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<p class="caption"><span class="caption-text">Background</span></p>
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<li class="toctree-l1"><a class="reference internal" href="introduction.html">Features of DeepPurpose</a><ul>
<li class="toctree-l2"><a class="reference internal" href="introduction.html#features">Features</a></li>
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<li class="toctree-l1"><a class="reference internal" href="DTI.html">What is drug repurposing, virtual screening and drug-target interaction prediction?</a><ul>
<li class="toctree-l2"><a class="reference internal" href="DTI.html#drug-repurposing">Drug Repurposing</a></li>
<li class="toctree-l2"><a class="reference internal" href="DTI.html#virtual-screening">Virtual Screening</a></li>
<li class="toctree-l2"><a class="reference internal" href="DTI.html#drug-target-interaction">Drug-Target Interaction</a></li>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">Case Study</a></li>
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<p class="caption"><span class="caption-text">Package Reference</span></p>
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<li class="toctree-l1"><a class="reference internal" href="models.html">DeepPurpose.models</a><ul>
<li class="toctree-l2"><a class="reference internal" href="dtba/classifier.html">Classifier</a></li>
<li class="toctree-l2"><a class="reference internal" href="dtba/dbta.html">Drug Target Binding Affinity (DTBA) Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="encoders/transformer.html">Transformer</a></li>
<li class="toctree-l2"><a class="reference internal" href="encoders/mpnn.html">Message Passing Neural Network (MPNN)</a></li>
<li class="toctree-l2"><a class="reference internal" href="encoders/cnnrnn.html">CNN+RNN</a></li>
<li class="toctree-l2"><a class="reference internal" href="encoders/cnn.html">CNN</a></li>
<li class="toctree-l2"><a class="reference internal" href="encoders/mlp.html">MLP</a></li>
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<li class="toctree-l1"><a class="reference internal" href="dataset.html">DeepPurpose.dataset</a><ul>
<li class="toctree-l2"><a class="reference internal" href="data/read_file_training_dataset_bioassay.html">read_file_training_dataset_bioassay</a></li>
<li class="toctree-l2"><a class="reference internal" href="data/read_file_training_dataset_drug_target_pairs.html">read_file_training_dataset_drug_target_pairs</a></li>
<li class="toctree-l2"><a class="reference internal" href="data/read_file_virtual_screening_drug_target_pairs.html">read_file_virtual_screening_drug_target_pairs</a></li>
<li class="toctree-l2"><a class="reference internal" href="data/read_file_repurposing_library.html">load bioarray dataset (read_file_training_dataset_bioassay)</a></li>
<li class="toctree-l2"><a class="reference internal" href="data/read_file_target_sequence.html">read_file_target_sequence</a></li>
<li class="toctree-l2"><a class="reference internal" href="data/download_BindingDB.html">download_DrugTargetCommons</a></li>
<li class="toctree-l2"><a class="reference internal" href="data/process_BindingDB.html">process_BindingDB</a></li>
<li class="toctree-l2"><a class="reference internal" href="data/load_process_DAVIS.html">load_process_DAVIS</a></li>
<li class="toctree-l2"><a class="reference internal" href="data/load_process_KIBA.html">load_process_KIBA</a></li>
<li class="toctree-l2"><a class="reference internal" href="data/load_AID1706_txt_file.html">load_AID1706_txt_file</a></li>
<li class="toctree-l2"><a class="reference internal" href="data/load_AID1706_SARS_CoV_3CL.html">load_AID1706_SARS_CoV_3CL</a></li>
<li class="toctree-l2"><a class="reference internal" href="data/load_antiviral_drugs.html">load_antiviral_drugs</a></li>
<li class="toctree-l2"><a class="reference internal" href="data/load_broad_repurposing_hub.html">load_broad_repurposing_hub</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="chemutils.html">DeepPurpose.chemutils</a><ul>
<li class="toctree-l2"><a class="reference internal" href="chem/onek_encoding_unk.html">DeepPurpose.chemutils.onek_encoding_unk</a></li>
<li class="toctree-l2"><a class="reference internal" href="chem/atom_features.html">DeepPurpose.chemutils.atom_features</a></li>
<li class="toctree-l2"><a class="reference internal" href="chem/bond_features.html">DeepPurpose.chemutils.bond_features</a></li>
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<li class="toctree-l1"><a class="reference internal" href="oneliner.html">DeepPurpose.oneliner</a><ul>
<li class="toctree-l2"><a class="reference internal" href="oneliner_folder/repurpose.html">DeepPurpose.oneliner.repurpose</a></li>
<li class="toctree-l2"><a class="reference internal" href="oneliner_folder/virtual_screening.html">DeepPurpose.oneliner.virtual_screening</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="model_helper.html">DeepPurpose.model_helper</a></li>
