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diff --git a/docs/_build/html/.buildinfo b/docs/_build/html/.buildinfo
new file mode 100644
index 0000000000000000000000000000000000000000..454bfd3b34ffddcb371d1d68566bb1865f86152b
--- /dev/null
+++ b/docs/_build/html/.buildinfo
@@ -0,0 +1,4 @@
+# Sphinx build info version 1
+# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
+config: 18efb40e970fb2c9b3465eeb13d835bc
+tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..f6754d9593a08b02cf9af8205533962ea6cab6d8
--- /dev/null
+++ b/docs/_build/html/_sources/index.rst.txt
@@ -0,0 +1,18 @@
+.. PROTAC-Degradation-Predictor documentation master file, created by
+   sphinx-quickstart on Mon Aug 19 11:16:51 2024.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+PROTAC-Degradation-Predictor documentation
+==========================================
+
+Add your content using ``reStructuredText`` syntax. See the
+`reStructuredText <https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html>`_
+documentation for details.
+
+
+.. toctree::
+   :maxdepth: 4
+   :caption: Contents:
+
+   source/modules
\ No newline at end of file
diff --git a/docs/_build/html/_sources/source/modules.rst.txt b/docs/_build/html/_sources/source/modules.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..cac44ec69b6a88537a027c4b3b28c1c4741c3b3d
--- /dev/null
+++ b/docs/_build/html/_sources/source/modules.rst.txt
@@ -0,0 +1,7 @@
+protac_degradation_predictor
+============================
+
+.. toctree::
+   :maxdepth: 4
+
+   protac_degradation_predictor
diff --git a/docs/_build/html/_sources/source/protac_degradation_predictor.optuna.rst.txt b/docs/_build/html/_sources/source/protac_degradation_predictor.optuna.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..44e702e0ecad724cf16d55ffcda92bb3d03b4c38
--- /dev/null
+++ b/docs/_build/html/_sources/source/protac_degradation_predictor.optuna.rst.txt
@@ -0,0 +1,45 @@
+protac\_degradation\_predictor.optuna package
+=============================================
+
+Submodules
+----------
+
+protac\_degradation\_predictor.optuna.pytorch\_models module
+------------------------------------------------------------
+
+.. automodule:: protac_degradation_predictor.optuna.pytorch_models
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+protac\_degradation\_predictor.optuna.sklearn\_models module
+------------------------------------------------------------
+
+.. automodule:: protac_degradation_predictor.optuna.sklearn_models
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+protac\_degradation\_predictor.optuna.utils module
+--------------------------------------------------
+
+.. automodule:: protac_degradation_predictor.optuna.utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+protac\_degradation\_predictor.optuna.xgboost\_models module
+------------------------------------------------------------
+
+.. automodule:: protac_degradation_predictor.optuna.xgboost_models
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: protac_degradation_predictor.optuna
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/_build/html/_sources/source/protac_degradation_predictor.rst.txt b/docs/_build/html/_sources/source/protac_degradation_predictor.rst.txt
new file mode 100644
index 0000000000000000000000000000000000000000..c33a7d8875beafb9be7903ad95b444824660f9cf
--- /dev/null
+++ b/docs/_build/html/_sources/source/protac_degradation_predictor.rst.txt
@@ -0,0 +1,85 @@
+protac\_degradation\_predictor package
+======================================
+
+Subpackages
+-----------
+
+.. toctree::
+   :maxdepth: 4
+
+   protac_degradation_predictor.optuna
+
+Submodules
+----------
+
+protac\_degradation\_predictor.config module
+--------------------------------------------
+
+.. automodule:: protac_degradation_predictor.config
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+protac\_degradation\_predictor.data\_utils module
+-------------------------------------------------
+
+.. automodule:: protac_degradation_predictor.data_utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+protac\_degradation\_predictor.optuna\_utils module
+---------------------------------------------------
+
+.. automodule:: protac_degradation_predictor.optuna_utils
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+protac\_degradation\_predictor.optuna\_utils\_xgboost module
+------------------------------------------------------------
+
+.. automodule:: protac_degradation_predictor.optuna_utils_xgboost
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+protac\_degradation\_predictor.protac\_dataset module
+-----------------------------------------------------
+
+.. automodule:: protac_degradation_predictor.protac_dataset
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+protac\_degradation\_predictor.protac\_degradation\_predictor module
+--------------------------------------------------------------------
+
+.. automodule:: protac_degradation_predictor.protac_degradation_predictor
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+protac\_degradation\_predictor.pytorch\_models module
+-----------------------------------------------------
+
+.. automodule:: protac_degradation_predictor.pytorch_models
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+protac\_degradation\_predictor.sklearn\_models module
+-----------------------------------------------------
+
+.. automodule:: protac_degradation_predictor.sklearn_models
+   :members:
+   :undoc-members:
+   :show-inheritance:
+
+Module contents
+---------------
+
+.. automodule:: protac_degradation_predictor
+   :members:
+   :undoc-members:
+   :show-inheritance:
diff --git a/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js b/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
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--- /dev/null
+++ b/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,123 @@
+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
+    }
+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
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+};
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+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+    function highlight(node, addItems) {
+        if (node.nodeType === 3) {
+            var val = node.nodeValue;
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+    for (var i = 0; i < addItems.length; ++i) {
+        jQuery(addItems[i].parent).before(addItems[i].target);
+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
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+            [];
+
+        return {
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+        };
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+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
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new file mode 100644
index 0000000000000000000000000000000000000000..f316efcb47bde67762d472130ad86676339ffa7e
--- /dev/null
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@@ -0,0 +1,925 @@
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+ * ~~~~~~~~~
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+ * Sphinx stylesheet -- basic theme.
+ *
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+
+div.section::after {
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+    font-size: 90%;
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+    border: 0;
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+    padding: 0;
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+    border-bottom: 1px solid #ddd;
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+    margin: 0;
+    padding-left: 1em;
+}
+
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+    margin: 0;
+}
+
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+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
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+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
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+    background-color: transparent;
+    font-weight: bold;
+}
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+	font-size: 1.1em;
+}
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+    font-size: 1.2em;
+}
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+    background-color: transparent;
+}
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+}
+
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+    font-size: larger;
+}
+
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+	font-style: italic;
+}
+
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+
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+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
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+    list-style: upper-roman;
+}
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+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
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+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
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+ul.simple ol p,
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+    word-break: break-word;
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+    padding-right: 5px;
+}
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+    padding-left: 0.5em;
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+    margin-left: 0em;
+    margin-bottom: 0em;
+}
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+    margin-bottom: 15px;
+}
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+    margin-top: 0px;
+}
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+    margin-bottom: 10px;
+}
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+    margin-bottom: 10px;
+    margin-left: 30px;
+}
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+}
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+}
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+}
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+}
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+    margin-top: 1em;
+    margin-bottom: 1em;
+}
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+    margin-bottom: 0;
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+    content: ":";
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+}
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+    cursor: help;
+}
+
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+    background-color: rgba(207, 255, 207, 0.2)
+}
+
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+    background-color: rgba(255, 207, 207, 0.2)
+}
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+/* -- code displays --------------------------------------------------------- */
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+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
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+    clear: both;
+}
+
+span.pre {
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+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
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+}
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+}
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+}
+
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+}
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+}
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+}
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+}
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+}
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+}
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+}
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+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
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+}
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+}
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+
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+}
+
+div.body div.math p {
+    text-align: center;
+}
+
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+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
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+}
+
+/* -- printout stylesheet --------------------------------------------------- */
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+@media print {
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diff --git a/docs/_build/html/_static/doctools.js b/docs/_build/html/_static/doctools.js
new file mode 100644
index 0000000000000000000000000000000000000000..4d67807d17d27b16d4ac115dc383523157635f93
--- /dev/null
+++ b/docs/_build/html/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+  "TEXTAREA",
+  "INPUT",
+  "SELECT",
+  "BUTTON",
+]);
+
+const _ready = (callback) => {
+  if (document.readyState !== "loading") {
+    callback();
+  } else {
+    document.addEventListener("DOMContentLoaded", callback);
+  }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+  init: () => {
+    Documentation.initDomainIndexTable();
+    Documentation.initOnKeyListeners();
+  },
+
+  /**
+   * i18n support
+   */
+  TRANSLATIONS: {},
+  PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+  LOCALE: "unknown",
+
+  // gettext and ngettext don't access this so that the functions
+  // can safely bound to a different name (_ = Documentation.gettext)
+  gettext: (string) => {
+    const translated = Documentation.TRANSLATIONS[string];
+    switch (typeof translated) {
+      case "undefined":
+        return string; // no translation
+      case "string":
+        return translated; // translation exists
+      default:
+        return translated[0]; // (singular, plural) translation tuple exists
+    }
+  },
+
+  ngettext: (singular, plural, n) => {
+    const translated = Documentation.TRANSLATIONS[singular];
+    if (typeof translated !== "undefined")
+      return translated[Documentation.PLURAL_EXPR(n)];
+    return n === 1 ? singular : plural;
+  },
+
+  addTranslations: (catalog) => {
+    Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+    Documentation.PLURAL_EXPR = new Function(
+      "n",
+      `return (${catalog.plural_expr})`
+    );
+    Documentation.LOCALE = catalog.locale;
+  },
+
+  /**
+   * helper function to focus on search bar
+   */
+  focusSearchBar: () => {
+    document.querySelectorAll("input[name=q]")[0]?.focus();
+  },
+
+  /**
+   * Initialise the domain index toggle buttons
+   */
+  initDomainIndexTable: () => {
+    const toggler = (el) => {
+      const idNumber = el.id.substr(7);
+      const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+      if (el.src.substr(-9) === "minus.png") {
+        el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+        toggledRows.forEach((el) => (el.style.display = "none"));
+      } else {
+        el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+        toggledRows.forEach((el) => (el.style.display = ""));
+      }
+    };
+
+    const togglerElements = document.querySelectorAll("img.toggler");
+    togglerElements.forEach((el) =>
+      el.addEventListener("click", (event) => toggler(event.currentTarget))
+    );
+    togglerElements.forEach((el) => (el.style.display = ""));
+    if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+  },
+
+  initOnKeyListeners: () => {
+    // only install a listener if it is really needed
+    if (
+      !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+      !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+    )
+      return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+      if (!event.shiftKey) {
+        switch (event.key) {
+          case "ArrowLeft":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const prevLink = document.querySelector('link[rel="prev"]');
+            if (prevLink && prevLink.href) {
+              window.location.href = prevLink.href;
+              event.preventDefault();
+            }
+            break;
+          case "ArrowRight":
+            if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+            const nextLink = document.querySelector('link[rel="next"]');
+            if (nextLink && nextLink.href) {
+              window.location.href = nextLink.href;
+              event.preventDefault();
+            }
+            break;
+        }
+      }
+
+      // some keyboard layouts may need Shift to get /
+      switch (event.key) {
+        case "/":
+          if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+          Documentation.focusSearchBar();
+          event.preventDefault();
+      }
+    });
+  },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/docs/_build/html/_static/documentation_options.js b/docs/_build/html/_static/documentation_options.js
new file mode 100644
index 0000000000000000000000000000000000000000..20f85f65c7c028c5e273828430caa2cff57d60cc
--- /dev/null
+++ b/docs/_build/html/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: 'v1.0.1',
+    LANGUAGE: 'en',
+    COLLAPSE_INDEX: false,
+    BUILDER: 'html',
+    FILE_SUFFIX: '.html',
+    LINK_SUFFIX: '.html',
+    HAS_SOURCE: true,
+    SOURCELINK_SUFFIX: '.txt',
+    NAVIGATION_WITH_KEYS: false,
+    SHOW_SEARCH_SUMMARY: true,
+    ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
diff --git a/docs/_build/html/_static/file.png b/docs/_build/html/_static/file.png
new file mode 100644
index 0000000000000000000000000000000000000000..a858a410e4faa62ce324d814e4b816fff83a6fb3
Binary files /dev/null and b/docs/_build/html/_static/file.png differ
diff --git a/docs/_build/html/_static/jquery.js b/docs/_build/html/_static/jquery.js
new file mode 100644
index 0000000000000000000000000000000000000000..c4c6022f2982e8dae64cebd6b9a2b59f2547faad
--- /dev/null
+++ b/docs/_build/html/_static/jquery.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
