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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> +</li> +<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> +</ul> +</li> +<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> +</li> +<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> +</ul> +<p class="caption"><span class="caption-text">Importance Function</span></p> +<ul class="current"> +<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> +</li> +</ul> +</li> +<li class="toctree-l1 current"><a class="current reference internal" href="#">Configuration</a></li> +<li class="toctree-l1"><a class="reference internal" href="utility_function.html">Utility Function</a></li> +</ul> + + + + </div> + </div> + </nav> + + <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"> + + + <nav class="wy-nav-top" aria-label="top navigation"> + + <i data-toggle="wy-nav-top" class="fa fa-bars"></i> + <a href="../index.html">DeepPurpose</a> + + </nav> + + + <div class="wy-nav-content"> + + <div class="rst-content"> + + + + + + + + + + + + + + + + + +<div role="navigation" aria-label="breadcrumbs navigation"> + + <ul class="wy-breadcrumbs"> + + <li><a href="../index.html">Docs</a> »</li> + + <li>Configuration</li> + + + <li class="wy-breadcrumbs-aside"> + + + <a href="../_sources/notes/configuration.rst.txt" rel="nofollow"> View page source</a> + + + </li> + + </ul> + + + <hr/> +</div> + <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article"> + <div itemprop="articleBody"> + + <div class="section" id="configuration"> +<h1>Configuration<a class="headerlink" href="#configuration" title="Permalink to this headline">¶</a></h1> +<p><strong>generate_config</strong> generate all the configuration that can be used in learning and inference.</p> +<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">utils</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">result_folder</span> <span class="o">=</span> <span class="s2">"./result/"</span><span class="p">,</span> + <span class="n">input_dim_drug</span> <span class="o">=</span> <span class="mi">1024</span><span class="p">,</span> + <span class="n">input_dim_protein</span> <span class="o">=</span> <span class="mi">8420</span><span class="p">,</span> + <span class="n">hidden_dim_drug</span> <span class="o">=</span> <span class="mi">256</span><span class="p">,</span> + <span class="n">hidden_dim_protein</span> <span class="o">=</span> <span class="mi">256</span><span class="p">,</span> + <span class="n">cls_hidden_dims</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1024</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="n">mlp_hidden_dims_drug</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span> + <span class="n">mlp_hidden_dims_target</span> <span class="o">=</span> <span class="p">[</span><span class="mi">1024</span><span class="p">,</span> <span class="mi">256</span><span class="p">,</span> <span class="mi">64</span><span class="p">],</span> + <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">256</span><span class="p">,</span> + <span class="n">train_epoch</span> <span class="o">=</span> <span class="mi">10</span><span class="p">,</span> + <span class="n">test_every_X_epoch</span> <span class="o">=</span> <span class="mi">20</span><span class="p">,</span> + <span class="n">LR</span> <span class="o">=</span> <span class="mf">1e-4</span><span class="p">,</span> + <span class="n">transformer_emb_size_drug</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span> + <span class="n">transformer_intermediate_size_drug</span> <span class="o">=</span> <span class="mi">512</span><span class="p">,</span> + <span class="n">transformer_num_attention_heads_drug</span> <span class="o">=</span> <span class="mi">8</span><span class="p">,</span> + <span class="n">transformer_n_layer_drug</span> <span class="o">=</span> <span class="mi">8</span><span class="p">,</span> + <span class="n">transformer_emb_size_target</span> <span class="o">=</span> <span class="mi">128</span><span class="p">,</span> + <span class="n">transformer_intermediate_size_target</span> <span class="o">=</span> <span class="mi">512</span><span class="p">,</span> + <span class="n">transformer_num_attention_heads_target</span> <span class="o">=</span> <span class="mi">8</span><span class="p">,</span> + <span class="n">transformer_n_layer_target</span> <span class="o">=</span> <span class="mi">4</span><span class="p">,</span> + <span class="n">transformer_dropout_rate</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span> + <span class="n">transformer_attention_probs_dropout</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span> + <span class="n">transformer_hidden_dropout_rate</span> <span class="o">=</span> <span class="mf">0.1</span><span class="p">,</span> + <span class="n">mpnn_hidden_size</span> <span