<li class="toctree-l1"><a class="reference internal" href="utils.html">DeepPurpose.utils</a></li>
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<p class="caption"><span class="caption-text">Importance Function</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="model.html">Drug Target Binding Affinity (DTBA) Model</a><ul>
<li class="toctree-l2"><a class="reference internal" href="dtba/classifier.html">Classifier</a></li>
<li class="toctree-l2"><a class="reference internal" href="dtba/dbta.html">Drug Target Binding Affinity (DTBA) Model</a></li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="encoder.html">Drug/Target Encoder</a><ul>
<li class="toctree-l2"><a class="reference internal" href="encoder.html#drug-encoding">Drug encoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="encoder.html#target-encoding">Target encoding</a></li>
<li class="toctree-l2"><a class="reference internal" href="encoder.html#encoder-model">Encoder Model</a></li>
<li class="toctree-l2"><a class="reference internal" href="encoder.html#technical-details">Technical Details</a><ul>
<li class="toctree-l3"><a class="reference internal" href="encoders/transformer.html">Transformer</a></li>
<li class="toctree-l3"><a class="reference internal" href="encoders/mpnn.html">Message Passing Neural Network (MPNN)</a></li>
<li class="toctree-l3"><a class="reference internal" href="encoders/cnnrnn.html">CNN+RNN</a></li>
<li class="toctree-l3"><a class="reference internal" href="encoders/cnn.html">CNN</a></li>
<li class="toctree-l3"><a class="reference internal" href="encoders/mlp.html">MLP</a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="process_data.html">Processing Data</a><ul>
<li class="toctree-l2"><a class="reference internal" href="process_data.html#drug-target-binding-benchmark-dataset">Drug-Target Binding Benchmark Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="process_data.html#repurposing-dataset">Repurposing Dataset</a></li>
<li class="toctree-l2"><a class="reference internal" href="process_data.html#bioassay-data-for-covid-19">Bioassay Data for COVID-19</a></li>
<li class="toctree-l2"><a class="reference internal" href="process_data.html#covid-19-targets">COVID-19 Targets</a><ul>
<li class="toctree-l3"><a class="reference internal" href="data/read_file_training_dataset_bioassay.html">read_file_training_dataset_bioassay</a></li>
<li class="toctree-l3"><a class="reference internal" href="data/read_file_training_dataset_drug_target_pairs.html">read_file_training_dataset_drug_target_pairs</a></li>
<li class="toctree-l3"><a class="reference internal" href="data/read_file_virtual_screening_drug_target_pairs.html">read_file_virtual_screening_drug_target_pairs</a></li>
<li class="toctree-l3"><a class="reference internal" href="data/read_file_repurposing_library.html">load bioarray dataset (read_file_training_dataset_bioassay)</a></li>
<li class="toctree-l3"><a class="reference internal" href="data/read_file_target_sequence.html">read_file_target_sequence</a></li>
<li class="toctree-l3"><a class="reference internal" href="data/download_BindingDB.html">download_DrugTargetCommons</a></li>
<li class="toctree-l3"><a class="reference internal" href="data/process_BindingDB.html">process_BindingDB</a></li>
<li class="toctree-l3"><a class="reference internal" href="data/load_process_DAVIS.html">load_process_DAVIS</a></li>
<li class="toctree-l3"><a class="reference internal" href="data/load_process_KIBA.html">load_process_KIBA</a></li>
<li class="toctree-l3"><a class="reference internal" href="data/load_AID1706_txt_file.html">load_AID1706_txt_file</a></li>
<li class="toctree-l3"><a class="reference internal" href="data/load_AID1706_SARS_CoV_3CL.html">load_AID1706_SARS_CoV_3CL</a></li>
<li class="toctree-l3"><a class="reference internal" href="data/load_antiviral_drugs.html">load_antiviral_drugs</a></li>
<li class="toctree-l3"><a class="reference internal" href="data/load_broad_repurposing_hub.html">load_broad_repurposing_hub</a></li>
</ul>
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<li class="toctree-l1"><a class="reference internal" href="configuration.html">Configuration</a></li>
<li class="toctree-l1"><a class="reference internal" href="utility_function.html">Utility Function</a></li>
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<div class="section" id="case-study">
<h1>Case Study<a class="headerlink" href="#case-study" title="Permalink to this headline">¶</a></h1>
<ul class="simple">
<li><p><strong>1a. Antiviral Drugs Repurposing for SARS-CoV2 3CLPro, using One Line.</strong></p></li>
</ul>
<p>Given a new target sequence (e.g. SARS-CoV2 3CL Protease),
retrieve a list of repurposing drugs from a curated drug library of 81 antiviral drugs.
The Binding Score is the Kd values.