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[id="'+n.substring(1)+'"]').closest("div.section");0===(t=e.find('[href="#'+i.attr("id")+'"]')).length&&(t=e.find('[href="#"]'))}if(t.length>0){$(".wy-menu-vertical .current").removeClass("current").attr("aria-expanded","false"),t.addClass("current").attr("aria-expanded","true"),t.closest("li.toctree-l1").parent().addClass("current").attr("aria-expanded","true");for(let n=1;n<=10;n++)t.closest("li.toctree-l"+n).addClass("current").attr("aria-expanded","true");t[0].scrollIntoView()}}catch(n){console.log("Error expanding nav for anchor",n)}},onScroll:function(){this.winScroll=!1;var n=this.win.scrollTop(),e=n+this.winHeight,t=this.navBar.scrollTop()+(n-this.winPosition);n<0||e>this.docHeight||(this.navBar.scrollTop(t),this.winPosition=n)},onResize:function(){this.winResize=!1,this.winHeight=this.win.height(),this.docHeight=$(document).height()},hashChange:function(){this.linkScroll=!0,this.win.one("hashchange",(function(){this.linkScroll=!1}))},toggleCurrent:function(n){var e=n.closest("li");e.siblings("li.current").removeClass("current").attr("aria-expanded","false"),e.siblings().find("li.current").removeClass("current").attr("aria-expanded","false");var t=e.find("> ul li");t.length&&(t.removeClass("current").attr("aria-expanded","false"),e.toggleClass("current").attr("aria-expanded",(function(n,e){return"true"==e?"false":"true"})))}},"undefined"!=typeof window&&(window.SphinxRtdTheme={Navigation:n.exports.ThemeNav,StickyNav:n.exports.ThemeNav}),function(){for(var n=0,e=["ms","moz","webkit","o"],t=0;t<e.length&&!window.requestAnimationFrame;++t)window.requestAnimationFrame=window[e[t]+"RequestAnimationFrame"],window.cancelAnimationFrame=window[e[t]+"CancelAnimationFrame"]||window[e[t]+"CancelRequestAnimationFrame"];window.requestAnimationFrame||(window.requestAnimationFrame=function(e,t){var i=(new Date).getTime(),o=Math.max(0,16-(i-n)),r=window.setTimeout((function(){e(i+o)}),o);return 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\ No newline at end of file
diff --git a/docs/_build/html/_static/language_data.js b/docs/_build/html/_static/language_data.js
new file mode 100644
index 0000000000000000000000000000000000000000..367b8ed81b4e411bee6f81208dba76fbe37e0e15
--- /dev/null
+++ b/docs/_build/html/_static/language_data.js
@@ -0,0 +1,199 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
+
+
+/* Non-minified version is copied as a separate JS file, if available */
+
+/**
+ * Porter Stemmer
+ */
+var Stemmer = function() {
+
+  var step2list = {
+    ational: 'ate',
+    tional: 'tion',
+    enci: 'ence',
+    anci: 'ance',
+    izer: 'ize',
+    bli: 'ble',
+    alli: 'al',
+    entli: 'ent',
+    eli: 'e',
+    ousli: 'ous',
+    ization: 'ize',
+    ation: 'ate',
+    ator: 'ate',
+    alism: 'al',
+    iveness: 'ive',
+    fulness: 'ful',
+    ousness: 'ous',
+    aliti: 'al',
+    iviti: 'ive',
+    biliti: 'ble',
+    logi: 'log'
+  };
+
+  var step3list = {
+    icate: 'ic',
+    ative: '',
+    alize: 'al',
+    iciti: 'ic',
+    ical: 'ic',
+    ful: '',
+    ness: ''
+  };
+
+  var c = "[^aeiou]";          // consonant
+  var v = "[aeiouy]";          // vowel
+  var C = c + "[^aeiouy]*";    // consonant sequence
+  var V = v + "[aeiou]*";      // vowel sequence
+
+  var mgr0 = "^(" + C + ")?" + V + C;                      // [C]VC... is m>0
+  var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$";    // [C]VC[V] is m=1
+  var mgr1 = "^(" + C + ")?" + V + C + V + C;              // [C]VCVC... is m>1
+  var s_v   = "^(" + C + ")?" + v;                         // vowel in stem
+
+  this.stemWord = function (w) {
+    var stem;
+    var suffix;
+    var firstch;
+    var origword = w;
+
+    if (w.length < 3)
+      return w;
+
+    var re;
+    var re2;
+    var re3;
+    var re4;
+
+    firstch = w.substr(0,1);
+    if (firstch == "y")
+      w = firstch.toUpperCase() + w.substr(1);
+
+    // Step 1a
+    re = /^(.+?)(ss|i)es$/;
+    re2 = /^(.+?)([^s])s$/;
+
+    if (re.test(w))
+      w = w.replace(re,"$1$2");
+    else if (re2.test(w))
+      w = w.replace(re2,"$1$2");
+
+    // Step 1b
+    re = /^(.+?)eed$/;
+    re2 = /^(.+?)(ed|ing)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      re = new RegExp(mgr0);
+      if (re.test(fp[1])) {
+        re = /.$/;
+        w = w.replace(re,"");
+      }
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1];
+      re2 = new RegExp(s_v);
+      if (re2.test(stem)) {
+        w = stem;
+        re2 = /(at|bl|iz)$/;
+        re3 = new RegExp("([^aeiouylsz])\\1$");
+        re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+        if (re2.test(w))
+          w = w + "e";
+        else if (re3.test(w)) {
+          re = /.$/;
+          w = w.replace(re,"");
+        }
+        else if (re4.test(w))
+          w = w + "e";
+      }
+    }
+
+    // Step 1c
+    re = /^(.+?)y$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(s_v);
+      if (re.test(stem))
+        w = stem + "i";
+    }
+
+    // Step 2
+    re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step2list[suffix];
+    }
+
+    // Step 3
+    re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      suffix = fp[2];
+      re = new RegExp(mgr0);
+      if (re.test(stem))
+        w = stem + step3list[suffix];
+    }
+
+    // Step 4
+    re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+    re2 = /^(.+?)(s|t)(ion)$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      if (re.test(stem))
+        w = stem;
+    }
+    else if (re2.test(w)) {
+      var fp = re2.exec(w);
+      stem = fp[1] + fp[2];
+      re2 = new RegExp(mgr1);
+      if (re2.test(stem))
+        w = stem;
+    }
+
+    // Step 5
+    re = /^(.+?)e$/;
+    if (re.test(w)) {
+      var fp = re.exec(w);
+      stem = fp[1];
+      re = new RegExp(mgr1);
+      re2 = new RegExp(meq1);
+      re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+      if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+        w = stem;
+    }
+    re = /ll$/;
+    re2 = new RegExp(mgr1);
+    if (re.test(w) && re2.test(w)) {
+      re = /.$/;
+      w = w.replace(re,"");
+    }
+
+    // and turn initial Y back to y
+    if (firstch == "y")
+      w = firstch.toLowerCase() + w.substr(1);
+    return w;
+  }
+}
+
diff --git a/docs/_build/html/_static/minus.png b/docs/_build/html/_static/minus.png
new file mode 100644
index 0000000000000000000000000000000000000000..d96755fdaf8bb2214971e0db9c1fd3077d7c419d
Binary files /dev/null and b/docs/_build/html/_static/minus.png differ
diff --git a/docs/_build/html/_static/plus.png b/docs/_build/html/_static/plus.png
new file mode 100644
index 0000000000000000000000000000000000000000..7107cec93a979b9a5f64843235a16651d563ce2d
Binary files /dev/null and b/docs/_build/html/_static/plus.png differ
diff --git a/docs/_build/html/_static/pygments.css b/docs/_build/html/_static/pygments.css
new file mode 100644
index 0000000000000000000000000000000000000000..84ab3030a9329e5598877502bfa7f8a999af8535
--- /dev/null
+++ b/docs/_build/html/_static/pygments.css
@@ -0,0 +1,75 @@
+pre { line-height: 125%; }
+td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+span.linenos { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }
+td.linenos .special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+span.linenos.special { color: #000000; background-color: #ffffc0; padding-left: 5px; padding-right: 5px; }
+.highlight .hll { background-color: #ffffcc }
+.highlight { background: #f8f8f8; }
+.highlight .c { color: #3D7B7B; font-style: italic } /* Comment */
+.highlight .err { border: 1px solid #FF0000 } /* Error */
+.highlight .k { color: #008000; font-weight: bold } /* Keyword */
+.highlight .o { color: #666666 } /* Operator */
+.highlight .ch { color: #3D7B7B; font-style: italic } /* Comment.Hashbang */
+.highlight .cm { color: #3D7B7B; font-style: italic } /* Comment.Multiline */
+.highlight .cp { color: #9C6500 } /* Comment.Preproc */
+.highlight .cpf { color: #3D7B7B; font-style: italic } /* Comment.PreprocFile */
+.highlight .c1 { color: #3D7B7B; font-style: italic } /* Comment.Single */
+.highlight .cs { color: #3D7B7B; font-style: italic } /* Comment.Special */
+.highlight .gd { color: #A00000 } /* Generic.Deleted */
+.highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
+.highlight .gr { color: #E40000 } /* Generic.Error */
+.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
+.highlight .gi { color: #008400 } /* Generic.Inserted */
+.highlight .go { color: #717171 } /* Generic.Output */
+.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
+.highlight .gs { font-weight: bold } /* Generic.Strong */
+.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
+.highlight .gt { color: #0044DD } /* Generic.Traceback */
+.highlight .kc { color: #008000; font-weight: bold } /* Keyword.Constant */
+.highlight .kd { color: #008000; font-weight: bold } /* Keyword.Declaration */
+.highlight .kn { color: #008000; font-weight: bold } /* Keyword.Namespace */
+.highlight .kp { color: #008000 } /* Keyword.Pseudo */
+.highlight .kr { color: #008000; font-weight: bold } /* Keyword.Reserved */
+.highlight .kt { color: #B00040 } /* Keyword.Type */
+.highlight .m { color: #666666 } /* Literal.Number */
+.highlight .s { color: #BA2121 } /* Literal.String */
+.highlight .na { color: #687822 } /* Name.Attribute */
+.highlight .nb { color: #008000 } /* Name.Builtin */
+.highlight .nc { color: #0000FF; font-weight: bold } /* Name.Class */
+.highlight .no { color: #880000 } /* Name.Constant */
+.highlight .nd { color: #AA22FF } /* Name.Decorator */
+.highlight .ni { color: #717171; font-weight: bold } /* Name.Entity */
+.highlight .ne { color: #CB3F38; font-weight: bold } /* Name.Exception */
+.highlight .nf { color: #0000FF } /* Name.Function */
+.highlight .nl { color: #767600 } /* Name.Label */
+.highlight .nn { color: #0000FF; font-weight: bold } /* Name.Namespace */
+.highlight .nt { color: #008000; font-weight: bold } /* Name.Tag */
+.highlight .nv { color: #19177C } /* Name.Variable */
+.highlight .ow { color: #AA22FF; font-weight: bold } /* Operator.Word */
+.highlight .w { color: #bbbbbb } /* Text.Whitespace */
+.highlight .mb { color: #666666 } /* Literal.Number.Bin */
+.highlight .mf { color: #666666 } /* Literal.Number.Float */
+.highlight .mh { color: #666666 } /* Literal.Number.Hex */
+.highlight .mi { color: #666666 } /* Literal.Number.Integer */
+.highlight .mo { color: #666666 } /* Literal.Number.Oct */
+.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
+.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
+.highlight .sc { color: #BA2121 } /* Literal.String.Char */
+.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
+.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
+.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
+.highlight .se { color: #AA5D1F; font-weight: bold } /* Literal.String.Escape */
+.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
+.highlight .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */
+.highlight .sx { color: #008000 } /* Literal.String.Other */
+.highlight .sr { color: #A45A77 } /* Literal.String.Regex */
+.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
+.highlight .ss { color: #19177C } /* Literal.String.Symbol */
+.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
+.highlight .fm { color: #0000FF } /* Name.Function.Magic */
+.highlight .vc { color: #19177C } /* Name.Variable.Class */
+.highlight .vg { color: #19177C } /* Name.Variable.Global */
+.highlight .vi { color: #19177C } /* Name.Variable.Instance */
+.highlight .vm { color: #19177C } /* Name.Variable.Magic */
+.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/docs/_build/html/_static/searchtools.js b/docs/_build/html/_static/searchtools.js
new file mode 100644
index 0000000000000000000000000000000000000000..b08d58c9b9b9a439ea5ec1739984ea24627d8c53
--- /dev/null
+++ b/docs/_build/html/_static/searchtools.js
@@ -0,0 +1,620 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+  var Scorer = {
+    // Implement the following function to further tweak the score for each result
+    // The function takes a result array [docname, title, anchor, descr, score, filename]
+    // and returns the new score.
+    /*
+    score: result => {
+      const [docname, title, anchor, descr, score, filename] = result
+      return score
+    },
+    */
+
+    // query matches the full name of an object
+    objNameMatch: 11,
+    // or matches in the last dotted part of the object name
+    objPartialMatch: 6,
+    // Additive scores depending on the priority of the object
+    objPrio: {
+      0: 15, // used to be importantResults
+      1: 5, // used to be objectResults
+      2: -5, // used to be unimportantResults
+    },
+    //  Used when the priority is not in the mapping.
+    objPrioDefault: 0,
+
+    // query found in title
+    title: 15,
+    partialTitle: 7,
+    // query found in terms
+    term: 5,
+    partialTerm: 2,
+  };
+}
+
+const _removeChildren = (element) => {
+  while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+  string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+  const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+  const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+  const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+  const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
+
+  const [docName, title, anchor, descr, score, _filename] = item;
+
+  let listItem = document.createElement("li");
+  let requestUrl;
+  let linkUrl;
+  if (docBuilder === "dirhtml") {
+    // dirhtml builder
+    let dirname = docName + "/";
+    if (dirname.match(/\/index\/$/))
+      dirname = dirname.substring(0, dirname.length - 6);
+    else if (dirname === "index/") dirname = "";
+    requestUrl = contentRoot + dirname;
+    linkUrl = requestUrl;
+  } else {
+    // normal html builders
+    requestUrl = contentRoot + docName + docFileSuffix;
+    linkUrl = docName + docLinkSuffix;
+  }
+  let linkEl = listItem.appendChild(document.createElement("a"));
+  linkEl.href = linkUrl + anchor;
+  linkEl.dataset.score = score;
+  linkEl.innerHTML = title;
+  if (descr) {
+    listItem.appendChild(document.createElement("span")).innerHTML =
+      " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
+  else if (showSearchSummary)
+    fetch(requestUrl)
+      .then((responseData) => responseData.text())
+      .then((data) => {
+        if (data)
+          listItem.appendChild(
+            Search.makeSearchSummary(data, searchTerms, anchor)
+          );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+      });
+  Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+  Search.stopPulse();
+  Search.title.innerText = _("Search Results");
+  if (!resultCount)
+    Search.status.innerText = Documentation.gettext(
+      "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+    );
+  else
+    Search.status.innerText = _(
+      "Search finished, found ${resultCount} page(s) matching the search query."
+    ).replace('${resultCount}', resultCount);
+};
+const _displayNextItem = (
+  results,
+  resultCount,
+  searchTerms,
+  highlightTerms,
+) => {
+  // results left, load the summary and display it
+  // this is intended to be dynamic (don't sub resultsCount)
+  if (results.length) {
+    _displayItem(results.pop(), searchTerms, highlightTerms);
+    setTimeout(
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+      5
+    );
+  }
+  // search finished, update title and status message
+  else _finishSearch(resultCount);
+};
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+  var splitQuery = (query) => query
+      .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+      .filter(term => term)  // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+  _index: null,
+  _queued_query: null,
+  _pulse_status: -1,
+
+  htmlToText: (htmlString, anchor) => {
+    const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+    for (const removalQuery of [".headerlink", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
+    const docContent = htmlElement.querySelector('[role="main"]');
+    if (docContent) return docContent.textContent;
+
+    console.warn(
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+    );
+    return "";
+  },
+
+  init: () => {
+    const query = new URLSearchParams(window.location.search).get("q");
+    document
+      .querySelectorAll('input[name="q"]')
+      .forEach((el) => (el.value = query));
+    if (query) Search.performSearch(query);
+  },
+
+  loadIndex: (url) =>
+    (document.body.appendChild(document.createElement("script")).src = url),
+
+  setIndex: (index) => {
+    Search._index = index;
+    if (Search._queued_query !== null) {
+      const query = Search._queued_query;
+      Search._queued_query = null;
+      Search.query(query);
+    }
+  },
+
+  hasIndex: () => Search._index !== null,
+
+  deferQuery: (query) => (Search._queued_query = query),
+
+  stopPulse: () => (Search._pulse_status = -1),
+
+  startPulse: () => {
+    if (Search._pulse_status >= 0) return;
+
+    const pulse = () => {
+      Search._pulse_status = (Search._pulse_status + 1) % 4;
+      Search.dots.innerText = ".".repeat(Search._pulse_status);
+      if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+    };
+    pulse();
+  },
+
+  /**
+   * perform a search for something (or wait until index is loaded)
+   */
+  performSearch: (query) => {
+    // create the required interface elements
+    const searchText = document.createElement("h2");
+    searchText.textContent = _("Searching");
+    const searchSummary = document.createElement("p");
+    searchSummary.classList.add("search-summary");
+    searchSummary.innerText = "";
+    const searchList = document.createElement("ul");
+    searchList.classList.add("search");
+
+    const out = document.getElementById("search-results");
+    Search.title = out.appendChild(searchText);
+    Search.dots = Search.title.appendChild(document.createElement("span"));
+    Search.status = out.appendChild(searchSummary);
+    Search.output = out.appendChild(searchList);
+
+    const searchProgress = document.getElementById("search-progress");
+    // Some themes don't use the search progress node
+    if (searchProgress) {
+      searchProgress.innerText = _("Preparing search...");
+    }
+    Search.startPulse();
+
+    // index already loaded, the browser was quick!
