bt2ybtjxe+5TMCz62gyuoaGhjRs3AoCrq2tWVhZjrLa21s/PDwDCwsKuXbtmxZj8+jNlZWWjo6NyuVwikXR1dQm94LaisNCZTKZXX30VAJycnD766CPGWGtra3h4OAD4+PhYcQ2c0tLSAwcODA8PX7p0CQDmz5+PsNS2orBEotVq+ceF/NIx3d3d/HRWT09P604FY4wdPHgQANRqtaBLKgwKSzw6nY5fqC0pKWl0dHRoaIhfTMDV1TU7O9uKAZOSkgDgww8/FHxRbUdhiSovL8/NzQ0A1q1bZzAY+AkRfCuZmZk5wUEGBgZyc3PVarVcLp8xY0ZxcTHqMluHwhJbcXHxzJkzAWDp0qWdnZ2Msb179/L9rfufOH/79u1jx44lJiZanhORl5c3Nc/jpbDsoKGhITg4GABCQ0P59Q0PHz78a3vxXV1dOp1OpVIpFApzT2FhYRqNZrJnQouJwrKP1tZWfjkdb2/vioqK8Q+4efOmTqeLj483f9tYKpVGR0drtdrr16+Lv8CTRWHZTXd394oVKwDAw8Pj3Llz/M4bN27wnvhuPt/94j21trbad4EnhU5Ytafh4eHnn38+KyvL1dV148aNDQ0NP/zwg8lkAgBXV9dVq1YlJiauW7fO8uJHjoLCsrOxsbHU1NSDBw+6ubn19vZOmzYtLi5OpVKtX7+eXw3LQVFYU0JxcXF1dbWnp+fq1atFPREeDYVFUNC3GwgKCougoLAICgqLoKCwCAoKi6CgsAgKCougoLAICgqLoKCwCAoKi6CgsAgKCougoLAICgqLoKCwCAoKi6CgsAgKCougoLAICgqLoKCwCAoKi6CgsAgKCougoLAICgqLoKCwCAoKi6CgsAgKCougoLAICgqLoKCwCAoKi6CgsAgKCougoLAICgqLoKCwCAoKi6CgsAgKCougoLAICgqLoKCwCAoKi6CgsAgKCougoLAICgqLoKCwCAoKi6CgsAgKCougoLAICgqLoKCwCAoKi6CgsAiK/wMWSXeiJZ7tkgAAAABJRU5ErkJggg==" alt="Mol"/></td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
</tr>
<tr>
<th>4</th>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
<td><img data-content="rdkit/molecule" src="data:image/png;base64,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" alt="Mol"/></td>
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</table>
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<p>For the purposes of this analysis, it's quicker/easier to get the data from the RGD code in a different format:</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">groups</span><span class="p">,</span><span class="n">unmatched</span> <span class="o">=</span> <span class="n">rdRGroupDecomposition</span><span class="o">.</span><span class="n">RGroupDecompose</span><span class="p">([</span><span class="n">doc_scaffold</span><span class="p">],</span><span class="n">docdf</span><span class="o">.</span><span class="n">ROMol</span><span class="p">,</span><span class="n">asSmiles</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">asRows</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
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<p>This next block defines the function that actually creates the SAR matrix as HTML</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">collections</span> <span class="k">import</span> <span class="n">Counter</span>
<span class="kn">from</span> <span class="nn">IPython.display</span> <span class="k">import</span> <span class="n">HTML</span>
<span class="kn">import</span> <span class="nn">base64</span>
<span class="k">def</span> <span class="nf">mol_to_img</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
<span class="n">dm</span> <span class="o">=</span> <span class="n">Draw</span><span class="o">.</span><span class="n">PrepareMolForDrawing</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
<span class="n">d2d</span> <span class="o">=</span> <span class="n">Draw</span><span class="o">.</span><span class="n">MolDraw2DCairo</span><span class="p">(</span><span class="mi">250</span><span class="p">,</span><span class="mi">200</span><span class="p">)</span>
<span class="n">dopts</span> <span class="o">=</span> <span class="n">d2d</span><span class="o">.</span><span class="n">drawOptions</span><span class="p">()</span>
<span class="n">dopts</span><span class="o">.</span><span class="n">dummiesAreAttachments</span><span class="o">=</span><span class="kc">True</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">DrawMolecule</span><span class="p">(</span><span class="n">dm</span><span class="p">)</span>
<span class="n">d2d</span><span class="o">.</span><span class="n">FinishDrawing</span><span class="p">()</span>
<span class="n">png_data</span> <span class="o">=</span> <span class="n">d2d</span><span class="o">.</span><span class="n">GetDrawingText</span><span class="p">()</span>
<span class="n">png_data</span> <span class="o">=</span> <span class="n">base64</span><span class="o">.</span><span class="n">encodebytes</span><span class="p">(</span><span class="n">png_data</span><span class="p">)</span>
<span class="n">html</span> <span class="o">=</span><span class="s1">'<img src="data:image/png;base64,</span><span class="si">%s</span><span class="s1">">'</span><span class="o">%</span><span class="k">png_data</span>.decode()