Results aggregated from five pretrained model on BindingDB dataset!</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">DeepPurpose</span> <span class="kn">import</span> <span class="n">oneliner</span>
<span class="n">oneliner</span><span class="o">.</span><span class="n">repurpose</span><span class="p">(</span><span class="o">*</span><span class="n">load_SARS_CoV2_Protease_3CL</span><span class="p">(),</span> <span class="o">*</span><span class="n">load_antiviral_drugs</span><span class="p">())</span>
</pre></div>
</div>
<ul class="simple">
<li><p><strong>1b. New Target Repurposing using Broad Drug Repurposing Hub, with One Line.</strong></p></li>
</ul>
<p>Given a new target sequence (e.g. MMP9),
retrieve a list of repurposing drugs from Broad Drug Repurposing Hub,
which is the default.
Results also aggregated from five pretrained model!
Note the drug name here is the Pubchem CID since some drug names in Broad is too long.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">DeepPurpose</span> <span class="kn">import</span> <span class="n">oneliner</span>
<span class="n">oneliner</span><span class="o">.</span><span class="n">repurpose</span><span class="p">(</span><span class="o">*</span><span class="n">load_MMP9</span><span class="p">())</span>
</pre></div>
</div>
<ul class="simple">
<li><p><strong>2. Repurposing using Customized training data, with One Line.</strong></p></li>
</ul>
<p>Given a new target sequence (e.g. SARS-CoV 3CL Pro),
training on new data (AID1706 Bioassay),
and then retrieve a list of repurposing drugs from a proprietary library (e.g. antiviral drugs).
The model can be trained from scratch or finetuned from the pretraining checkpoint!</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">DeepPurpose</span> <span class="kn">import</span> <span class="n">oneliner</span>
<span class="kn">from</span> <span class="nn">DeepPurpose.dataset</span> <span class="kn">import</span> <span class="o">*</span>
<span class="n">oneliner</span><span class="o">.</span><span class="n">repurpose</span><span class="p">(</span><span class="o">*</span><span class="n">load_SARS_CoV_Protease_3CL</span><span class="p">(),</span> <span class="o">*</span><span class="n">load_antiviral_drugs</span><span class="p">(</span><span class="n">no_cid</span> <span class="o">=</span> <span class="kc">True</span><span class="p">),</span> <span class="o">*</span><span class="n">load_AID1706_SARS_CoV_3CL</span><span class="p">(),</span> \
<span class="n">split</span><span class="o">=</span><span class="s1">'HTS'</span><span class="p">,</span> <span class="n">convert_y</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">frac</span><span class="o">=</span><span class="p">[</span><span class="mf">0.8</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">],</span> <span class="n">pretrained</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">agg</span> <span class="o">=</span> <span class="s1">'max_effect'</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><ol class="arabic simple" start="3">
<li><p><strong>A Framework for Drug Target Interaction Prediction, with less than 10 lines of codes.</strong></p></li>
</ol>
</li>
</ul>
<p>Under the hood of one model from scratch, a flexible framework for method researchers:</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">DeepPurpose</span> <span class="kn">import</span> <span class="n">models</span>
<span class="kn">from</span> <span class="nn">DeepPurpose.utils</span> <span class="kn">import</span> <span class="o">*</span>
<span class="kn">from</span> <span class="nn">DeepPurpose.dataset</span> <span class="kn">import</span> <span class="o">*</span>
<span class="c1"># Load Data, an array of SMILES for drug,</span>
<span class="c1"># an array of Amino Acid Sequence for Target</span>
<span class="c1"># and an array of binding values/0-1 label.</span>
<span class="c1"># e.g. ['Cc1ccc(CNS(=O)(=O)c2ccc(s2)S(N)(=O)=O)cc1', ...],</span>
<span class="c1"># ['MSHHWGYGKHNGPEHWHKDFPIAKGERQSPVDIDTH...', ...],</span>
<span class="c1"># [0.46, 0.49, ...]</span>
<span class="c1"># In this example, BindingDB with Kd binding score is used.</span>
<span class="n">X_drug</span><span class="p">,</span> <span class="n">X_target</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">process_BindingDB</span><span class="p">(</span><span class="n">download_BindingDB</span><span class="p">(</span><span class="n">SAVE_PATH</span><span class="p">),</span>
<span class="n">y</span> <span class="o">=</span> <span class="s1">'Kd'</span><span class="p">,</span>
<span class="n">binary</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span>
<span class="n">convert_to_log</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
<span class="c1"># Type in the encoding names for drug/protein.</span>
<span class="n">drug_encoding</span><span class="p">,</span> <span class="n">target_encoding</span> <span class="o">=</span> <span class="s1">'MPNN'</span><span class="p">,</span> <span class="s1">'Transformer'</span>
<span class="c1"># Data processing, here we select cold protein split setup.</span>
<span class="n">train</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="n">data_process</span><span class="p">(</span><span class="n">X_drug</span><span class="p">,</span> <span class="n">X_target</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span>