+    if (Search.hasIndex()) Search.query(query);
+    else Search.deferQuery(query);
+  },
+
+  _parseQuery: (query) => {
+    // stem the search terms and add them to the correct list
+    const stemmer = new Stemmer();
+    const searchTerms = new Set();
+    const excludedTerms = new Set();
+    const highlightTerms = new Set();
+    const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+    splitQuery(query.trim()).forEach((queryTerm) => {
+      const queryTermLower = queryTerm.toLowerCase();
+
+      // maybe skip this "word"
+      // stopwords array is from language_data.js
+      if (
+        stopwords.indexOf(queryTermLower) !== -1 ||
+        queryTerm.match(/^\d+$/)
+      )
+        return;
+
+      // stem the word
+      let word = stemmer.stemWord(queryTermLower);
+      // select the correct list
+      if (word[0] === "-") excludedTerms.add(word.substr(1));
+      else {
+        searchTerms.add(word);
+        highlightTerms.add(queryTermLower);
+      }
+    });
+
+    if (SPHINX_HIGHLIGHT_ENABLED) {  // set in sphinx_highlight.js
+      localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+    }
+
+    // console.debug("SEARCH: searching for:");
+    // console.info("required: ", [...searchTerms]);
+    // console.info("excluded: ", [...excludedTerms]);
+
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
+    _removeChildren(document.getElementById("search-progress"));
+
+    const queryLower = query.toLowerCase().trim();
+    for (const [title, foundTitles] of Object.entries(allTitles)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+        for (const [file, id] of foundTitles) {
+          const score = Math.round(Scorer.title * queryLower.length / title.length);
+          const boost = titles[file] === title ? 1 : 0;  // add a boost for document titles
+          normalResults.push([
+            docNames[file],
+            titles[file] !== title ? `${titles[file]} > ${title}` : title,
+            id !== null ? "#" + id : "",
+            null,
+            score + boost,
+            filenames[file],
+          ]);
+        }
+      }
+    }
+
+    // search for explicit entries in index directives
+    for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+      if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
+            docNames[file],
+            titles[file],
+            id ? "#" + id : "",
+            null,
+            score,
+            filenames[file],
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
+        }
+      }
+    }
+
+    // lookup as object
+    objectTerms.forEach((term) =>
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
+    );
+
+    // lookup as search terms in fulltext
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+    // let the scorer override scores with a custom scoring function
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
+
+    // remove duplicate search results
+    // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+    let seen = new Set();
+    results = results.reverse().reduce((acc, result) => {
+      let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+      if (!seen.has(resultStr)) {
+        acc.push(result);
+        seen.add(resultStr);
+      }
+      return acc;
+    }, []);
+
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+
+    // for debugging
+    //Search.lastresults = results.slice();  // a copy
+    // console.info("search results:", Search.lastresults);
+
+    // print the results
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
+  },
+
+  /**
+   * search for object names
+   */
+  performObjectSearch: (object, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const objects = Search._index.objects;
+    const objNames = Search._index.objnames;
+    const titles = Search._index.titles;
+
+    const results = [];
+
+    const objectSearchCallback = (prefix, match) => {
+      const name = match[4]
+      const fullname = (prefix ? prefix + "." : "") + name;
+      const fullnameLower = fullname.toLowerCase();
+      if (fullnameLower.indexOf(object) < 0) return;
+
+      let score = 0;
+      const parts = fullnameLower.split(".");
+
+      // check for different match types: exact matches of full name or
+      // "last name" (i.e. last dotted part)
+      if (fullnameLower === object || parts.slice(-1)[0] === object)
+        score += Scorer.objNameMatch;
+      else if (parts.slice(-1)[0].indexOf(object) > -1)
+        score += Scorer.objPartialMatch; // matches in last name
+
+      const objName = objNames[match[1]][2];
+      const title = titles[match[0]];
+
+      // If more than one term searched for, we require other words to be
+      // found in the name/title/description
+      const otherTerms = new Set(objectTerms);
+      otherTerms.delete(object);
+      if (otherTerms.size > 0) {
+        const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+        if (
+          [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+        )
+          return;
+      }
+
+      let anchor = match[3];
+      if (anchor === "") anchor = fullname;
+      else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+      const descr = objName + _(", in ") + title;
+
+      // add custom score for some objects according to scorer
+      if (Scorer.objPrio.hasOwnProperty(match[2]))
+        score += Scorer.objPrio[match[2]];
+      else score += Scorer.objPrioDefault;
+
+      results.push([
+        docNames[match[0]],
+        fullname,
+        "#" + anchor,
+        descr,
+        score,
+        filenames[match[0]],
+      ]);
+    };
+    Object.keys(objects).forEach((prefix) =>
+      objects[prefix].forEach((array) =>
+        objectSearchCallback(prefix, array)
+      )
+    );
+    return results;
+  },
+
+  /**
+   * search for full-text terms in the index
+   */
+  performTermsSearch: (searchTerms, excludedTerms) => {
+    // prepare search
+    const terms = Search._index.terms;
+    const titleTerms = Search._index.titleterms;
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+
+    const scoreMap = new Map();
+    const fileMap = new Map();
+
+    // perform the search on the required terms
+    searchTerms.forEach((word) => {
+      const files = [];
+      const arr = [
+        { files: terms[word], score: Scorer.term },
+        { files: titleTerms[word], score: Scorer.title },
+      ];
+      // add support for partial matches
+      if (word.length > 2) {
+        const escapedWord = _escapeRegExp(word);
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
+      }
+
+      // no match but word was a required one
+      if (arr.every((record) => record.files === undefined)) return;
+
+      // found search word in contents
+      arr.forEach((record) => {
+        if (record.files === undefined) return;
+
+        let recordFiles = record.files;
+        if (recordFiles.length === undefined) recordFiles = [recordFiles];
+        files.push(...recordFiles);
+
+        // set score for the word in each file
+        recordFiles.forEach((file) => {
+          if (!scoreMap.has(file)) scoreMap.set(file, {});
+          scoreMap.get(file)[word] = record.score;
+        });
+      });
+
+      // create the mapping
+      files.forEach((file) => {
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+      });
+    });
+
+    // now check if the files don't contain excluded terms
+    const results = [];
+    for (const [file, wordList] of fileMap) {
+      // check if all requirements are matched
+
+      // as search terms with length < 3 are discarded
+      const filteredTermCount = [...searchTerms].filter(
+        (term) => term.length > 2
+      ).length;
+      if (
+        wordList.length !== searchTerms.size &&
+        wordList.length !== filteredTermCount
+      )
+        continue;
+
+      // ensure that none of the excluded terms is in the search result
+      if (
+        [...excludedTerms].some(
+          (term) =>
+            terms[term] === file ||
+            titleTerms[term] === file ||
+            (terms[term] || []).includes(file) ||
+            (titleTerms[term] || []).includes(file)
+        )
+      )
+        break;
+
+      // select one (max) score for the file.
+      const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+      // add result to the result list
+      results.push([
+        docNames[file],
+        titles[file],
+        "",
+        null,
+        score,
+        filenames[file],
+      ]);
+    }
+    return results;
+  },
+
+  /**
+   * helper function to return a node containing the
+   * search summary for a given text. keywords is a list
+   * of stemmed words.
+   */
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
+    if (text === "") return null;
+
+    const textLower = text.toLowerCase();
+    const actualStartPosition = [...keywords]
+      .map((k) => textLower.indexOf(k.toLowerCase()))
+      .filter((i) => i > -1)
+      .slice(-1)[0];
+    const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+    const top = startWithContext === 0 ? "" : "...";
+    const tail = startWithContext + 240 < text.length ? "..." : "";
+
+    let summary = document.createElement("p");
+    summary.classList.add("context");
+    summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+    return summary;
+  },
+};
+
+_ready(Search.init);
diff --git a/docs/_build/html/_static/sphinx_highlight.js b/docs/_build/html/_static/sphinx_highlight.js
new file mode 100644
index 0000000000000000000000000000000000000000..8a96c69a1942318413af68fd459122b56edd8d69
--- /dev/null
+++ b/docs/_build/html/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+  if (node.nodeType === Node.TEXT_NODE) {
+    const val = node.nodeValue;
+    const parent = node.parentNode;
+    const pos = val.toLowerCase().indexOf(text);
+    if (
+      pos >= 0 &&
+      !parent.classList.contains(className) &&
+      !parent.classList.contains("nohighlight")
+    ) {
+      let span;
+
+      const closestNode = parent.closest("body, svg, foreignObject");
+      const isInSVG = closestNode && closestNode.matches("svg");
+      if (isInSVG) {
+        span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+      } else {
+        span = document.createElement("span");
+        span.classList.add(className);
+      }
+
+      span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
+      parent.insertBefore(
+        span,
+        parent.insertBefore(
+          rest,
+          node.nextSibling
+        )
+      );
+      node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
+
+      if (isInSVG) {
+        const rect = document.createElementNS(
+          "http://www.w3.org/2000/svg",
+          "rect"
+        );
+        const bbox = parent.getBBox();
+        rect.x.baseVal.value = bbox.x;
+        rect.y.baseVal.value = bbox.y;
+        rect.width.baseVal.value = bbox.width;
+        rect.height.baseVal.value = bbox.height;
+        rect.setAttribute("class", className);
+        addItems.push({ parent: parent, target: rect });
+      }
+    }
+  } else if (node.matches && !node.matches("button, select, textarea")) {
+    node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+  }
+};
+const _highlightText = (thisNode, text, className) => {
+  let addItems = [];
+  _highlight(thisNode, addItems, text, className);
+  addItems.forEach((obj) =>
+    obj.parent.insertAdjacentElement("beforebegin", obj.target)
+  );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+  /**
+   * highlight the search words provided in localstorage in the text
+   */
+  highlightSearchWords: () => {
+    if (!SPHINX_HIGHLIGHT_ENABLED) return;  // bail if no highlight
+
+    // get and clear terms from localstorage
+    const url = new URL(window.location);
+    const highlight =
+        localStorage.getItem("sphinx_highlight_terms")
+        || url.searchParams.get("highlight")
+        || "";
+    localStorage.removeItem("sphinx_highlight_terms")
+    url.searchParams.delete("highlight");
+    window.history.replaceState({}, "", url);
+
+    // get individual terms from highlight string
+    const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+    if (terms.length === 0) return; // nothing to do
+
+    // There should never be more than one element matching "div.body"
+    const divBody = document.querySelectorAll("div.body");
+    const body = divBody.length ? divBody[0] : document.querySelector("body");
+    window.setTimeout(() => {
+      terms.forEach((term) => _highlightText(body, term, "highlighted"));
+    }, 10);
+
+    const searchBox = document.getElementById("searchbox");
+    if (searchBox === null) return;
+    searchBox.appendChild(
+      document
+        .createRange()
+        .createContextualFragment(
+          '<p class="highlight-link">' +
+            '<a href="javascript:SphinxHighlight.hideSearchWords()">' +
+            _("Hide Search Matches") +
+            "</a></p>"
+        )
+    );
+  },
+
+  /**
+   * helper function to hide the search marks again
+   */
+  hideSearchWords: () => {
+    document
+      .querySelectorAll("#searchbox .highlight-link")
+      .forEach((el) => el.remove());
+    document
+      .querySelectorAll("span.highlighted")
+      .forEach((el) => el.classList.remove("highlighted"));
+    localStorage.removeItem("sphinx_highlight_terms")
+  },
+
+  initEscapeListener: () => {
+    // only install a listener if it is really needed
+    if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return;
+
+    document.addEventListener("keydown", (event) => {
+      // bail for input elements
+      if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+      // bail with special keys
+      if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return;
+      if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) {
+        SphinxHighlight.hideSearchWords();
+        event.preventDefault();
+      }
+    });
+  },
+};
+
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/docs/_build/html/genindex.html b/docs/_build/html/genindex.html
new file mode 100644
index 0000000000000000000000000000000000000000..87c18b2aea82953e85fe058676e6beb187546d15
--- /dev/null
+++ b/docs/_build/html/genindex.html
@@ -0,0 +1,649 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Index &mdash; PROTAC-Degradation-Predictor v1.0.1 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=80d5e7a1" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+
+  
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=d29a5c0e"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+    <script src="_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="#" />
+    <link rel="search" title="Search" href="search.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="index.html" class="icon icon-home">
+            PROTAC-Degradation-Predictor
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="source/modules.html">protac_degradation_predictor</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">PROTAC-Degradation-Predictor</a>
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+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+
+<h1 id="index">Index</h1>
+
+<div class="genindex-jumpbox">
+ <a href="#A"><strong>A</strong></a>
+ | <a href="#B"><strong>B</strong></a>
+ | <a href="#C"><strong>C</strong></a>
+ | <a href="#D"><strong>D</strong></a>
+ | <a href="#E"><strong>E</strong></a>
+ | <a href="#F"><strong>F</strong></a>
+ | <a href="#G"><strong>G</strong></a>
+ | <a href="#H"><strong>H</strong></a>
+ | <a href="#I"><strong>I</strong></a>
+ | <a href="#L"><strong>L</strong></a>
+ | <a href="#M"><strong>M</strong></a>
+ | <a href="#N"><strong>N</strong></a>
+ | <a href="#O"><strong>O</strong></a>
+ | <a href="#P"><strong>P</strong></a>
+ | <a href="#R"><strong>R</strong></a>
+ | <a href="#S"><strong>S</strong></a>
+ | <a href="#T"><strong>T</strong></a>
+ | <a href="#V"><strong>V</strong></a>
+ | <a href="#X"><strong>X</strong></a>
+ | <a href="#Z"><strong>Z</strong></a>
+ 
+</div>
+<h2 id="A">A</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.config.Config.active_label">active_label (protac_degradation_predictor.config.Config attribute)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.add_module">add_module() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.apply">apply() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.apply_scalers">apply_scalers() (protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.apply_scaling">apply_scaling() (protac_degradation_predictor.protac_dataset.PROTAC_Dataset method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.apply_smote">apply_smote() (protac_degradation_predictor.protac_dataset.PROTAC_Dataset method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.data_utils.avail_cell_lines">avail_cell_lines() (in module protac_degradation_predictor.data_utils)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.data_utils.avail_e3_ligases">avail_e3_ligases() (in module protac_degradation_predictor.data_utils)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.data_utils.avail_uniprots">avail_uniprots() (in module protac_degradation_predictor.data_utils)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.bfloat16">bfloat16() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.buffers">buffers() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="C">C</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.call_super_init">call_super_init (protac_degradation_predictor.pytorch_models.PROTAC_Predictor attribute)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.config.Config.cell_embedding_size">cell_embedding_size (protac_degradation_predictor.config.Config attribute)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.children">children() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.config.Config">Config (class in protac_degradation_predictor.config)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.configure_optimizers">configure_optimizers() (protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.cpu">cpu() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.cuda">cuda() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="D">D</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.config.Config.dmax_threshold">dmax_threshold (protac_degradation_predictor.config.Config attribute)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.double">double() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.dump_patches">dump_patches (protac_degradation_predictor.pytorch_models.PROTAC_Predictor attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="E">E</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.config.Config.e3_ligase2uniprot">e3_ligase2uniprot (protac_degradation_predictor.config.Config attribute)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.eval">eval() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.evaluate_model">evaluate_model() (in module protac_degradation_predictor.pytorch_models)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.extra_repr">extra_repr() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="F">F</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.config.Config.fingerprint_size">fingerprint_size (protac_degradation_predictor.config.Config attribute)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.fit_scaling">fit_scaling() (protac_degradation_predictor.protac_dataset.PROTAC_Dataset method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.float">float() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.forward">forward() (protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.forward">(protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+</tr></table>
+
+<h2 id="G">G</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_buffer">get_buffer() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_cell_emb_dim">get_cell_emb_dim() (protac_degradation_predictor.protac_dataset.PROTAC_Dataset method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils_xgboost.get_confidence_scores">get_confidence_scores() (in module protac_degradation_predictor.optuna_utils_xgboost)</a>
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.get_confidence_scores">(in module protac_degradation_predictor.pytorch_models)</a>
+</li>
+      </ul></li>
+      <li><a href="source/protac_degradation_predictor.optuna.html#protac_degradation_predictor.optuna.utils.get_dataframe_stats">get_dataframe_stats() (in module protac_degradation_predictor.optuna.utils)</a>
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.get_dataframe_stats">(in module protac_degradation_predictor.optuna_utils)</a>
+</li>
+      </ul></li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.get_datasets">get_datasets() (in module protac_degradation_predictor.protac_dataset)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_e3_ligase_split_indices">get_e3_ligase_split_indices() (protac_degradation_predictor.protac_dataset.PROTAC_DataModule static method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_extra_state">get_extra_state() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.data_utils.get_fingerprint">get_fingerprint() (in module protac_degradation_predictor.data_utils)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.optuna.html#protac_degradation_predictor.optuna.utils.get_majority_vote_metrics">get_majority_vote_metrics() (in module protac_degradation_predictor.optuna.utils)</a>
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.get_majority_vote_metrics">(in module protac_degradation_predictor.optuna_utils)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_numpy_arrays">get_numpy_arrays() (protac_degradation_predictor.protac_dataset.PROTAC_Dataset method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_parameter">get_parameter() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_degradation_predictor.get_protac_active_proba">get_protac_active_proba() (in module protac_degradation_predictor.protac_degradation_predictor)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_degradation_predictor.get_protac_embedding">get_protac_embedding() (in module protac_degradation_predictor.protac_degradation_predictor)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_protein_emb_dim">get_protein_emb_dim() (protac_degradation_predictor.protac_dataset.PROTAC_Dataset method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_random_split_indices">get_random_split_indices() (protac_degradation_predictor.protac_dataset.PROTAC_DataModule static method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_smiles2fp_and_avg_tanimoto">get_smiles2fp_and_avg_tanimoto() (protac_degradation_predictor.protac_dataset.PROTAC_DataModule static method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_smiles_emb_dim">get_smiles_emb_dim() (protac_degradation_predictor.protac_dataset.PROTAC_Dataset method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_submodule">get_submodule() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.get_suggestion">get_suggestion() (in module protac_degradation_predictor.optuna_utils)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_tanimoto_split_indices">get_tanimoto_split_indices() (protac_degradation_predictor.protac_dataset.PROTAC_DataModule static method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_target_split_indices">get_target_split_indices() (protac_degradation_predictor.protac_dataset.PROTAC_DataModule static method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="H">H</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.half">half() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.hyperparameter_tuning_and_training">hyperparameter_tuning_and_training() (in module protac_degradation_predictor.optuna_utils)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.initialize_scalers">initialize_scalers() (protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.ipu">ipu() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.data_utils.is_active">is_active() (in module protac_degradation_predictor.data_utils)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_degradation_predictor.is_protac_active">is_protac_active() (in module protac_degradation_predictor.protac_degradation_predictor)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="L">L</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.data_utils.load_curated_dataset">load_curated_dataset() (in module protac_degradation_predictor.data_utils)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.load_model">load_model() (in module protac_degradation_predictor.pytorch_models)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.load_state_dict">load_state_dict() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="M">M</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    module
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor">protac_degradation_predictor</a>
+</li>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.config">protac_degradation_predictor.config</a>
+</li>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.data_utils">protac_degradation_predictor.data_utils</a>
+</li>
+        <li><a href="source/protac_degradation_predictor.optuna.html#module-protac_degradation_predictor.optuna">protac_degradation_predictor.optuna</a>
+</li>
+        <li><a href="source/protac_degradation_predictor.optuna.html#module-protac_degradation_predictor.optuna.utils">protac_degradation_predictor.optuna.utils</a>
+</li>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.optuna_utils">protac_degradation_predictor.optuna_utils</a>
+</li>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.optuna_utils_xgboost">protac_degradation_predictor.optuna_utils_xgboost</a>
+</li>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.protac_dataset">protac_degradation_predictor.protac_dataset</a>
+</li>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.protac_degradation_predictor">protac_degradation_predictor.protac_degradation_predictor</a>
+</li>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.pytorch_models">protac_degradation_predictor.pytorch_models</a>
+</li>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.sklearn_models">protac_degradation_predictor.sklearn_models</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.modules">modules() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.config.Config.morgan_radius">morgan_radius (protac_degradation_predictor.config.Config attribute)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="N">N</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_buffers">named_buffers() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_children">named_children() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_modules">named_modules() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_parameters">named_parameters() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="O">O</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.on_load_checkpoint">on_load_checkpoint() (protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.on_save_checkpoint">on_save_checkpoint() (protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="P">P</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.parameters">parameters() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.config.Config.pdc50_threshold">pdc50_threshold (protac_degradation_predictor.config.Config attribute)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.predict_step">predict_step() (protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule">PROTAC_DataModule (class in protac_degradation_predictor.protac_dataset)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset">PROTAC_Dataset (class in protac_degradation_predictor.protac_dataset)</a>
+</li>
+      <li>
+    protac_degradation_predictor
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor">module</a>
+</li>
+      </ul></li>
+      <li>
+    protac_degradation_predictor.config
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.config">module</a>
+</li>
+      </ul></li>
+      <li>
+    protac_degradation_predictor.data_utils
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.data_utils">module</a>