<span class="k">return</span> <span class="n">html</span>
<span class="k">def</span> <span class="nf">run_groups</span><span class="p">(</span><span class="n">groups</span><span class="p">,</span><span class="n">unmatched</span><span class="p">,</span><span class="n">mols</span><span class="p">,</span><span class="n">values</span><span class="p">,</span><span class="n">r1_label</span><span class="o">=</span><span class="s1">'R1'</span><span class="p">,</span><span class="n">r2_label</span><span class="o">=</span><span class="s1">'R2'</span><span class="p">,</span><span class="n">threshold</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="c1"># generate SAR matrix</span>
<span class="c1"># generate SMILES for each of the R-groups and map those </span>
<span class="c1"># to the R-group's molecule objects:</span>
<span class="n">r1_smiles</span> <span class="o">=</span> <span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolToSmiles</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">groups</span><span class="p">[</span><span class="n">r1_label</span><span class="p">]]</span>
<span class="n">r2_smiles</span> <span class="o">=</span> <span class="p">[</span><span class="n">Chem</span><span class="o">.</span><span class="n">MolToSmiles</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">groups</span><span class="p">[</span><span class="n">r2_label</span><span class="p">]]</span>
<span class="n">r1_lookup</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">r1_smiles</span><span class="p">,</span><span class="n">groups</span><span class="p">[</span><span class="n">r1_label</span><span class="p">]))</span>
<span class="n">r2_lookup</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">(</span><span class="nb">zip</span><span class="p">(</span><span class="n">r2_smiles</span><span class="p">,</span><span class="n">groups</span><span class="p">[</span><span class="n">r2_label</span><span class="p">]))</span>
<span class="c1"># all_r1s and all_r2s map R indices to the corresponding SMILES:</span>
<span class="n">all_r1s</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">y</span><span class="p">,</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">r1_lookup</span><span class="o">.</span><span class="n">keys</span><span class="p">())])</span>
<span class="n">all_r2s</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">y</span><span class="p">,</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">r2_lookup</span><span class="o">.</span><span class="n">keys</span><span class="p">())])</span>
<span class="c1"># labelled_mols will contain 3-tuples:</span>
<span class="c1"># (molecule_index,R1_index,R2_index)</span>
<span class="n">labelled_mols</span> <span class="o">=</span> <span class="p">[]</span>
<span class="n">residx</span> <span class="o">=</span> <span class="mi">0</span>
<span class="k">for</span> <span class="n">i</span><span class="p">,</span><span class="n">m</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">mols</span><span class="p">):</span>
<span class="k">if</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">unmatched</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">r1_idx</span> <span class="o">=</span> <span class="n">all_r1s</span><span class="p">[</span><span class="n">r1_smiles</span><span class="p">[</span><span class="n">residx</span><span class="p">]]</span>
<span class="n">r2_idx</span> <span class="o">=</span> <span class="n">all_r2s</span><span class="p">[</span><span class="n">r2_smiles</span><span class="p">[</span><span class="n">residx</span><span class="p">]]</span>
<span class="n">residx</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">labelled_mols</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">i</span><span class="p">,</span><span class="n">r1_idx</span><span class="p">,</span><span class="n">r2_idx</span><span class="p">))</span>
<span class="c1"># We only keep r groups that appear at least `threshold times in the full list:</span>
<span class="n">c1</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">()</span>