<span class="n">drug_encoding</span><span class="p">,</span> <span class="n">target_encoding</span><span class="p">,</span>
<span class="n">split_method</span><span class="o">=</span><span class="s1">'cold_protein'</span><span class="p">,</span>
<span class="n">frac</span><span class="o">=</span><span class="p">[</span><span class="mf">0.7</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.2</span><span class="p">])</span>
<span class="c1"># Generate new model using default parameters;</span>
<span class="c1"># also allow model tuning via input parameters.</span>
<span class="n">config</span> <span class="o">=</span> <span class="n">generate_config</span><span class="p">(</span><span class="n">drug_encoding</span><span class="p">,</span> <span class="n">target_encoding</span><span class="p">,</span> \
<span class="n">transformer_n_layer_target</span> <span class="o">=</span> <span class="mi">8</span><span class="p">)</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">model_initialize</span><span class="p">(</span><span class="o">**</span><span class="n">config</span><span class="p">)</span>
<span class="c1"># Train the new model.</span>
<span class="c1"># Detailed output including a tidy table storing</span>
<span class="c1"># validation loss, metrics, AUC curves figures and etc.</span>
<span class="c1"># are stored in the ./result folder.</span>
<span class="n">net</span><span class="o">.</span><span class="n">train</span><span class="p">(</span><span class="n">train</span><span class="p">,</span> <span class="n">val</span><span class="p">,</span> <span class="n">test</span><span class="p">)</span>
<span class="c1"># or simply load pretrained model from a model directory path</span>
<span class="c1"># or reproduced model name such as DeepDTA</span>
<span class="n">net</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">model_pretrained</span><span class="p">(</span><span class="n">MODEL_PATH_DIR</span> <span class="ow">or</span> <span class="n">MODEL_NAME</span><span class="p">)</span>
<span class="c1"># Repurpose using the trained model or pre-trained model</span>
<span class="c1"># In this example, loading repurposing dataset using</span>
<span class="c1"># Broad Repurposing Hub and SARS-CoV 3CL Protease Target.</span>
<span class="n">X_repurpose</span><span class="p">,</span> <span class="n">drug_name</span><span class="p">,</span> <span class="n">drug_cid</span> <span class="o">=</span> <span class="n">load_broad_repurposing_hub</span><span class="p">(</span><span class="n">SAVE_PATH</span><span class="p">)</span>
<span class="n">target</span><span class="p">,</span> <span class="n">target_name</span> <span class="o">=</span> <span class="n">load_SARS_CoV_Protease_3CL</span><span class="p">()</span>
<span class="n">_</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">repurpose</span><span class="p">(</span><span class="n">X_repurpose</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">net</span><span class="p">,</span> <span class="n">drug_name</span><span class="p">,</span> <span class="n">target_name</span><span class="p">)</span>
<span class="c1"># Virtual screening using the trained model or pre-trained model</span>
<span class="n">X_repurpose</span><span class="p">,</span> <span class="n">drug_name</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">target_name</span> <span class="o">=</span> \
<span class="p">[</span><span class="s1">'CCCCCCCOc1cccc(c1)C([O-])=O'</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="p">[</span><span class="s1">'16007391'</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> \
<span class="p">[</span><span class="s1">'MLARRKPVLPALTINPTIAEGPSPTSEGASEANLVDLQKKLEEL...'</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>\
<span class="p">[</span><span class="s1">'P36896'</span><span class="p">,</span> <span class="s1">'P00374'</span><span class="p">]</span>
<span class="n">_</span> <span class="o">=</span> <span class="n">models</span><span class="o">.</span><span class="n">virtual_screening</span><span class="p">(</span><span class="n">X_repurpose</span><span class="p">,</span> <span class="n">target</span><span class="p">,</span> <span class="n">net</span><span class="p">,</span> <span class="n">drug_name</span><span class="p">,</span> <span class="n">target_name</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><ol class="arabic simple" start="4">
<li><p><strong>Virtual Screening with Customized Training Data with One Line</strong></p></li>
</ol>
</li>
</ul>
<p>Given a list of new drug-target pairs to be screened,
retrieve a list of drug-target pairs with top predicted binding scores.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">DeepPurpose</span> <span class="kn">import</span> <span class="n">oneliner</span>
<span class="n">oneliner</span><span class="o">.</span><span class="n">virtual_screening</span><span class="p">([</span><span class="s1">'MKK...LIDL'</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="p">[</span><span class="s1">'CC1=C...C4)N'</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span>
</pre></div>
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