+</li>
+      </ul></li>
+      <li>
+    protac_degradation_predictor.optuna
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.optuna.html#module-protac_degradation_predictor.optuna">module</a>
+</li>
+      </ul></li>
+      <li>
+    protac_degradation_predictor.optuna.utils
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.optuna.html#module-protac_degradation_predictor.optuna.utils">module</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li>
+    protac_degradation_predictor.optuna_utils
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.optuna_utils">module</a>
+</li>
+      </ul></li>
+      <li>
+    protac_degradation_predictor.optuna_utils_xgboost
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.optuna_utils_xgboost">module</a>
+</li>
+      </ul></li>
+      <li>
+    protac_degradation_predictor.protac_dataset
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.protac_dataset">module</a>
+</li>
+      </ul></li>
+      <li>
+    protac_degradation_predictor.protac_degradation_predictor
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.protac_degradation_predictor">module</a>
+</li>
+      </ul></li>
+      <li>
+    protac_degradation_predictor.pytorch_models
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.pytorch_models">module</a>
+</li>
+      </ul></li>
+      <li>
+    protac_degradation_predictor.sklearn_models
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.sklearn_models">module</a>
+</li>
+      </ul></li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model">PROTAC_Model (class in protac_degradation_predictor.pytorch_models)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor">PROTAC_Predictor (class in protac_degradation_predictor.pytorch_models)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.config.Config.protein_embedding_size">protein_embedding_size (protac_degradation_predictor.config.Config attribute)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.pytorch_model_objective">pytorch_model_objective() (in module protac_degradation_predictor.optuna_utils)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="R">R</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_backward_hook">register_backward_hook() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_buffer">register_buffer() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_forward_hook">register_forward_hook() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_forward_pre_hook">register_forward_pre_hook() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_full_backward_hook">register_full_backward_hook() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_full_backward_pre_hook">register_full_backward_pre_hook() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_load_state_dict_post_hook">register_load_state_dict_post_hook() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_module">register_module() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_parameter">register_parameter() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_state_dict_pre_hook">register_state_dict_pre_hook() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.requires_grad_">requires_grad_() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="S">S</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.scale_tensor">scale_tensor() (protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.set_extra_state">set_extra_state() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.setup">setup() (protac_degradation_predictor.protac_dataset.PROTAC_DataModule method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.share_memory">share_memory() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict">state_dict() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.step">step() (protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.sklearn_models.suggest_gradient_boosting">suggest_gradient_boosting() (in module protac_degradation_predictor.sklearn_models)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.sklearn_models.suggest_logistic_regression">suggest_logistic_regression() (in module protac_degradation_predictor.sklearn_models)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.sklearn_models.suggest_random_forest">suggest_random_forest() (in module protac_degradation_predictor.sklearn_models)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.sklearn_models.suggest_svc">suggest_svc() (in module protac_degradation_predictor.sklearn_models)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="T">T</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.T_destination">T_destination (protac_degradation_predictor.pytorch_models.PROTAC_Predictor attribute)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.test_dataloader">test_dataloader() (protac_degradation_predictor.protac_dataset.PROTAC_DataModule method)</a>
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.test_dataloader">(protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+</li>
+      </ul></li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.test_step">test_step() (protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.to">to() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.to_empty">to_empty() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.train">train() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils_xgboost.train_and_evaluate_xgboost">train_and_evaluate_xgboost() (in module protac_degradation_predictor.optuna_utils_xgboost)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.train_dataloader">train_dataloader() (protac_degradation_predictor.protac_dataset.PROTAC_DataModule method)</a>
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.train_dataloader">(protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+</li>
+      </ul></li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.train_model">train_model() (in module protac_degradation_predictor.pytorch_models)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.sklearn_models.train_sklearn_model">train_sklearn_model() (in module protac_degradation_predictor.sklearn_models)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.training">training (protac_degradation_predictor.pytorch_models.PROTAC_Predictor attribute)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.training_step">training_step() (protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.type">type() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="V">V</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.val_dataloader">val_dataloader() (protac_degradation_predictor.protac_dataset.PROTAC_DataModule method)</a>
+
+      <ul>
+        <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.val_dataloader">(protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+</li>
+      </ul></li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.validation_step">validation_step() (protac_degradation_predictor.pytorch_models.PROTAC_Model method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="X">X</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils_xgboost.xgboost_hyperparameter_tuning_and_training">xgboost_hyperparameter_tuning_and_training() (in module protac_degradation_predictor.optuna_utils_xgboost)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils_xgboost.xgboost_model_objective">xgboost_model_objective() (in module protac_degradation_predictor.optuna_utils_xgboost)</a>
+</li>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.xpu">xpu() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+<h2 id="Z">Z</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.zero_grad">zero_grad() (protac_degradation_predictor.pytorch_models.PROTAC_Predictor method)</a>
+</li>
+  </ul></td>
+</tr></table>
+
+
+
+           </div>
+          </div>
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+<h1>PROTAC-Degradation-Predictor documentation<a class="headerlink" href="#protac-degradation-predictor-documentation" title="Link to this heading"></a></h1>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="source/modules.html">protac_degradation_predictor</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="source/protac_degradation_predictor.html">protac_degradation_predictor package</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="source/protac_degradation_predictor.html#subpackages">Subpackages</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.optuna.html">protac_degradation_predictor.optuna package</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="source/protac_degradation_predictor.html#submodules">Submodules</a></li>
+<li class="toctree-l3"><a class="reference internal" href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.config">protac_degradation_predictor.config module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.config.Config"><code class="docutils literal notranslate"><span class="pre">Config</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.data_utils">protac_degradation_predictor.data_utils module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.data_utils.avail_e3_ligases"><code class="docutils literal notranslate"><span class="pre">avail_e3_ligases()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.data_utils.avail_cell_lines"><code class="docutils literal notranslate"><span class="pre">avail_cell_lines()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.data_utils.avail_uniprots"><code class="docutils literal notranslate"><span class="pre">avail_uniprots()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.data_utils.get_fingerprint"><code class="docutils literal notranslate"><span class="pre">get_fingerprint()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.data_utils.is_active"><code class="docutils literal notranslate"><span class="pre">is_active()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.data_utils.load_curated_dataset"><code class="docutils literal notranslate"><span class="pre">load_curated_dataset()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.optuna_utils">protac_degradation_predictor.optuna_utils module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.get_dataframe_stats"><code class="docutils literal notranslate"><span class="pre">get_dataframe_stats()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.get_majority_vote_metrics"><code class="docutils literal notranslate"><span class="pre">get_majority_vote_metrics()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.get_suggestion"><code class="docutils literal notranslate"><span class="pre">get_suggestion()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.pytorch_model_objective"><code class="docutils literal notranslate"><span class="pre">pytorch_model_objective()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.hyperparameter_tuning_and_training"><code class="docutils literal notranslate"><span class="pre">hyperparameter_tuning_and_training()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.optuna_utils_xgboost">protac_degradation_predictor.optuna_utils_xgboost module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils_xgboost.get_confidence_scores"><code class="docutils literal notranslate"><span class="pre">get_confidence_scores()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils_xgboost.train_and_evaluate_xgboost"><code class="docutils literal notranslate"><span class="pre">train_and_evaluate_xgboost()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils_xgboost.xgboost_model_objective"><code class="docutils literal notranslate"><span class="pre">xgboost_model_objective()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils_xgboost.xgboost_hyperparameter_tuning_and_training"><code class="docutils literal notranslate"><span class="pre">xgboost_hyperparameter_tuning_and_training()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.protac_dataset">protac_degradation_predictor.protac_dataset module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset"><code class="docutils literal notranslate"><span class="pre">PROTAC_Dataset</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.get_datasets"><code class="docutils literal notranslate"><span class="pre">get_datasets()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule"><code class="docutils literal notranslate"><span class="pre">PROTAC_DataModule</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.protac_degradation_predictor">protac_degradation_predictor.protac_degradation_predictor module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_degradation_predictor.get_protac_active_proba"><code class="docutils literal notranslate"><span class="pre">get_protac_active_proba()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_degradation_predictor.is_protac_active"><code class="docutils literal notranslate"><span class="pre">is_protac_active()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.protac_degradation_predictor.get_protac_embedding"><code class="docutils literal notranslate"><span class="pre">get_protac_embedding()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.pytorch_models">protac_degradation_predictor.pytorch_models module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.get_confidence_scores"><code class="docutils literal notranslate"><span class="pre">get_confidence_scores()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.train_model"><code class="docutils literal notranslate"><span class="pre">train_model()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.evaluate_model"><code class="docutils literal notranslate"><span class="pre">evaluate_model()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.load_model"><code class="docutils literal notranslate"><span class="pre">load_model()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.sklearn_models">protac_degradation_predictor.sklearn_models module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.sklearn_models.train_sklearn_model"><code class="docutils literal notranslate"><span class="pre">train_sklearn_model()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.sklearn_models.suggest_random_forest"><code class="docutils literal notranslate"><span class="pre">suggest_random_forest()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.sklearn_models.suggest_logistic_regression"><code class="docutils literal notranslate"><span class="pre">suggest_logistic_regression()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="source/protac_degradation_predictor.html#protac_degradation_predictor.sklearn_models.suggest_svc"><code class="docutils literal notranslate"><span class="pre">suggest_svc()</span></code></a></li>
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+   <table class="indextable modindextable">
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+       <a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.config"><code class="xref">protac_degradation_predictor.config</code></a></td><td>
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+       <a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.data_utils"><code class="xref">protac_degradation_predictor.data_utils</code></a></td><td>
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+       <a href="source/protac_degradation_predictor.optuna.html#module-protac_degradation_predictor.optuna"><code class="xref">protac_degradation_predictor.optuna</code></a></td><td>
+       <em></em></td></tr>
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+       <td>&#160;&#160;&#160;
+       <a href="source/protac_degradation_predictor.optuna.html#module-protac_degradation_predictor.optuna.utils"><code class="xref">protac_degradation_predictor.optuna.utils</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
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+       <a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.optuna_utils"><code class="xref">protac_degradation_predictor.optuna_utils</code></a></td><td>
+       <em></em></td></tr>
+     <tr class="cg-1">
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+       <td>&#160;&#160;&#160;
+       <a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.optuna_utils_xgboost"><code class="xref">protac_degradation_predictor.optuna_utils_xgboost</code></a></td><td>
+       <em></em></td></tr>
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+       <a href="source/protac_degradation_predictor.html#module-protac_degradation_predictor.protac_dataset"><code class="xref">protac_degradation_predictor.protac_dataset</code></a></td><td>
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+  <section id="protac-degradation-predictor">
+<h1>protac_degradation_predictor<a class="headerlink" href="#protac-degradation-predictor" title="Link to this heading"></a></h1>
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+<ul>
+<li class="toctree-l1"><a class="reference internal" href="protac_degradation_predictor.html">protac_degradation_predictor package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.html#subpackages">Subpackages</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.optuna.html">protac_degradation_predictor.optuna package</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.optuna.html#submodules">Submodules</a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.optuna.html#protac-degradation-predictor-optuna-pytorch-models-module">protac_degradation_predictor.optuna.pytorch_models module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.optuna.html#protac-degradation-predictor-optuna-sklearn-models-module">protac_degradation_predictor.optuna.sklearn_models module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.optuna.html#module-protac_degradation_predictor.optuna.utils">protac_degradation_predictor.optuna.utils module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.optuna.html#protac-degradation-predictor-optuna-xgboost-models-module">protac_degradation_predictor.optuna.xgboost_models module</a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.optuna.html#module-protac_degradation_predictor.optuna">Module contents</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.html#module-protac_degradation_predictor.config">protac_degradation_predictor.config module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.config.Config"><code class="docutils literal notranslate"><span class="pre">Config</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.config.Config.morgan_radius"><code class="docutils literal notranslate"><span class="pre">Config.morgan_radius</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.config.Config.fingerprint_size"><code class="docutils literal notranslate"><span class="pre">Config.fingerprint_size</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.config.Config.protein_embedding_size"><code class="docutils literal notranslate"><span class="pre">Config.protein_embedding_size</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.config.Config.cell_embedding_size"><code class="docutils literal notranslate"><span class="pre">Config.cell_embedding_size</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.config.Config.dmax_threshold"><code class="docutils literal notranslate"><span class="pre">Config.dmax_threshold</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.config.Config.pdc50_threshold"><code class="docutils literal notranslate"><span class="pre">Config.pdc50_threshold</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.config.Config.active_label"><code class="docutils literal notranslate"><span class="pre">Config.active_label</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.config.Config.e3_ligase2uniprot"><code class="docutils literal notranslate"><span class="pre">Config.e3_ligase2uniprot</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.html#module-protac_degradation_predictor.data_utils">protac_degradation_predictor.data_utils module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.data_utils.avail_e3_ligases"><code class="docutils literal notranslate"><span class="pre">avail_e3_ligases()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.data_utils.avail_cell_lines"><code class="docutils literal notranslate"><span class="pre">avail_cell_lines()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.data_utils.avail_uniprots"><code class="docutils literal notranslate"><span class="pre">avail_uniprots()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.data_utils.get_fingerprint"><code class="docutils literal notranslate"><span class="pre">get_fingerprint()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.data_utils.is_active"><code class="docutils literal notranslate"><span class="pre">is_active()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.data_utils.load_curated_dataset"><code class="docutils literal notranslate"><span class="pre">load_curated_dataset()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.html#module-protac_degradation_predictor.optuna_utils">protac_degradation_predictor.optuna_utils module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.get_dataframe_stats"><code class="docutils literal notranslate"><span class="pre">get_dataframe_stats()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.get_majority_vote_metrics"><code class="docutils literal notranslate"><span class="pre">get_majority_vote_metrics()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.get_suggestion"><code class="docutils literal notranslate"><span class="pre">get_suggestion()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.pytorch_model_objective"><code class="docutils literal notranslate"><span class="pre">pytorch_model_objective()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils.hyperparameter_tuning_and_training"><code class="docutils literal notranslate"><span class="pre">hyperparameter_tuning_and_training()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.html#module-protac_degradation_predictor.optuna_utils_xgboost">protac_degradation_predictor.optuna_utils_xgboost module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils_xgboost.get_confidence_scores"><code class="docutils literal notranslate"><span class="pre">get_confidence_scores()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils_xgboost.train_and_evaluate_xgboost"><code class="docutils literal notranslate"><span class="pre">train_and_evaluate_xgboost()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils_xgboost.xgboost_model_objective"><code class="docutils literal notranslate"><span class="pre">xgboost_model_objective()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.optuna_utils_xgboost.xgboost_hyperparameter_tuning_and_training"><code class="docutils literal notranslate"><span class="pre">xgboost_hyperparameter_tuning_and_training()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.html#module-protac_degradation_predictor.protac_dataset">protac_degradation_predictor.protac_dataset module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset"><code class="docutils literal notranslate"><span class="pre">PROTAC_Dataset</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_smiles_emb_dim"><code class="docutils literal notranslate"><span class="pre">PROTAC_Dataset.get_smiles_emb_dim()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_protein_emb_dim"><code class="docutils literal notranslate"><span class="pre">PROTAC_Dataset.get_protein_emb_dim()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_cell_emb_dim"><code class="docutils literal notranslate"><span class="pre">PROTAC_Dataset.get_cell_emb_dim()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.apply_smote"><code class="docutils literal notranslate"><span class="pre">PROTAC_Dataset.apply_smote()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.fit_scaling"><code class="docutils literal notranslate"><span class="pre">PROTAC_Dataset.fit_scaling()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.apply_scaling"><code class="docutils literal notranslate"><span class="pre">PROTAC_Dataset.apply_scaling()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_numpy_arrays"><code class="docutils literal notranslate"><span class="pre">PROTAC_Dataset.get_numpy_arrays()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.get_datasets"><code class="docutils literal notranslate"><span class="pre">get_datasets()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule"><code class="docutils literal notranslate"><span class="pre">PROTAC_DataModule</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.setup"><code class="docutils literal notranslate"><span class="pre">PROTAC_DataModule.setup()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.train_dataloader"><code class="docutils literal notranslate"><span class="pre">PROTAC_DataModule.train_dataloader()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.val_dataloader"><code class="docutils literal notranslate"><span class="pre">PROTAC_DataModule.val_dataloader()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.test_dataloader"><code class="docutils literal notranslate"><span class="pre">PROTAC_DataModule.test_dataloader()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_random_split_indices"><code class="docutils literal notranslate"><span class="pre">PROTAC_DataModule.get_random_split_indices()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_e3_ligase_split_indices"><code class="docutils literal notranslate"><span class="pre">PROTAC_DataModule.get_e3_ligase_split_indices()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_smiles2fp_and_avg_tanimoto"><code class="docutils literal notranslate"><span class="pre">PROTAC_DataModule.get_smiles2fp_and_avg_tanimoto()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_tanimoto_split_indices"><code class="docutils literal notranslate"><span class="pre">PROTAC_DataModule.get_tanimoto_split_indices()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_target_split_indices"><code class="docutils literal notranslate"><span class="pre">PROTAC_DataModule.get_target_split_indices()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.html#module-protac_degradation_predictor.protac_degradation_predictor">protac_degradation_predictor.protac_degradation_predictor module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_degradation_predictor.get_protac_active_proba"><code class="docutils literal notranslate"><span class="pre">get_protac_active_proba()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_degradation_predictor.is_protac_active"><code class="docutils literal notranslate"><span class="pre">is_protac_active()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.protac_degradation_predictor.get_protac_embedding"><code class="docutils literal notranslate"><span class="pre">get_protac_embedding()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.html#module-protac_degradation_predictor.pytorch_models">protac_degradation_predictor.pytorch_models