<span class="n">c2</span> <span class="o">=</span> <span class="n">Counter</span><span class="p">()</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span><span class="n">i</span><span class="p">,</span><span class="n">j</span> <span class="ow">in</span> <span class="n">labelled_mols</span><span class="p">:</span>
<span class="n">c1</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">c2</span><span class="p">[</span><span class="n">j</span><span class="p">]</span> <span class="o">+=</span> <span class="mi">1</span>
<span class="n">freq1</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">c1</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">y</span><span class="o">>=</span><span class="n">threshold</span><span class="p">]</span>
<span class="n">freq2</span> <span class="o">=</span> <span class="p">[</span><span class="n">x</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">c2</span><span class="o">.</span><span class="n">items</span><span class="p">()</span> <span class="k">if</span> <span class="n">y</span><span class="o">>=</span><span class="n">threshold</span><span class="p">]</span>
<span class="n">reverse_r1s</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">y</span><span class="p">,</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">all_r1s</span><span class="o">.</span><span class="n">items</span><span class="p">()])</span>
<span class="n">reverse_r2s</span> <span class="o">=</span> <span class="nb">dict</span><span class="p">([(</span><span class="n">y</span><span class="p">,</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="n">all_r2s</span><span class="o">.</span><span class="n">items</span><span class="p">()])</span>
<span class="n">freq_r1s</span> <span class="o">=</span> <span class="p">[</span><span class="n">reverse_r1s</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">freq1</span><span class="p">]</span>
<span class="n">freq_r2s</span> <span class="o">=</span> <span class="p">[</span><span class="n">reverse_r2s</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">freq2</span><span class="p">]</span>
<span class="n">n_r1</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">freq1</span><span class="p">)</span>
<span class="n">n_r2</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">freq2</span><span class="p">)</span>
<span class="c1"># now construct a matrix </span>
<span class="n">matrix</span> <span class="o">=</span> <span class="p">[</span><span class="kc">None</span><span class="p">]</span><span class="o">*</span><span class="p">(</span><span class="n">n_r1</span><span class="o">*</span><span class="n">n_r2</span><span class="p">)</span>
<span class="n">matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">matrix</span><span class="p">,(</span><span class="n">n_r1</span><span class="p">,</span><span class="n">n_r2</span><span class="p">))</span>
<span class="k">for</span> <span class="n">idx</span><span class="p">,</span><span class="n">i</span><span class="p">,</span><span class="n">j</span> <span class="ow">in</span> <span class="n">labelled_mols</span><span class="p">:</span>
<span class="k">if</span> <span class="n">i</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">freq1</span> <span class="ow">or</span> <span class="n">j</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">freq2</span><span class="p">:</span>
<span class="k">continue</span>
<span class="n">r1idx</span> <span class="o">=</span> <span class="n">freq1</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
<span class="n">r2idx</span> <span class="o">=</span> <span class="n">freq2</span><span class="o">.</span><span class="n">index</span><span class="p">(</span><span class="n">j</span><span class="p">)</span>
<span class="n">matrix</span><span class="p">[</span><span class="n">r1idx</span><span class="p">,</span><span class="n">r2idx</span><span class="p">]</span> <span class="o">=</span> <span class="n">idx</span>
<span class="c1"># now create the html from that</span>
<span class="n">html</span> <span class="o">=</span> <span class="s2">"<table>"</span>
<span class="n">ths</span> <span class="o">=</span> <span class="s2">""</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="s2">"<th></span><span class="si">%s</span><span class="s2"></th>"</span><span class="o">%</span><span class="k">mol_to_img</span>(r2_lookup[x]) for x in freq_r2s)
