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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> +</ul> +</li> +</ul> +<p class="caption"><span class="caption-text">How to run</span></p> +<ul class="current"> +<li class="toctree-l1"><a class="reference internal" href="download.html">Download Code & Install</a><ul> +<li class="toctree-l2"><a class="reference internal" href="download.html#download-code">Download Code</a></li> +<li class="toctree-l2"><a class="reference internal" href="download.html#first-time-usage-setup-conda-environment">First time usage: setup conda environment</a></li> +<li class="toctree-l2"><a class="reference internal" href="download.html#second-time-and-later">Second time and later</a></li> +</ul> +</li> +<li class="toctree-l1 current"><a class="current reference internal" href="#">Case Study</a></li> +</ul> +<p class="caption"><span class="caption-text">Package Reference</span></p> +<ul> +<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> +</ul> +</li> +<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> +</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> +<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"><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> +</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>Case Study</li> + + + <li class="wy-breadcrumbs-aside"> + + + <a href="../_sources/notes/casestudy.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="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> +</div> +</div> + + + </div> + + </div> + <footer> + + <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation"> + + <a href="models.html" class="btn btn-neutral float-right" title="DeepPurpose.models" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a> + + + <a href="download.html" class="btn btn-neutral float-left" title="Download Code & Install" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left"></span> Previous</a> + + </div> + + + <hr/> + + <div role="contentinfo"> + <p> + © 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