module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.forward"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.T_destination"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.T_destination</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.add_module"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.add_module()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.apply"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.apply()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.bfloat16"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.bfloat16()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.buffers"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.buffers()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.call_super_init"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.call_super_init</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.children"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.children()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.cpu"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.cpu()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.cuda"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.cuda()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.double"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.double()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.dump_patches"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.dump_patches</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.eval"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.eval()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.extra_repr"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.extra_repr()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.float"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.float()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_buffer"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.get_buffer()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_extra_state"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.get_extra_state()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_parameter"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.get_parameter()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_submodule"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.get_submodule()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.half"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.half()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.ipu"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.ipu()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.load_state_dict"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.load_state_dict()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.modules"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.modules()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_buffers"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.named_buffers()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_children"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.named_children()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_modules"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.named_modules()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_parameters"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.named_parameters()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.parameters"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.parameters()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_backward_hook"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.register_backward_hook()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_buffer"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.register_buffer()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_forward_hook"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.register_forward_hook()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_forward_pre_hook"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.register_forward_pre_hook()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_full_backward_hook"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.register_full_backward_hook()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_full_backward_pre_hook"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.register_full_backward_pre_hook()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_load_state_dict_post_hook"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.register_load_state_dict_post_hook()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_module"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.register_module()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_parameter"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.register_parameter()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_state_dict_pre_hook"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.register_state_dict_pre_hook()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.requires_grad_"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.requires_grad_()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.set_extra_state"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.set_extra_state()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.share_memory"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.share_memory()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.state_dict()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.to"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.to()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.to_empty"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.to_empty()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.train"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.train()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.type"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.type()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.xpu"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.xpu()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.zero_grad"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.zero_grad()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.training"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor.training</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model</span></code></a><ul>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.initialize_scalers"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.initialize_scalers()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.apply_scalers"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.apply_scalers()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.scale_tensor"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.scale_tensor()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.forward"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.forward()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.step"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.step()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.training_step"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.training_step()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.validation_step"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.validation_step()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.test_step"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.test_step()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.configure_optimizers"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.configure_optimizers()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.predict_step"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.predict_step()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.train_dataloader"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.train_dataloader()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.val_dataloader"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.val_dataloader()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.test_dataloader"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.test_dataloader()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.on_save_checkpoint"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.on_save_checkpoint()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.PROTAC_Model.on_load_checkpoint"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model.on_load_checkpoint()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.get_confidence_scores"><code class="docutils literal notranslate"><span class="pre">get_confidence_scores()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.train_model"><code class="docutils literal notranslate"><span class="pre">train_model()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.evaluate_model"><code class="docutils literal notranslate"><span class="pre">evaluate_model()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.pytorch_models.load_model"><code class="docutils literal notranslate"><span class="pre">load_model()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.html#module-protac_degradation_predictor.sklearn_models">protac_degradation_predictor.sklearn_models module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.sklearn_models.train_sklearn_model"><code class="docutils literal notranslate"><span class="pre">train_sklearn_model()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.sklearn_models.suggest_random_forest"><code class="docutils literal notranslate"><span class="pre">suggest_random_forest()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.sklearn_models.suggest_logistic_regression"><code class="docutils literal notranslate"><span class="pre">suggest_logistic_regression()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.sklearn_models.suggest_svc"><code class="docutils literal notranslate"><span class="pre">suggest_svc()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#protac_degradation_predictor.sklearn_models.suggest_gradient_boosting"><code class="docutils literal notranslate"><span class="pre">suggest_gradient_boosting()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.html#module-protac_degradation_predictor">Module contents</a></li>
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diff --git a/docs/_build/html/source/protac_degradation_predictor.html b/docs/_build/html/source/protac_degradation_predictor.html
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+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
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+<li class="toctree-l1 current"><a class="reference internal" href="modules.html">protac_degradation_predictor</a><ul class="current">
+<li class="toctree-l2 current"><a class="current reference internal" href="#">protac_degradation_predictor package</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#subpackages">Subpackages</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="protac_degradation_predictor.optuna.html">protac_degradation_predictor.optuna package</a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#submodules">Submodules</a></li>
+<li class="toctree-l3"><a class="reference internal" href="#module-protac_degradation_predictor.config">protac_degradation_predictor.config module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.config.Config"><code class="docutils literal notranslate"><span class="pre">Config</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-protac_degradation_predictor.data_utils">protac_degradation_predictor.data_utils module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.data_utils.avail_e3_ligases"><code class="docutils literal notranslate"><span class="pre">avail_e3_ligases()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.data_utils.avail_cell_lines"><code class="docutils literal notranslate"><span class="pre">avail_cell_lines()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.data_utils.avail_uniprots"><code class="docutils literal notranslate"><span class="pre">avail_uniprots()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.data_utils.get_fingerprint"><code class="docutils literal notranslate"><span class="pre">get_fingerprint()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.data_utils.is_active"><code class="docutils literal notranslate"><span class="pre">is_active()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.data_utils.load_curated_dataset"><code class="docutils literal notranslate"><span class="pre">load_curated_dataset()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-protac_degradation_predictor.optuna_utils">protac_degradation_predictor.optuna_utils module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.optuna_utils.get_dataframe_stats"><code class="docutils literal notranslate"><span class="pre">get_dataframe_stats()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.optuna_utils.get_majority_vote_metrics"><code class="docutils literal notranslate"><span class="pre">get_majority_vote_metrics()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.optuna_utils.get_suggestion"><code class="docutils literal notranslate"><span class="pre">get_suggestion()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.optuna_utils.pytorch_model_objective"><code class="docutils literal notranslate"><span class="pre">pytorch_model_objective()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.optuna_utils.hyperparameter_tuning_and_training"><code class="docutils literal notranslate"><span class="pre">hyperparameter_tuning_and_training()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-protac_degradation_predictor.optuna_utils_xgboost">protac_degradation_predictor.optuna_utils_xgboost module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.optuna_utils_xgboost.get_confidence_scores"><code class="docutils literal notranslate"><span class="pre">get_confidence_scores()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.optuna_utils_xgboost.train_and_evaluate_xgboost"><code class="docutils literal notranslate"><span class="pre">train_and_evaluate_xgboost()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.optuna_utils_xgboost.xgboost_model_objective"><code class="docutils literal notranslate"><span class="pre">xgboost_model_objective()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.optuna_utils_xgboost.xgboost_hyperparameter_tuning_and_training"><code class="docutils literal notranslate"><span class="pre">xgboost_hyperparameter_tuning_and_training()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-protac_degradation_predictor.protac_dataset">protac_degradation_predictor.protac_dataset module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset"><code class="docutils literal notranslate"><span class="pre">PROTAC_Dataset</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.protac_dataset.get_datasets"><code class="docutils literal notranslate"><span class="pre">get_datasets()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.protac_dataset.PROTAC_DataModule"><code class="docutils literal notranslate"><span class="pre">PROTAC_DataModule</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-protac_degradation_predictor.protac_degradation_predictor">protac_degradation_predictor.protac_degradation_predictor module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.protac_degradation_predictor.get_protac_active_proba"><code class="docutils literal notranslate"><span class="pre">get_protac_active_proba()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.protac_degradation_predictor.is_protac_active"><code class="docutils literal notranslate"><span class="pre">is_protac_active()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.protac_degradation_predictor.get_protac_embedding"><code class="docutils literal notranslate"><span class="pre">get_protac_embedding()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-protac_degradation_predictor.pytorch_models">protac_degradation_predictor.pytorch_models module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor"><code class="docutils literal notranslate"><span class="pre">PROTAC_Predictor</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model"><code class="docutils literal notranslate"><span class="pre">PROTAC_Model</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.pytorch_models.get_confidence_scores"><code class="docutils literal notranslate"><span class="pre">get_confidence_scores()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.pytorch_models.train_model"><code class="docutils literal notranslate"><span class="pre">train_model()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.pytorch_models.evaluate_model"><code class="docutils literal notranslate"><span class="pre">evaluate_model()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.pytorch_models.load_model"><code class="docutils literal notranslate"><span class="pre">load_model()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-protac_degradation_predictor.sklearn_models">protac_degradation_predictor.sklearn_models module</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.sklearn_models.train_sklearn_model"><code class="docutils literal notranslate"><span class="pre">train_sklearn_model()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.sklearn_models.suggest_random_forest"><code class="docutils literal notranslate"><span class="pre">suggest_random_forest()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.sklearn_models.suggest_logistic_regression"><code class="docutils literal notranslate"><span class="pre">suggest_logistic_regression()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.sklearn_models.suggest_svc"><code class="docutils literal notranslate"><span class="pre">suggest_svc()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#protac_degradation_predictor.sklearn_models.suggest_gradient_boosting"><code class="docutils literal notranslate"><span class="pre">suggest_gradient_boosting()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-protac_degradation_predictor">Module contents</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+
+        </div>
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+             
+  <section id="protac-degradation-predictor-package">
+<h1>protac_degradation_predictor package<a class="headerlink" href="#protac-degradation-predictor-package" title="Link to this heading"></a></h1>
+<section id="subpackages">
+<h2>Subpackages<a class="headerlink" href="#subpackages" title="Link to this heading"></a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="protac_degradation_predictor.optuna.html">protac_degradation_predictor.optuna package</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.optuna.html#submodules">Submodules</a></li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.optuna.html#protac-degradation-predictor-optuna-pytorch-models-module">protac_degradation_predictor.optuna.pytorch_models module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.optuna.html#protac-degradation-predictor-optuna-sklearn-models-module">protac_degradation_predictor.optuna.sklearn_models module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.optuna.html#module-protac_degradation_predictor.optuna.utils">protac_degradation_predictor.optuna.utils module</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.optuna.html#protac_degradation_predictor.optuna.utils.get_dataframe_stats"><code class="docutils literal notranslate"><span class="pre">get_dataframe_stats()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.optuna.html#protac_degradation_predictor.optuna.utils.get_majority_vote_metrics"><code class="docutils literal notranslate"><span class="pre">get_majority_vote_metrics()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.optuna.html#protac-degradation-predictor-optuna-xgboost-models-module">protac_degradation_predictor.optuna.xgboost_models module</a></li>
+<li class="toctree-l2"><a class="reference internal" href="protac_degradation_predictor.optuna.html#module-protac_degradation_predictor.optuna">Module contents</a></li>
+</ul>
+</li>
+</ul>
+</div>
+</section>
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
+</section>
+<section id="module-protac_degradation_predictor.config">
+<span id="protac-degradation-predictor-config-module"></span><h2>protac_degradation_predictor.config module<a class="headerlink" href="#module-protac_degradation_predictor.config" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="protac_degradation_predictor.config.Config">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.config.</span></span><span class="sig-name descname"><span class="pre">Config</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">morgan_radius:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">10</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fingerprint_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">protein_embedding_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">1024</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell_embedding_size:</span> <span class="pre">int</span> <span class="pre">=</span> <span class="pre">768</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dmax_threshold:</span> <span class="pre">float</span> <span class="pre">=</span> <span class="pre">0.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pdc50_threshold:</span> <span class="pre">float</span> <span class="pre">=</span> <span class="pre">6.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label:</span> <span class="pre">str</span> <span class="pre">=</span> <span class="pre">'Active</span> <span class="pre">(Dmax</span> <span class="pre">0.6</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pDC50</span> <span class="pre">6.0)'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">e3_ligase2uniprot:</span> <span class="pre">dict</span> <span class="pre">=</span> <span class="pre">&lt;factory&gt;</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.config.Config" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>morgan_radius</strong> (<em>int</em>)</p></li>
+<li><p><strong>fingerprint_size</strong> (<em>int</em>)</p></li>
+<li><p><strong>protein_embedding_size</strong> (<em>int</em>)</p></li>
+<li><p><strong>cell_embedding_size</strong> (<em>int</em>)</p></li>
+<li><p><strong>dmax_threshold</strong> (<em>float</em>)</p></li>
+<li><p><strong>pdc50_threshold</strong> (<em>float</em>)</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>)</p></li>
+<li><p><strong>e3_ligase2uniprot</strong> (<em>dict</em>)</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py attribute">
+<dt class="sig sig-object py" id="protac_degradation_predictor.config.Config.morgan_radius">
+<span class="sig-name descname"><span class="pre">morgan_radius</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">10</span></em><a class="headerlink" href="#protac_degradation_predictor.config.Config.morgan_radius" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="protac_degradation_predictor.config.Config.fingerprint_size">
+<span class="sig-name descname"><span class="pre">fingerprint_size</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">256</span></em><a class="headerlink" href="#protac_degradation_predictor.config.Config.fingerprint_size" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="protac_degradation_predictor.config.Config.protein_embedding_size">
+<span class="sig-name descname"><span class="pre">protein_embedding_size</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">1024</span></em><a class="headerlink" href="#protac_degradation_predictor.config.Config.protein_embedding_size" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="protac_degradation_predictor.config.Config.cell_embedding_size">
+<span class="sig-name descname"><span class="pre">cell_embedding_size</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">int</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">768</span></em><a class="headerlink" href="#protac_degradation_predictor.config.Config.cell_embedding_size" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="protac_degradation_predictor.config.Config.dmax_threshold">
+<span class="sig-name descname"><span class="pre">dmax_threshold</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">float</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">0.6</span></em><a class="headerlink" href="#protac_degradation_predictor.config.Config.dmax_threshold" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="protac_degradation_predictor.config.Config.pdc50_threshold">
+<span class="sig-name descname"><span class="pre">pdc50_threshold</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">float</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">6.0</span></em><a class="headerlink" href="#protac_degradation_predictor.config.Config.pdc50_threshold" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="protac_degradation_predictor.config.Config.active_label">
+<span class="sig-name descname"><span class="pre">active_label</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">str</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">'Active</span> <span class="pre">(Dmax</span> <span class="pre">0.6,</span> <span class="pre">pDC50</span> <span class="pre">6.0)'</span></em><a class="headerlink" href="#protac_degradation_predictor.config.Config.active_label" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="protac_degradation_predictor.config.Config.e3_ligase2uniprot">
+<span class="sig-name descname"><span class="pre">e3_ligase2uniprot</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">dict</span></em><a class="headerlink" href="#protac_degradation_predictor.config.Config.e3_ligase2uniprot" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-protac_degradation_predictor.data_utils">
+<span id="protac-degradation-predictor-data-utils-module"></span><h2>protac_degradation_predictor.data_utils module<a class="headerlink" href="#module-protac_degradation_predictor.data_utils" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.data_utils.avail_e3_ligases">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.data_utils.</span></span><span class="sig-name descname"><span class="pre">avail_e3_ligases</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.data_utils.avail_e3_ligases" title="Link to this definition"></a></dt>
+<dd><p>Get the available E3 ligases.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The available E3 ligases.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>List[str]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.data_utils.avail_cell_lines">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.data_utils.</span></span><span class="sig-name descname"><span class="pre">avail_cell_lines</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.data_utils.avail_cell_lines" title="Link to this definition"></a></dt>
+<dd><p>Get the available cell lines.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The available cell lines.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>List[str]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.data_utils.avail_uniprots">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.data_utils.</span></span><span class="sig-name descname"><span class="pre">avail_uniprots</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.data_utils.avail_uniprots" title="Link to this definition"></a></dt>
+<dd><p>Get the available Uniprot IDs.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The available Uniprot IDs.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>List[str]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.data_utils.get_fingerprint">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.data_utils.</span></span><span class="sig-name descname"><span class="pre">get_fingerprint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">smiles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">morgan_fpgen</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.data_utils.get_fingerprint" title="Link to this definition"></a></dt>
+<dd><p>Get the Morgan fingerprint of a molecule.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>smiles</strong> (<em>str</em>) – The SMILES string of the molecule.</p></li>
+<li><p><strong>morgan_fpgen</strong> – The Morgan fingerprint generator.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The Morgan fingerprint.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>np.ndarray</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.data_utils.is_active">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.data_utils.</span></span><span class="sig-name descname"><span class="pre">is_active</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">DC50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Dmax</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">pDC50_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">7.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">Dmax_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.8</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oring</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.data_utils.is_active" title="Link to this definition"></a></dt>
+<dd><p>Check if a PROTAC is active based on DC50 and Dmax.