class="o">=</span> <span class="mi">50</span><span class="p">,</span> + <span class="n">mpnn_depth</span> <span class="o">=</span> <span class="mi">3</span><span class="p">,</span> + <span class="n">cnn_drug_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">32</span><span class="p">,</span><span class="mi">64</span><span class="p">,</span><span class="mi">96</span><span class="p">],</span> + <span class="n">cnn_drug_kernels</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">6</span><span class="p">,</span><span class="mi">8</span><span class="p">],</span> + <span class="n">cnn_target_filters</span> <span class="o">=</span> <span class="p">[</span><span class="mi">32</span><span class="p">,</span><span class="mi">64</span><span class="p">,</span><span class="mi">96</span><span class="p">],</span> + <span class="n">cnn_target_kernels</span> <span class="o">=</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span><span class="mi">8</span><span class="p">,</span><span class="mi">12</span><span class="p">],</span> + <span class="n">rnn_Use_GRU_LSTM_drug</span> <span class="o">=</span> <span class="s1">'GRU'</span><span class="p">,</span> + <span class="n">rnn_drug_hid_dim</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> + <span class="n">rnn_drug_n_layers</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> + <span class="n">rnn_drug_bidirectional</span> <span class="o">=</span> <span class="kc">True</span><span class="p">,</span> + <span class="n">rnn_Use_GRU_LSTM_target</span> <span class="o">=</span> <span class="s1">'GRU'</span><span class="p">,</span> + <span class="n">rnn_target_hid_dim</span> <span class="o">=</span> <span class="mi">64</span><span class="p">,</span> + <span class="n">rnn_target_n_layers</span> <span class="o">=</span> <span class="mi">2</span><span class="p">,</span> + <span class="n">rnn_target_bidirectional</span> <span class="o">=</span> <span class="kc">True</span> + <span class="p">)</span> +</pre></div> +</div> +<ul class="simple"> +<li><p><strong>drug_encoding</strong> (str) - Encoder mode for drug. It can be “transformer”, “MPNN”, “CNN”, “CNN_RNN” …,</p></li> +<li><p><strong>target_encoding</strong> (str) - Encoder mode for protein. It can be “transformer”, “CNN”, “CNN_RNN” …,</p></li> +<li><p><strong>input_dim_drug</strong> (int) - Dimension of input drug feature.</p></li> +<li><p><strong>input_dim_protein</strong> (int) - Dimension of input protein feature.</p></li> +<li><p><strong>hidden_dim_drug</strong> (int) - Dimension of hidden layer of drug feature.</p></li> +<li><p><strong>hidden_dim_protein</strong> (int) - Dimension of hidden layer of protein feature.</p></li> +<li><p><strong>batch_size</strong> (int) - batch size</p></li> +<li><p><strong>train_epoch</strong> (int) - training epoch</p></li> +<li><p><strong>test_every_X_epoch</strong> (int) - test every X epochs</p></li> +<li><p><strong>LR</strong> (float) - Learning rate.</p></li> +<li><p><strong>cls_hidden_dims</strong> (list of int) - hidden dimensions of classifier.</p></li> +<li><p><strong>mlp_hidden_dims_drug</strong> (list of int) - hidden dimension of MLP when encoding drug.</p></li> +<li><p><strong>mlp_hidden_dims_target</strong> (list of int) - hidden dimension of MLP when encoding protein.</p></li> +<li><p><strong>transformer_emb_size_drug</strong> (int) - embedding size of transformer when encoding drug.</p></li> +<li><p><strong>transformer_intermediate_size_drug</strong> (int) -</p></li> +<li><p><strong>transformer_num_attention_heads_drug</strong> (int) -</p></li> +<li><p><strong>transformer_n_layer_drug</strong> (int) -</p></li> +<li><p><strong>transformer_emb_size_target</strong> (int) -</p></li> +<li><p><strong>transformer_intermediate_size_target</strong> (int) -</p></li> +<li><p><strong>transformer_num_attention_heads_target</strong> (int) -</p></li> +<li><p><strong>transformer_n_layer_target</strong> (int) -</p></li> +<li><p><strong>transformer_dropout_rate</strong> (float) -</p></li> +</ul> +</div> + + + </div> + + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="utility_function.html" class="btn btn-neutral float-right" title="Utility Function" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a> + + + <a href="process_data.html" class="btn btn-neutral float-left" title="Processing Data" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a> + + </div> + + + <hr/> + + <div role="contentinfo"> + <p> + © Copyright 2020, Kexin Huang, Tianfan Fu + + </p> + </div> + Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a> provided by <a href="https://readthedocs.org">Read the Docs</a>. + +</footer> + + </div> + </div> + + </section> + + </div> + + + + <script type="text/javascript"> + jQuery(function () { + SphinxRtdTheme.Navigation.enable(true); + }); + </script> + + + + + + +</body> +</html> \ No newline at end of file