<span class="n">html</span> <span class="o">+=</span> <span class="n">f</span><span class="s2">"<tr><td></td></span><span class="si">{ths}</span><span class="s2"></tr>"</span>
<span class="k">for</span> <span class="n">i1</span><span class="p">,</span><span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">freq_r1s</span><span class="p">):</span>
<span class="n">img</span> <span class="o">=</span> <span class="n">mol_to_img</span><span class="p">(</span><span class="n">r1_lookup</span><span class="p">[</span><span class="n">x</span><span class="p">])</span>
<span class="n">row</span> <span class="o">=</span> <span class="n">f</span><span class="s2">"<tr><td></span><span class="si">{img}</span><span class="s2"></td>"</span>
<span class="k">for</span> <span class="n">i2</span><span class="p">,</span><span class="n">y</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">freq_r2s</span><span class="p">):</span>
<span class="k">if</span> <span class="n">matrix</span><span class="p">[</span><span class="n">i1</span><span class="p">,</span><span class="n">i2</span><span class="p">]</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
<span class="n">elem</span> <span class="o">=</span> <span class="n">matrix</span><span class="p">[</span><span class="n">i1</span><span class="p">,</span><span class="n">i2</span><span class="p">]</span>
<span class="n">elem</span> <span class="o">=</span> <span class="n">values</span><span class="o">.</span><span class="n">iloc</span><span class="p">[</span><span class="n">elem</span><span class="p">]</span>
<span class="k">else</span><span class="p">:</span>
<span class="n">elem</span> <span class="o">=</span> <span class="s1">''</span>
<span class="n">row</span> <span class="o">+=</span> <span class="n">f</span><span class="s1">'<td></span><span class="si">{elem}</span><span class="s1"></td>'</span>
<span class="n">row</span> <span class="o">+=</span> <span class="s2">"</tr>"</span>
<span class="n">html</span> <span class="o">+=</span> <span class="n">row</span>
<span class="n">html</span> <span class="o">+=</span> <span class="s2">"</table>"</span>
<span class="k">return</span> <span class="n">html</span>
</pre></div>
</div>
</div>
</div>
</div>
<div class="cell border-box-sizing code_cell rendered">
<div class="input">
<div class="prompt input_prompt">In [8]:</div>
<div class="inner_cell">
<div class="input_area">
<div class=" highlight hl-ipython3"><pre><span></span><span class="n">html</span> <span class="o">=</span> <span class="n">run_groups</span><span class="p">(</span><span class="n">groups</span><span class="p">,</span><span class="n">unmatched</span><span class="p">,</span><span class="n">docdf</span><span class="o">.</span><span class="n">ROMol</span><span class="p">,</span><span class="n">docdf</span><span class="o">.</span><span class="n">pchembl_value</span><span class="p">)</span>
<span class="n">HTML</span><span class="p">(</span><span class="n">html</span><span class="p">)</span>
</pre></div>
</div>
</div>
</div>
<div class="output_wrapper">
<div class="output">
<div class="output_area">
<div class="prompt output_prompt">Out[8]:</div>
<div class="output_html rendered_html output_subarea output_execute_result">
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"></td><td></td><td></td><td>8.2</td><td></td></tr><tr><td><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAPoAAADICAIAAABOPGvMAAAABmJLR0QA/wD/AP+gvaeTAAAaHUlE
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"></td><td></td><td></td><td></td><td>6.6</td></tr></table>
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<p>Let's make that more convenient by creating a single wrapper function:</p>
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<div class="prompt input_prompt">In [9]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">create_SAR_table</span><span class="p">(</span><span class="n">mols</span><span class="p">,</span><span class="n">scaffold</span><span class="p">,</span><span class="n">values</span><span class="p">,</span><span class="n">r1_label</span><span class="o">=</span><span class="s1">'R1'</span><span class="p">,</span><span class="n">r2_label</span><span class="o">=</span><span class="s1">'R2'</span><span class="p">,</span><span class="n">threshold</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