+:param DC50: DC50 in nM
+:type DC50: float
+:param Dmax: Dmax in %
+:type Dmax: float</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>True if active, False if inactive, np.nan if either DC50 or Dmax is NaN</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>bool</p>
+</dd>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>DC50</strong> (<em>float</em>)</p></li>
+<li><p><strong>Dmax</strong> (<em>float</em>)</p></li>
+<li><p><strong>pDC50_threshold</strong> (<em>float</em>)</p></li>
+<li><p><strong>Dmax_threshold</strong> (<em>float</em>)</p></li>
+<li><p><strong>oring</strong> (<em>bool</em>)</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.data_utils.load_curated_dataset">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.data_utils.</span></span><span class="sig-name descname"><span class="pre">load_curated_dataset</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.data_utils.load_curated_dataset" title="Link to this definition"></a></dt>
+<dd><p>Load the curated PROTAC dataset as described in the paper: <a class="reference external" href="https://arxiv.org/abs/2406.02637">https://arxiv.org/abs/2406.02637</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>The curated PROTAC dataset.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>pd.DataFrame</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-protac_degradation_predictor.optuna_utils">
+<span id="protac-degradation-predictor-optuna-utils-module"></span><h2>protac_degradation_predictor.optuna_utils module<a class="headerlink" href="#module-protac_degradation_predictor.optuna_utils" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.optuna_utils.get_dataframe_stats">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.optuna_utils.</span></span><span class="sig-name descname"><span class="pre">get_dataframe_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Active'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.optuna_utils.get_dataframe_stats" title="Link to this definition"></a></dt>
+<dd><p>Get some statistics from the dataframes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>train_df</strong> (<em>pd.DataFrame</em>) – The training set.</p></li>
+<li><p><strong>val_df</strong> (<em>pd.DataFrame</em>) – The validation set.</p></li>
+<li><p><strong>test_df</strong> (<em>pd.DataFrame</em>) – The test set.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Dict</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.optuna_utils.get_majority_vote_metrics">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.optuna_utils.</span></span><span class="sig-name descname"><span class="pre">get_majority_vote_metrics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">test_preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Active'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.optuna_utils.get_majority_vote_metrics" title="Link to this definition"></a></dt>
+<dd><p>Get the majority vote metrics.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>test_preds</strong> (<em>List</em>)</p></li>
+<li><p><strong>test_df</strong> (<em>DataFrame</em>)</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Dict</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.optuna_utils.get_suggestion">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.optuna_utils.</span></span><span class="sig-name descname"><span class="pre">get_suggestion</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">trial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hparams_range</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.optuna_utils.get_suggestion" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.optuna_utils.pytorch_model_objective">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.optuna_utils.</span></span><span class="sig-name descname"><span class="pre">pytorch_model_objective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">trial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">protein2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smiles2fp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_val_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hparams_ranges</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast_dev_run</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Active'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disabled_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_logger</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_save_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'logs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cv_model'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_checkpointing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.optuna_utils.pytorch_model_objective" title="Link to this definition"></a></dt>
+<dd><p>Objective function for hyperparameter optimization.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>trial</strong> (<em>optuna.Trial</em>) – The Optuna trial object.</p></li>
+<li><p><strong>train_df</strong> (<em>pd.DataFrame</em>) – The training set.</p></li>
+<li><p><strong>val_df</strong> (<em>pd.DataFrame</em>) – The validation set.</p></li>
+<li><p><strong>hparams_ranges</strong> (<em>List</em><em>[</em><em>Dict</em><em>[</em><em>str</em><em>, </em><em>Any</em><em>]</em><em>]</em>) – NOT IMPLEMENTED YET. Hyperparameters ranges.
+The list must be of a tuple of the type of hparam to suggest (‘int’, ‘float’, or ‘categorical’), and the dictionary must contain the arguments of the corresponding trial.suggest method.</p></li>
+<li><p><strong>fast_dev_run</strong> (<em>bool</em>) – Whether to run a fast development run.</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>) – The active label column.</p></li>
+<li><p><strong>disabled_embeddings</strong> (<em>List</em><em>[</em><em>str</em><em>]</em>) – The list of disabled embeddings.</p></li>
+<li><p><strong>protein2embedding</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>cell2embedding</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>smiles2fp</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>train_val_df</strong> (<em>DataFrame</em>)</p></li>
+<li><p><strong>kf</strong> (<em>StratifiedKFold</em><em> | </em><em>StratifiedGroupKFold</em>)</p></li>
+<li><p><strong>groups</strong> (<em>array</em><em> | </em><em>None</em>)</p></li>
+<li><p><strong>test_df</strong> (<em>DataFrame</em><em> | </em><em>None</em>)</p></li>
+<li><p><strong>max_epochs</strong> (<em>int</em>)</p></li>
+<li><p><strong>use_logger</strong> (<em>bool</em>)</p></li>
+<li><p><strong>logger_save_dir</strong> (<em>str</em>)</p></li>
+<li><p><strong>logger_name</strong> (<em>str</em>)</p></li>
+<li><p><strong>enable_checkpointing</strong> (<em>bool</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.optuna_utils.hyperparameter_tuning_and_training">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.optuna_utils.</span></span><span class="sig-name descname"><span class="pre">hyperparameter_tuning_and_training</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">protein2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smiles2fp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_val_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'standard'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_models_for_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast_dev_run</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_trials</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_save_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'logs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'protac_hparam_search'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Active'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">study_filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_study</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.optuna_utils.hyperparameter_tuning_and_training" title="Link to this definition"></a></dt>
+<dd><p>Hyperparameter tuning and training of a PROTAC model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>protein2embedding</strong> (<em>Dict</em>) – The protein to embedding dictionary.</p></li>
+<li><p><strong>cell2embedding</strong> (<em>Dict</em>) – The cell to embedding dictionary.</p></li>
+<li><p><strong>smiles2fp</strong> (<em>Dict</em>) – The SMILES to fingerprint dictionary.</p></li>
+<li><p><strong>train_val_df</strong> (<em>pd.DataFrame</em>) – The training and validation set.</p></li>
+<li><p><strong>test_df</strong> (<em>pd.DataFrame</em>) – The test set.</p></li>
+<li><p><strong>kf</strong> (<em>StratifiedKFold</em><em> | </em><em>StratifiedGroupKFold</em>) – The KFold object.</p></li>
+<li><p><strong>groups</strong> (<em>np.array</em>) – The groups for the StratifiedGroupKFold.</p></li>
+<li><p><strong>split_type</strong> (<em>str</em>) – The split type of the current study. Used for reporting.</p></li>
+<li><p><strong>n_models_for_test</strong> (<em>int</em>) – The number of models to train for the test set.</p></li>
+<li><p><strong>fast_dev_run</strong> (<em>bool</em>) – Whether to run a fast development run.</p></li>
+<li><p><strong>n_trials</strong> (<em>int</em>) – The number of trials for the hyperparameter search.</p></li>
+<li><p><strong>logger_save_dir</strong> (<em>str</em>) – The logger save directory.</p></li>
+<li><p><strong>logger_name</strong> (<em>str</em>) – The logger name.</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>) – The active label column.</p></li>
+<li><p><strong>max_epochs</strong> (<em>int</em>) – The maximum number of epochs.</p></li>
+<li><p><strong>study_filename</strong> (<em>str</em>) – The study filename.</p></li>
+<li><p><strong>force_study</strong> (<em>bool</em>) – Whether to force the study.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The trained model, the trainer, and the best metrics.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>tuple</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-protac_degradation_predictor.optuna_utils_xgboost">
+<span id="protac-degradation-predictor-optuna-utils-xgboost-module"></span><h2>protac_degradation_predictor.optuna_utils_xgboost module<a class="headerlink" href="#module-protac_degradation_predictor.optuna_utils_xgboost" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.optuna_utils_xgboost.get_confidence_scores">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.optuna_utils_xgboost.</span></span><span class="sig-name descname"><span class="pre">get_confidence_scores</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">y</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_pred</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.optuna_utils_xgboost.get_confidence_scores" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.optuna_utils_xgboost.train_and_evaluate_xgboost">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.optuna_utils_xgboost.</span></span><span class="sig-name descname"><span class="pre">train_and_evaluate_xgboost</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">protein2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smiles2fp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Active'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_boost_round</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle_train_data</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.optuna_utils_xgboost.train_and_evaluate_xgboost" title="Link to this definition"></a></dt>
+<dd><p>Train and evaluate an XGBoost model with the given parameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>train_df</strong> (<em>pd.DataFrame</em>) – The training and validation data.</p></li>
+<li><p><strong>test_df</strong> (<em>pd.DataFrame</em>) – The test data.</p></li>
+<li><p><strong>params</strong> (<em>dict</em>) – Hyperparameters for the XGBoost model.</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>) – The active label column.</p></li>
+<li><p><strong>num_boost_round</strong> (<em>int</em>) – Maximum number of epochs.</p></li>
+<li><p><strong>protein2embedding</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>cell2embedding</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>smiles2fp</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>val_df</strong> (<em>DataFrame</em>)</p></li>
+<li><p><strong>shuffle_train_data</strong> (<em>bool</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The trained model, test predictions, and metrics.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>tuple</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.optuna_utils_xgboost.xgboost_model_objective">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.optuna_utils_xgboost.</span></span><span class="sig-name descname"><span class="pre">xgboost_model_objective</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">trial</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">protein2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smiles2fp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_val_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Active'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_boost_round</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.optuna_utils_xgboost.xgboost_model_objective" title="Link to this definition"></a></dt>
+<dd><p>Objective function for hyperparameter optimization with XGBoost.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>trial</strong> (<em>optuna.Trial</em>) – The Optuna trial object.</p></li>
+<li><p><strong>train_val_df</strong> (<em>pd.DataFrame</em>) – The training and validation data.</p></li>
+<li><p><strong>kf</strong> (<em>StratifiedKFold</em>) – Stratified K-Folds cross-validator.</p></li>
+<li><p><strong>test_df</strong> (<em>Optional</em><em>[</em><em>pd.DataFrame</em><em>]</em>) – The test data.</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>) – The active label column.</p></li>
+<li><p><strong>num_boost_round</strong> (<em>int</em>) – Maximum number of epochs.</p></li>
+<li><p><strong>model_name</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – The prefix name of the CV models to save, if supplied. Used as: <cite>f”{model_name}_fold_{k}.json”</cite></p></li>
+<li><p><strong>protein2embedding</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>cell2embedding</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>smiles2fp</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>groups</strong> (<em>array</em><em> | </em><em>None</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>float</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.optuna_utils_xgboost.xgboost_hyperparameter_tuning_and_training">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.optuna_utils_xgboost.</span></span><span class="sig-name descname"><span class="pre">xgboost_hyperparameter_tuning_and_training</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">protein2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smiles2fp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_val_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">kf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">groups</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">split_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'random'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_models_for_test</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_trials</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Active'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_boost_round</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">study_filename</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">force_study</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.optuna_utils_xgboost.xgboost_hyperparameter_tuning_and_training" title="Link to this definition"></a></dt>
+<dd><p>Hyperparameter tuning and training of an XGBoost model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>train_val_df</strong> (<em>pd.DataFrame</em>) – The training and validation data.</p></li>
+<li><p><strong>test_df</strong> (<em>pd.DataFrame</em>) – The test data.</p></li>
+<li><p><strong>kf</strong> (<em>StratifiedKFold</em>) – Stratified K-Folds cross-validator.</p></li>
+<li><p><strong>groups</strong> (<em>Optional</em><em>[</em><em>np.array</em><em>]</em>) – Group labels for the samples used while splitting the dataset into train/test set.</p></li>
+<li><p><strong>split_type</strong> (<em>str</em>) – Type of the data split. Used for reporting information.</p></li>
+<li><p><strong>n_models_for_test</strong> (<em>int</em>) – Number of models to train for testing.</p></li>
+<li><p><strong>fast_dev_run</strong> (<em>bool</em>) – Whether to run a fast development run.</p></li>
+<li><p><strong>n_trials</strong> (<em>int</em>) – Number of trials for hyperparameter optimization.</p></li>
+<li><p><strong>logger_save_dir</strong> (<em>str</em>) – Directory to save logs.</p></li>
+<li><p><strong>logger_name</strong> (<em>str</em>) – Name of the logger.</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>) – The active label column.</p></li>
+<li><p><strong>num_boost_round</strong> (<em>int</em>) – Maximum number of epochs.</p></li>
+<li><p><strong>study_filename</strong> (<em>Optional</em><em>[</em><em>str</em><em>]</em>) – File name to save/load the Optuna study.</p></li>
+<li><p><strong>force_study</strong> (<em>bool</em>) – Whether to force the study optimization even if the study file exists.</p></li>
+<li><p><strong>protein2embedding</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>cell2embedding</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>smiles2fp</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>model_name</strong> (<em>str</em><em> | </em><em>None</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>A dictionary containing reports from the CV and test.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>dict</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-protac_degradation_predictor.protac_dataset">
+<span id="protac-degradation-predictor-protac-dataset-module"></span><h2>protac_degradation_predictor.protac_dataset module<a class="headerlink" href="#module-protac_degradation_predictor.protac_dataset" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_Dataset">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.protac_dataset.</span></span><span class="sig-name descname"><span class="pre">PROTAC_Dataset</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">protac_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">protein2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smiles2fp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_smote</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">oversampler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Active'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disabled_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_single_scaler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle_embedding_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Dataset</span></code></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>protac_df</strong> (<em>DataFrame</em>)</p></li>
+<li><p><strong>protein2embedding</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>ndarray</em><em>]</em>)</p></li>
+<li><p><strong>cell2embedding</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>ndarray</em><em>]</em>)</p></li>
+<li><p><strong>smiles2fp</strong> (<em>Dict</em><em>[</em><em>str</em><em>, </em><em>ndarray</em><em>]</em>)</p></li>
+<li><p><strong>use_smote</strong> (<em>bool</em>)</p></li>
+<li><p><strong>oversampler</strong> (<em>SMOTE</em><em> | </em><em>ADASYN</em><em> | </em><em>None</em>)</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>)</p></li>
+<li><p><strong>disabled_embeddings</strong> (<em>List</em><em>[</em><em>Literal</em><em>[</em><em>'smiles'</em><em>, </em><em>'poi'</em><em>, </em><em>'e3'</em><em>, </em><em>'cell'</em><em>]</em><em>]</em>)</p></li>
+<li><p><strong>scaler</strong> (<em>StandardScaler</em><em> | </em><em>Dict</em><em>[</em><em>str</em><em>, </em><em>StandardScaler</em><em>] </em><em>| </em><em>None</em>)</p></li>
+<li><p><strong>use_single_scaler</strong> (<em>bool</em><em> | </em><em>None</em>)</p></li>
+<li><p><strong>shuffle_embedding_prob</strong> (<em>float</em>)</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_smiles_emb_dim">
+<span class="sig-name descname"><span class="pre">get_smiles_emb_dim</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_smiles_emb_dim" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_protein_emb_dim">
+<span class="sig-name descname"><span class="pre">get_protein_emb_dim</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_protein_emb_dim" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_cell_emb_dim">
+<span class="sig-name descname"><span class="pre">get_cell_emb_dim</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_cell_emb_dim" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_Dataset.apply_smote">
+<span class="sig-name descname"><span class="pre">apply_smote</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.apply_smote" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_Dataset.fit_scaling">
+<span class="sig-name descname"><span class="pre">fit_scaling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">use_single_scaler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">scaler_kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.fit_scaling" title="Link to this definition"></a></dt>
+<dd><p>Fit the scalers for the data and save them in the dataset class.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>use_single_scaler</strong> (<em>bool</em>) – Whether to use a single scaler for all features.</p></li>
+<li><p><strong>scaler_kwargs</strong> – Keyword arguments for the StandardScaler.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The fitted scalers.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>dict</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_Dataset.apply_scaling">
+<span class="sig-name descname"><span class="pre">apply_scaling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">scalers</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_single_scaler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.apply_scaling" title="Link to this definition"></a></dt>
+<dd><p>Apply scaling to the data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>scalers</strong> (<em>dict</em>) – The scalers for each feature.</p></li>
+<li><p><strong>use_single_scaler</strong> (<em>bool</em>) – Whether to use a single scaler for all features.</p></li>
+</ul>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_numpy_arrays">
+<span class="sig-name descname"><span class="pre">get_numpy_arrays</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">component</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset.get_numpy_arrays" title="Link to this definition"></a></dt>
+<dd><p>Get the numpy arrays for the dataset.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>component</strong> (<em>str</em>) – The component to get the numpy arrays for. Defaults to None, i.e., get a single stacked array.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The numpy arrays for the dataset. The first element is the input array, and the second element is the output array.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>tuple</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.get_datasets">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.protac_dataset.</span></span><span class="sig-name descname"><span class="pre">get_datasets</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">protein2embedding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell2embedding</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smiles2fp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smote_k_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Active'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disabled_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_single_scaler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle_embedding_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.get_datasets" title="Link to this definition"></a></dt>
