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<p class="caption"><span class="caption-text">Importance Function</span></p>
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<div class="section" id="drug-target-binding-affinity-dtba-model">
<h1>Drug Target Binding Affinity (DTBA) Model<a class="headerlink" href="#drug-target-binding-affinity-dtba-model" title="Permalink to this headline">¶</a></h1>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="k">class</span> <span class="nc">DeepPurpose</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">DBTA</span>
</pre></div>
</div>
<p><strong>Drug Target Binding Affinity (DBTA)</strong> (<a class="reference external" href="https://github.com/kexinhuang12345/DeepPurpose/blob/master/DeepPurpose/models.py#L509">Source</a>) include all component, including drug encoder, target encoder and classifier/regressor.</p>
<p><strong>constructor</strong> create an instance of DBTA.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">config</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><dl class="simple">
<dt><strong>config</strong> (kwargs, keyword arguments) - specify the parameter of DBTA.</dt><dd><ul>
<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>result_folder</strong> (str) - directory that store the learning log/results.</p></li>
<li><p><strong>concrete parameter for encoder model</strong> (repeated)</p></li>
</ul>
</dd>
</dl>
</li>
</ul>
<p><strong>test_</strong> include all the test procedure.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">test_</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_generator</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">repurposing_mode</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">test</span> <span class="o">=</span> <span class="kc">False</span><span class="p">):</span>
</pre></div>
</div>
<ul class="simple">
<li><p><strong>data_generator</strong> (iterator) - iterator of torch.utils.data.DataLoader. It can be test data or validation data.</p></li>
<li><p><strong>model</strong> (DeepPurpose.models.Classifier) - model of DBTA.</p></li>
<li><p><strong>repurposing_mode</strong> (bool) - If repurposing_mode is True, then do repurposing. Otherwise, do compute the accuracy (including AUC score).</p></li>
<li><p><strong>test</strong> (bool) - If test is True, plot ROC-AUC and PR-AUC curve. Otherwise, pass.</p></li>
</ul>
<p><strong>train</strong> include all the training procedure.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">train</span><span class="p">(</span><span class="bp">self</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="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">verbose</span> <span class="o">=</span> <span class="kc">True</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><p><strong>train</strong> (torch.utils.data.dataloader) - Train data loader</p></li>
<li><p><strong>val</strong> (torch.utils.data.dataloader) - Valid data loader</p></li>
<li><p><strong>test</strong> (torch.utils.data.dataloader) - Test data loader</p></li>
<li><p><strong>verbose</strong> (bool) - If verbose is True, then print training record every 100 iterations.</p></li>
</ul>
<p><strong>predict</strong> include all the inference procedure.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">predict</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">df_data</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><p><strong>df_data</strong> (pd.DataFrame) - specify data that we need to predict.</p></li>
</ul>
<p><strong>save_model</strong> save the well-trained model to specific directory.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">save_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path_dir</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><p><strong>path_dir</strong> (str, a directory) - the path where model is saved.</p></li>
</ul>
<p><strong>load_pretrained</strong> load the well-trained model so that we are able to make inference directly and don’t have to train model from scratch.</p>
<div class="highlight-python notranslate"><div class="highlight"><pre><span></span><span class="n">load_pretrained</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path</span><span class="p">)</span>
</pre></div>
</div>
<ul class="simple">
<li><p><strong>path</strong> (str, a directory) - the path where model is loaded.</p></li>
</ul>
</div>
</div>
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