<span class="c1"># align all the molecules to the scaffold:</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">Compute2DCoords</span><span class="p">(</span><span class="n">scaffold</span><span class="p">)</span>
<span class="k">for</span> <span class="n">m</span> <span class="ow">in</span> <span class="n">mols</span><span class="p">:</span>
<span class="k">if</span> <span class="n">m</span><span class="o">.</span><span class="n">HasSubstructMatch</span><span class="p">(</span><span class="n">scaffold</span><span class="p">):</span>
<span class="n">rdDepictor</span><span class="o">.</span><span class="n">GenerateDepictionMatching2DStructure</span><span class="p">(</span><span class="n">m</span><span class="p">,</span><span class="n">scaffold</span><span class="p">)</span>
<span class="c1"># do an r-group decomposition:</span>
<span class="n">groups</span><span class="p">,</span><span class="n">unmatched</span> <span class="o">=</span> <span class="n">rdRGroupDecomposition</span><span class="o">.</span><span class="n">RGroupDecompose</span><span class="p">([</span><span class="n">scaffold</span><span class="p">],</span><span class="n">mols</span><span class="p">,</span><span class="n">asSmiles</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">asRows</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
<span class="n">html</span> <span class="o">=</span> <span class="n">run_groups</span><span class="p">(</span><span class="n">groups</span><span class="p">,</span><span class="n">unmatched</span><span class="p">,</span><span class="n">mols</span><span class="p">,</span><span class="n">values</span><span class="p">,</span>
<span class="n">r1_label</span><span class="o">=</span><span class="n">r1_label</span><span class="p">,</span><span class="n">r2_label</span><span class="o">=</span><span class="n">r2_label</span><span class="p">,</span><span class="n">threshold</span><span class="o">=</span><span class="n">threshold</span><span class="p">)</span>
<span class="k">return</span> <span class="n">html</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># Pull all the structures that contain a common scaffold:</span>
<span class="n">doc_scaffold</span> <span class="o">=</span> <span class="n">Chem</span><span class="o">.</span><span class="n">MolFromSmarts</span><span class="p">(</span><span class="s1">'[*:1]c1nc([*:2])on1'</span><span class="p">)</span>
<span class="n">match_df</span> <span class="o">=</span> <span class="n">df</span><span class="p">[</span><span class="n">df</span><span class="o">.</span><span class="n">ROMol</span><span class="o">>=</span><span class="n">doc_scaffold</span><span class="p">]</span>
<span class="c1"># Show the table for R-groups that appear at least 5 times:</span>
<span class="n">html</span> <span class="o">=</span> <span class="n">create_SAR_table</span><span class="p">(</span><span class="n">match_df</span><span class="o">.</span><span class="n">ROMol</span><span class="p">,</span><span class="n">doc_scaffold</span><span class="p">,</span><span class="n">match_df</span><span class="o">.</span><span class="n">pchembl_value</span><span class="p">,</span><span class="n">threshold</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="n">HTML</span><span class="p">(</span><span class="n">html</span><span class="p">)</span>
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"></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td>8.17</td><td></td><td></td><td></td><td></td></tr></table>
</div>
</div>
</div>
</div>
</div>
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<p>It'd be shiny if we could get that to pop up into a separate HTML window so that the images were a bit bigger, but I'm not willing to dive down that hole at the moment.</p>
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<p>This is clearly just a starting point (particularly since it can only show two sets of R groups at a time), but hopefully it's already useful.</p>
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</div>
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greg landrumhttp://www.blogger.com/profile/10263150365422242369noreply@blogger.com2