+<dd><p>Get the datasets for training the PROTAC model.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>train_df</strong> (<em>pd.DataFrame</em>) – The training data.</p></li>
+<li><p><strong>val_df</strong> (<em>pd.DataFrame</em>) – The validation data.</p></li>
+<li><p><strong>test_df</strong> (<em>pd.DataFrame</em>) – The test data.</p></li>
+<li><p><strong>protein2embedding</strong> (<em>dict</em>) – Dictionary of protein embeddings.</p></li>
+<li><p><strong>cell2embedding</strong> (<em>dict</em>) – Dictionary of cell line embeddings.</p></li>
+<li><p><strong>smiles2fp</strong> (<em>dict</em>) – Dictionary of SMILES to fingerprint.</p></li>
+<li><p><strong>use_smote</strong> (<em>bool</em>) – Whether to use SMOTE for oversampling.</p></li>
+<li><p><strong>smote_k_neighbors</strong> (<em>int</em>) – The number of neighbors to use for SMOTE.</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>) – The active label column.</p></li>
+<li><p><strong>disabled_embeddings</strong> (<em>list</em>) – The list of embeddings to disable.</p></li>
+<li><p><strong>scaler</strong> (<em>StandardScaler</em><em> | </em><em>dict</em>) – The scaler to use for the embeddings.</p></li>
+<li><p><strong>use_single_scaler</strong> (<em>bool</em>) – Whether to use a single scaler for all features.</p></li>
+<li><p><strong>apply_scaling</strong> (<em>bool</em>) – Whether to apply scaling to the data now. Defaults to False (the Pytorch Lightning model does that).</p></li>
+<li><p><strong>shuffle_embedding_prob</strong> (<em>float</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Tuple</em>[<a class="reference internal" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset" title="protac_degradation_predictor.protac_dataset.PROTAC_Dataset"><em>PROTAC_Dataset</em></a>, <a class="reference internal" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset" title="protac_degradation_predictor.protac_dataset.PROTAC_Dataset"><em>PROTAC_Dataset</em></a>, <a class="reference internal" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset" title="protac_degradation_predictor.protac_dataset.PROTAC_Dataset"><em>PROTAC_Dataset</em></a> | None]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_DataModule">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.protac_dataset.</span></span><span class="sig-name descname"><span class="pre">PROTAC_DataModule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_DataModule" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">LightningDataModule</span></code></p>
+<p>PyTorch Lightning DataModule for the PROTAC dataset.</p>
+<p>TODO: Work in progress. It would be nice to wrap all information into a
+single class, but it is not clear how to do it yet due to cross-validation
+and the need to split the data into training, validation, and test sets
+accordingly.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>protac_csv_filepath</strong> (<em>str</em>) – The path to the PROTAC CSV file.</p></li>
+<li><p><strong>protein2embedding_filepath</strong> (<em>str</em>) – The path to the protein to embedding dictionary.</p></li>
+<li><p><strong>cell2embedding_filepath</strong> (<em>str</em>) – The path to the cell line to embedding dictionary.</p></li>
+<li><p><strong>pDC50_threshold</strong> (<em>float</em>) – The threshold for the pDC50 value to consider a PROTAC active.</p></li>
+<li><p><strong>Dmax_threshold</strong> (<em>float</em>) – The threshold for the Dmax value to consider a PROTAC active.</p></li>
+<li><p><strong>use_smote</strong> (<em>bool</em>) – Whether to use SMOTE for oversampling.</p></li>
+<li><p><strong>smote_k_neighbors</strong> (<em>int</em>) – The number of neighbors to use for SMOTE.</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>) – The column containing the active/inactive information.</p></li>
+<li><p><strong>disabled_embeddings</strong> (<em>list</em>) – The list of embeddings to disable.</p></li>
+<li><p><strong>scaler</strong> (<em>StandardScaler</em><em> | </em><em>dict</em>) – The scaler to use for the embeddings.</p></li>
+<li><p><strong>use_single_scaler</strong> (<em>bool</em>) – Whether to use a single scaler for all features.</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_DataModule.setup">
+<span class="sig-name descname"><span class="pre">setup</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">stage</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.setup" title="Link to this definition"></a></dt>
+<dd><dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>stage</strong> (<em>str</em>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_DataModule.train_dataloader">
+<span class="sig-name descname"><span class="pre">train_dataloader</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.train_dataloader" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_DataModule.val_dataloader">
+<span class="sig-name descname"><span class="pre">val_dataloader</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.val_dataloader" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_DataModule.test_dataloader">
+<span class="sig-name descname"><span class="pre">test_dataloader</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.test_dataloader" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_random_split_indices">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_random_split_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">active_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_split</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_random_split_indices" title="Link to this definition"></a></dt>
+<dd><p>Get the indices of the test set using a random split.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>active_df</strong> (<em>pd.DataFrame</em>) – The DataFrame containing the active PROTACs.</p></li>
+<li><p><strong>test_split</strong> (<em>float</em>) – The percentage of the active PROTACs to use as the test set.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The indices of the test set.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>pd.Index</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_e3_ligase_split_indices">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_e3_ligase_split_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">active_df</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_e3_ligase_split_indices" title="Link to this definition"></a></dt>
+<dd><p>Get the indices of the test set using the E3 ligase split.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>active_df</strong> (<em>pd.DataFrame</em>) – The DataFrame containing the active PROTACs.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The indices of the test set.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>pd.Index</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_smiles2fp_and_avg_tanimoto">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_smiles2fp_and_avg_tanimoto</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">protac_df</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_smiles2fp_and_avg_tanimoto" title="Link to this definition"></a></dt>
+<dd><p>Get the SMILES to fingerprint dictionary and the average Tanimoto similarity.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>protac_df</strong> (<em>pd.DataFrame</em>) – The DataFrame containing the PROTACs.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The SMILES to fingerprint dictionary and the average Tanimoto similarity.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>tuple</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_tanimoto_split_indices">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_tanimoto_split_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">active_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_split</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_bins_tanimoto</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">200</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_tanimoto_split_indices" title="Link to this definition"></a></dt>
+<dd><p>Get the indices of the test set using the Tanimoto-based split.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>active_df</strong> (<em>pd.DataFrame</em>) – The DataFrame containing the active PROTACs.</p></li>
+<li><p><strong>n_bins_tanimoto</strong> (<em>int</em>) – The number of bins to use for the Tanimoto similarity.</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>)</p></li>
+<li><p><strong>test_split</strong> (<em>float</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The indices of the test set.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>pd.Index</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_target_split_indices">
+<em class="property"><span class="pre">static</span><span class="w"> </span></em><span class="sig-name descname"><span class="pre">get_target_split_indices</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">active_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_split</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_dataset.PROTAC_DataModule.get_target_split_indices" title="Link to this definition"></a></dt>
+<dd><p>Get the indices of the test set using the target-based split.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>active_df</strong> (<em>pd.DataFrame</em>) – The DataFrame containing the active PROTACs.</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>) – The column containing the active/inactive information.</p></li>
+<li><p><strong>test_split</strong> (<em>float</em>) – The percentage of the active PROTACs to use as the test set.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The indices of the test set.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>pd.Index</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+</section>
+<section id="module-protac_degradation_predictor.protac_degradation_predictor">
+<span id="protac-degradation-predictor-protac-degradation-predictor-module"></span><h2>protac_degradation_predictor.protac_degradation_predictor module<a class="headerlink" href="#module-protac_degradation_predictor.protac_degradation_predictor" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_degradation_predictor.get_protac_active_proba">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.protac_degradation_predictor.</span></span><span class="sig-name descname"><span class="pre">get_protac_active_proba</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">protac_smiles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">e3_ligase</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_uniprot</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell_line</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_models_from_cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_xgboost_models</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">study_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'standard'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_degradation_predictor.get_protac_active_proba" title="Link to this definition"></a></dt>
+<dd><p>Predict the probability of a PROTAC being active.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>protac_smiles</strong> (<em>str</em><em> | </em><em>List</em><em>[</em><em>str</em><em>]</em>) – The SMILES of the PROTAC.</p></li>
+<li><p><strong>e3_ligase</strong> (<em>str</em><em> | </em><em>List</em><em>[</em><em>str</em><em>]</em>) – The Uniprot ID of the E3 ligase.</p></li>
+<li><p><strong>target_uniprot</strong> (<em>str</em><em> | </em><em>List</em><em>[</em><em>str</em><em>]</em>) – The Uniprot ID of the target protein.</p></li>
+<li><p><strong>cell_line</strong> (<em>str</em><em> | </em><em>List</em><em>[</em><em>str</em><em>]</em>) – The cell line identifier.</p></li>
+<li><p><strong>device</strong> (<em>str</em>) – The device to run the model on.</p></li>
+<li><p><strong>use_models_from_cv</strong> (<em>bool</em>) – Whether to use the models from cross-validation.</p></li>
+<li><p><strong>use_xgb_models</strong> (<em>bool</em>) – Whether to use the XGBoost models.</p></li>
+<li><p><strong>study_type</strong> (<em>str</em>) – Use models trained on the specified study. Options are ‘standard’, ‘similarity’, ‘target’.</p></li>
+<li><p><strong>use_xgboost_models</strong> (<em>bool</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The predictions of the model. The dictionary contains the following: ‘preds’, ‘mean’, ‘majority_vote’. The ‘preds’ key contains the predictions of all models with shape: (n_models, batch_size), ‘mean’ contains the mean prediction, and ‘majority_vote’ contains the majority vote.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Dict[str, np.ndarray]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_degradation_predictor.is_protac_active">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.protac_degradation_predictor.</span></span><span class="sig-name descname"><span class="pre">is_protac_active</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">protac_smiles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">e3_ligase</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_uniprot</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell_line</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">proba_threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_majority_vote</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_models_from_cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_xgboost_models</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">study_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'standard'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_degradation_predictor.is_protac_active" title="Link to this definition"></a></dt>
+<dd><p>Predict whether a PROTAC is active or not.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>protac_smiles</strong> (<em>str</em>) – The SMILES of the PROTAC.</p></li>
+<li><p><strong>e3_ligase</strong> (<em>str</em>) – The Uniprot ID of the E3 ligase.</p></li>
+<li><p><strong>target_uniprot</strong> (<em>str</em>) – The Uniprot ID of the target protein.</p></li>
+<li><p><strong>cell_line</strong> (<em>str</em>) – The cell line identifier.</p></li>
+<li><p><strong>device</strong> (<em>str</em>) – The device to run the model on.</p></li>
+<li><p><strong>proba_threshold</strong> (<em>float</em>) – The probability threshold.</p></li>
+<li><p><strong>use_majority_vote</strong> (<em>bool</em>) – Whether to use the majority vote.</p></li>
+<li><p><strong>use_models_from_cv</strong> (<em>bool</em>) – Whether to use the models from cross-validation.</p></li>
+<li><p><strong>use_xgboost_models</strong> (<em>bool</em>) – Whether to use the XGBoost models.</p></li>
+<li><p><strong>study_type</strong> (<em>str</em>) – Use models trained on the specified study. Options are ‘standard’, ‘similarity’, ‘target’.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Whether the PROTAC is active or not.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>bool</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.protac_degradation_predictor.get_protac_embedding">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.protac_degradation_predictor.</span></span><span class="sig-name descname"><span class="pre">get_protac_embedding</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">protac_smiles</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">e3_ligase</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">target_uniprot</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell_line</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'cpu'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_models_from_cv</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">study_type</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'standard'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.protac_degradation_predictor.get_protac_embedding" title="Link to this definition"></a></dt>
+<dd><p>Get the embeddings of a PROTAC or a list of PROTACs.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>protac_smiles</strong> (<em>str</em><em> | </em><em>List</em><em>[</em><em>str</em><em>]</em>) – The SMILES of the PROTAC.</p></li>
+<li><p><strong>e3_ligase</strong> (<em>str</em><em> | </em><em>List</em><em>[</em><em>str</em><em>]</em>) – The Uniprot ID of the E3 ligase.</p></li>
+<li><p><strong>target_uniprot</strong> (<em>str</em><em> | </em><em>List</em><em>[</em><em>str</em><em>]</em>) – The Uniprot ID of the target protein.</p></li>
+<li><p><strong>cell_line</strong> (<em>str</em><em> | </em><em>List</em><em>[</em><em>str</em><em>]</em>) – The cell line identifier.</p></li>
+<li><p><strong>device</strong> (<em>str</em>) – The device to run the model on.</p></li>
+<li><p><strong>use_models_from_cv</strong> (<em>bool</em>) – Whether to use the models from cross-validation.</p></li>
+<li><p><strong>study_type</strong> (<em>str</em>) – Use models trained on the specified study. Options are ‘standard’, ‘similarity’, ‘target’.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The embeddings of the given PROTAC. Each key is the name of the model and the value is the embedding, of shape: (batch_size, model_hidden_size). NOTE: Each model has its own hidden size, so the embeddings might have different dimensions.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Dict[str, np.ndarray]</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-protac_degradation_predictor.pytorch_models">
+<span id="protac-degradation-predictor-pytorch-models-module"></span><h2>protac_degradation_predictor.pytorch_models module<a class="headerlink" href="#module-protac_degradation_predictor.pytorch_models" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.pytorch_models.</span></span><span class="sig-name descname"><span class="pre">PROTAC_Predictor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hidden_dim</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smiles_emb_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">256</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">poi_emb_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1024</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">e3_emb_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1024</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell_emb_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">768</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">join_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sum'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_batch_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disabled_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>hidden_dim</strong> (<em>int</em>)</p></li>
+<li><p><strong>smiles_emb_dim</strong> (<em>int</em>)</p></li>
+<li><p><strong>poi_emb_dim</strong> (<em>int</em>)</p></li>
+<li><p><strong>e3_emb_dim</strong> (<em>int</em>)</p></li>
+<li><p><strong>cell_emb_dim</strong> (<em>int</em>)</p></li>
+<li><p><strong>dropout</strong> (<em>float</em>)</p></li>
+<li><p><strong>join_embeddings</strong> (<em>Literal</em><em>[</em><em>'beginning'</em><em>, </em><em>'concat'</em><em>, </em><em>'sum'</em><em>]</em>)</p></li>
+<li><p><strong>use_batch_norm</strong> (<em>bool</em>)</p></li>
+<li><p><strong>disabled_embeddings</strong> (<em>List</em><em>[</em><em>Literal</em><em>[</em><em>'smiles'</em><em>, </em><em>'poi'</em><em>, </em><em>'e3'</em><em>, </em><em>'cell'</em><em>]</em><em>]</em>)</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">poi_emb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">e3_emb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell_emb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smiles_emb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.forward" title="Link to this definition"></a></dt>
+<dd><p>Defines the computation performed at every call.</p>
+<p>Should be overridden by all subclasses.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Although the recipe for forward pass needs to be defined within
+this function, one should call the <code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> instance afterwards
+instead of this since the former takes care of running the
+registered hooks while the latter silently ignores them.</p>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.T_destination">
+<span class="sig-name descname"><span class="pre">T_destination</span></span><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">~T_destination</span></em><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.T_destination" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.add_module">
+<span class="sig-name descname"><span class="pre">add_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.add_module" title="Link to this definition"></a></dt>
+<dd><p>Adds a child module to the current module.</p>
+<p>The module can be accessed as an attribute using the given name.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>name</strong> (<em>str</em>) – name of the child module. The child module can be
+accessed from this module using the given name</p></li>
+<li><p><strong>module</strong> (<em>Module</em>) – child module to be added to the module.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.apply">
+<span class="sig-name descname"><span class="pre">apply</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fn</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.apply" title="Link to this definition"></a></dt>
+<dd><p>Applies <code class="docutils literal notranslate"><span class="pre">fn</span></code> recursively to every submodule (as returned by <code class="docutils literal notranslate"><span class="pre">.children()</span></code>)
+as well as self. Typical use includes initializing the parameters of a model
+(see also <span class="xref std std-ref">nn-init-doc</span>).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fn</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">Module</span></code> -&gt; None) – function to be applied to each submodule</p></li>
+<li><p><strong>self</strong> (<em>T</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>self</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Module</p>
+</dd>
+</dl>
+<p>Example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="nd">@torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">()</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">def</span> <span class="nf">init_weights</span><span class="p">(</span><span class="n">m</span><span class="p">):</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">m</span><span class="p">)</span> <span class="o">==</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">:</span>
+<span class="gp">&gt;&gt;&gt; </span>        <span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="mf">1.0</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span>        <span class="nb">print</span><span class="p">(</span><span class="n">m</span><span class="o">.</span><span class="n">weight</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">),</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">net</span><span class="o">.</span><span class="n">apply</span><span class="p">(</span><span class="n">init_weights</span><span class="p">)</span>
+<span class="go">Linear(in_features=2, out_features=2, bias=True)</span>
+<span class="go">Parameter containing:</span>
+<span class="go">tensor([[1., 1.],</span>
+<span class="go">        [1., 1.]], requires_grad=True)</span>
+<span class="go">Linear(in_features=2, out_features=2, bias=True)</span>
+<span class="go">Parameter containing:</span>
+<span class="go">tensor([[1., 1.],</span>
+<span class="go">        [1., 1.]], requires_grad=True)</span>
+<span class="go">Sequential(</span>
+<span class="go">  (0): Linear(in_features=2, out_features=2, bias=True)</span>
+<span class="go">  (1): Linear(in_features=2, out_features=2, bias=True)</span>
+<span class="go">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.bfloat16">
+<span class="sig-name descname"><span class="pre">bfloat16</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.bfloat16" title="Link to this definition"></a></dt>
+<dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">bfloat16</span></code> datatype.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method modifies the module in-place.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>self</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Module</p>
+</dd>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>self</strong> (<em>T</em>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.buffers">
+<span class="sig-name descname"><span class="pre">buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.buffers" title="Link to this definition"></a></dt>
+<dd><p>Returns an iterator over module buffers.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>recurse</strong> (<em>bool</em>) – if True, then yields buffers of this module
+and all submodules. Otherwise, yields only buffers that
+are direct members of this module.</p>
+</dd>
+<dt class="field-even">Yields<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>torch.Tensor</em> – module buffer</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><em>Iterator</em>[<em>Tensor</em>]</p>
+</dd>
+</dl>
+<p>Example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +SKIP(&quot;undefined vars&quot;)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">buf</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">buffers</span><span class="p">():</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">buf</span><span class="p">),</span> <span class="n">buf</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
+<span class="go">&lt;class &#39;torch.Tensor&#39;&gt; (20L,)</span>
+<span class="go">&lt;class &#39;torch.Tensor&#39;&gt; (20L, 1L, 5L, 5L)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.call_super_init">
+<span class="sig-name descname"><span class="pre">call_super_init</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.call_super_init" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.children">
+<span class="sig-name descname"><span class="pre">children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.children" title="Link to this definition"></a></dt>
+<dd><p>Returns an iterator over immediate children modules.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Yields<span class="colon">:</span></dt>
+<dd class="field-odd"><p><em>Module</em> – a child module</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Iterator</em>[<em>Module</em>]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.cpu">
+<span class="sig-name descname"><span class="pre">cpu</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.cpu" title="Link to this definition"></a></dt>
+<dd><p>Moves all model parameters and buffers to the CPU.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method modifies the module in-place.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>self</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Module</p>
+</dd>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>self</strong> (<em>T</em>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.cuda">
+<span class="sig-name descname"><span class="pre">cuda</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.cuda" title="Link to this definition"></a></dt>
+<dd><p>Moves all model parameters and buffers to the GPU.</p>
+<p>This also makes associated parameters and buffers different objects. So
+it should be called before constructing optimizer if the module will
+live on GPU while being optimized.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method modifies the module in-place.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>device</strong> (<em>int</em><em>, </em><em>optional</em>) – if specified, all parameters will be
+copied to that device</p></li>
+<li><p><strong>self</strong> (<em>T</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>self</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Module</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.double">
+<span class="sig-name descname"><span class="pre">double</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.double" title="Link to this definition"></a></dt>
+<dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">double</span></code> datatype.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method modifies the module in-place.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>self</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Module</p>
+</dd>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>self</strong> (<em>T</em>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.dump_patches">
+<span class="sig-name descname"><span class="pre">dump_patches</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><em class="property"><span class="w"> </span><span class="p"><span class="pre">=</span></span><span class="w"> </span><span class="pre">False</span></em><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.dump_patches" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.eval">
+<span class="sig-name descname"><span class="pre">eval</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.eval" title="Link to this definition"></a></dt>
+<dd><p>Sets the module in evaluation mode.</p>
+<p>This has any effect only on certain modules. See documentations of
+particular modules for details of their behaviors in training/evaluation
+mode, if they are affected, e.g. <code class="xref py py-class docutils literal notranslate"><span class="pre">Dropout</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm</span></code>,
+etc.</p>
+<p>This is equivalent with <code class="xref py py-meth docutils literal notranslate"><span class="pre">self.train(False)</span></code>.</p>
+<p>See <span class="xref std std-ref">locally-disable-grad-doc</span> for a comparison between
+<cite>.eval()</cite> and several similar mechanisms that may be confused with it.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>self</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Module</p>
+</dd>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>self</strong> (<em>T</em>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.extra_repr">
+<span class="sig-name descname"><span class="pre">extra_repr</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.extra_repr" title="Link to this definition"></a></dt>
+<dd><p>Set the extra representation of the module</p>
+<p>To print customized extra information, you should re-implement
+this method in your own modules. Both single-line and multi-line
+strings are acceptable.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>str</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.float">
+<span class="sig-name descname"><span class="pre">float</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.float" title="Link to this definition"></a></dt>
+<dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">float</span></code> datatype.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method modifies the module in-place.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>self</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Module</p>
+</dd>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>self</strong> (<em>T</em>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_buffer">
+<span class="sig-name descname"><span class="pre">get_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_buffer" title="Link to this definition"></a></dt>
+<dd><p>Returns the buffer given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists,
+otherwise throws an error.</p>
+<p>See the docstring for <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for a more detailed
+explanation of this method’s functionality as well as how to
+correctly specify <code class="docutils literal notranslate"><span class="pre">target</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>target</strong> (<em>str</em>) – The fully-qualified string name of the buffer
+to look for. (See <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for how to specify a
+fully-qualified string.)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The buffer referenced by <code class="docutils literal notranslate"><span class="pre">target</span></code></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.Tensor</p>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>AttributeError</strong> – If the target string references an invalid
+    path or resolves to something that is not a
+    buffer</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_extra_state">
+<span class="sig-name descname"><span class="pre">get_extra_state</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_extra_state" title="Link to this definition"></a></dt>
+<dd><p>Returns any extra state to include in the module’s state_dict.
+Implement this and a corresponding <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.set_extra_state" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.set_extra_state"><code class="xref py py-func docutils literal notranslate"><span class="pre">set_extra_state()</span></code></a> for your module
+if you need to store extra state. This function is called when building the
+module’s <cite>state_dict()</cite>.</p>
+<p>Note that extra state should be picklable to ensure working serialization
+of the state_dict. We only provide provide backwards compatibility guarantees
+for serializing Tensors; other objects may break backwards compatibility if
+their serialized pickled form changes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Any extra state to store in the module’s state_dict</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>object</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_parameter">
+<span class="sig-name descname"><span class="pre">get_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_parameter" title="Link to this definition"></a></dt>
+<dd><p>Returns the parameter given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists,
+otherwise throws an error.</p>
+<p>See the docstring for <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for a more detailed
+explanation of this method’s functionality as well as how to
+correctly specify <code class="docutils literal notranslate"><span class="pre">target</span></code>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>target</strong> (<em>str</em>) – The fully-qualified string name of the Parameter
+to look for. (See <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> for how to specify a
+fully-qualified string.)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The Parameter referenced by <code class="docutils literal notranslate"><span class="pre">target</span></code></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.nn.Parameter</p>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>AttributeError</strong> – If the target string references an invalid
+    path or resolves to something that is not an
+    <code class="docutils literal notranslate"><span class="pre">nn.Parameter</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_submodule">
+<span class="sig-name descname"><span class="pre">get_submodule</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">target</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_submodule" title="Link to this definition"></a></dt>
+<dd><p>Returns the submodule given by <code class="docutils literal notranslate"><span class="pre">target</span></code> if it exists,
+otherwise throws an error.</p>
+<p>For example, let’s say you have an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> <code class="docutils literal notranslate"><span class="pre">A</span></code> that
+looks like this:</p>
+<div class="highlight-text notranslate"><div class="highlight"><pre><span></span>A(
+    (net_b): Module(
+        (net_c): Module(
+            (conv): Conv2d(16, 33, kernel_size=(3, 3), stride=(2, 2))
+        )
+        (linear): Linear(in_features=100, out_features=200, bias=True)
+    )
+)
+</pre></div>
+</div>
+<p>(The diagram shows an <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code> <code class="docutils literal notranslate"><span class="pre">A</span></code>. <code class="docutils literal notranslate"><span class="pre">A</span></code> has a nested
+submodule <code class="docutils literal notranslate"><span class="pre">net_b</span></code>, which itself has two submodules <code class="docutils literal notranslate"><span class="pre">net_c</span></code>
+and <code class="docutils literal notranslate"><span class="pre">linear</span></code>. <code class="docutils literal notranslate"><span class="pre">net_c</span></code> then has a submodule <code class="docutils literal notranslate"><span class="pre">conv</span></code>.)</p>
+<p>To check whether or not we have the <code class="docutils literal notranslate"><span class="pre">linear</span></code> submodule, we
+would call <code class="docutils literal notranslate"><span class="pre">get_submodule(&quot;net_b.linear&quot;)</span></code>. To check whether
+we have the <code class="docutils literal notranslate"><span class="pre">conv</span></code> submodule, we would call
+<code class="docutils literal notranslate"><span class="pre">get_submodule(&quot;net_b.net_c.conv&quot;)</span></code>.</p>
+<p>The runtime of <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> is bounded by the degree
+of module nesting in <code class="docutils literal notranslate"><span class="pre">target</span></code>. A query against
+<code class="docutils literal notranslate"><span class="pre">named_modules</span></code> achieves the same result, but it is O(N) in
+the number of transitive modules. So, for a simple check to see
+if some submodule exists, <code class="docutils literal notranslate"><span class="pre">get_submodule</span></code> should always be
+used.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>target</strong> (<em>str</em>) – The fully-qualified string name of the submodule
+to look for. (See above example for how to specify a
+fully-qualified string.)</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The submodule referenced by <code class="docutils literal notranslate"><span class="pre">target</span></code></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.nn.Module</p>
+</dd>
+<dt class="field-even">Raises<span class="colon">:</span></dt>
+<dd class="field-even"><p><strong>AttributeError</strong> – If the target string references an invalid
+    path or resolves to something that is not an
+    <code class="docutils literal notranslate"><span class="pre">nn.Module</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.half">
+<span class="sig-name descname"><span class="pre">half</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.half" title="Link to this definition"></a></dt>
+<dd><p>Casts all floating point parameters and buffers to <code class="docutils literal notranslate"><span class="pre">half</span></code> datatype.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method modifies the module in-place.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>self</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>Module</p>
+</dd>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>self</strong> (<em>T</em>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.ipu">
+<span class="sig-name descname"><span class="pre">ipu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.ipu" title="Link to this definition"></a></dt>
+<dd><p>Moves all model parameters and buffers to the IPU.</p>
+<p>This also makes associated parameters and buffers different objects. So
+it should be called before constructing optimizer if the module will
+live on IPU while being optimized.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method modifies the module in-place.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>device</strong> (<em>int</em><em>, </em><em>optional</em>) – if specified, all parameters will be
+copied to that device</p></li>
+<li><p><strong>self</strong> (<em>T</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>self</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Module</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.load_state_dict">
+<span class="sig-name descname"><span class="pre">load_state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state_dict</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">strict</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.load_state_dict" title="Link to this definition"></a></dt>
+<dd><p>Copies parameters and buffers from <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> into
+this module and its descendants. If <code class="xref py py-attr docutils literal notranslate"><span class="pre">strict</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, then
+the keys of <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> must exactly match the keys returned
+by this module’s <code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> function.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>state_dict</strong> (<em>dict</em>) – a dict containing parameters and
+persistent buffers.</p></li>
+<li><p><strong>strict</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to strictly enforce that the keys
+in <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a> match the keys returned by this module’s
+<code class="xref py py-meth docutils literal notranslate"><span class="pre">state_dict()</span></code> function. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code></p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><ul class="simple">
+<li><p><strong>missing_keys</strong> is a list of str containing the missing keys</p></li>
+<li><p><strong>unexpected_keys</strong> is a list of str containing the unexpected keys</p></li>
+</ul>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="docutils literal notranslate"><span class="pre">NamedTuple</span></code> with <code class="docutils literal notranslate"><span class="pre">missing_keys</span></code> and <code class="docutils literal notranslate"><span class="pre">unexpected_keys</span></code> fields</p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>If a parameter or buffer is registered as <code class="docutils literal notranslate"><span class="pre">None</span></code> and its corresponding key
+exists in <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a>, <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.load_state_dict" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.load_state_dict"><code class="xref py py-meth docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> will raise a
+<code class="docutils literal notranslate"><span class="pre">RuntimeError</span></code>.</p>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.modules">
+<span class="sig-name descname"><span class="pre">modules</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.modules" title="Link to this definition"></a></dt>
+<dd><p>Returns an iterator over all modules in the network.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Yields<span class="colon">:</span></dt>
+<dd class="field-odd"><p><em>Module</em> – a module in the network</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Iterator</em>[<em>Module</em>]</p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Duplicate modules are returned only once. In the following
+example, <code class="docutils literal notranslate"><span class="pre">l</span></code> will be returned only once.</p>
+</div>
+<p>Example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">l</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">l</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">idx</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">net</span><span class="o">.</span><span class="n">modules</span><span class="p">()):</span>
+<span class="gp">... </span>    <span class="nb">print</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="s1">&#39;-&gt;&#39;</span><span class="p">,</span> <span class="n">m</span><span class="p">)</span>
+
+<span class="go">0 -&gt; Sequential(</span>
+<span class="go">  (0): Linear(in_features=2, out_features=2, bias=True)</span>
+<span class="go">  (1): Linear(in_features=2, out_features=2, bias=True)</span>
+<span class="go">)</span>
+<span class="go">1 -&gt; Linear(in_features=2, out_features=2, bias=True)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_buffers">
+<span class="sig-name descname"><span class="pre">named_buffers</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_buffers" title="Link to this definition"></a></dt>
+<dd><p>Returns an iterator over module buffers, yielding both the
+name of the buffer as well as the buffer itself.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>prefix</strong> (<em>str</em>) – prefix to prepend to all buffer names.</p></li>
+<li><p><strong>recurse</strong> (<em>bool</em><em>, </em><em>optional</em>) – if True, then yields buffers of this module
+and all submodules. Otherwise, yields only buffers that
+are direct members of this module. Defaults to True.</p></li>
+<li><p><strong>remove_duplicate</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to remove the duplicated buffers in the result. Defaults to True.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Yields<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>(str, torch.Tensor)</em> – Tuple containing the name and buffer</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><em>Iterator</em>[<em>Tuple</em>[str, <em>Tensor</em>]]</p>
+</dd>
+</dl>
+<p>Example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +SKIP(&quot;undefined vars&quot;)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">buf</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">named_buffers</span><span class="p">():</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;running_var&#39;</span><span class="p">]:</span>
+<span class="gp">&gt;&gt;&gt; </span>        <span class="nb">print</span><span class="p">(</span><span class="n">buf</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_children">
+<span class="sig-name descname"><span class="pre">named_children</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_children" title="Link to this definition"></a></dt>
+<dd><p>Returns an iterator over immediate children modules, yielding both
+the name of the module as well as the module itself.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Yields<span class="colon">:</span></dt>
+<dd class="field-odd"><p><em>(str, Module)</em> – Tuple containing a name and child module</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Iterator</em>[<em>Tuple</em>[str, <em>Module</em>]]</p>
+</dd>
+</dl>
+<p>Example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +SKIP(&quot;undefined vars&quot;)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">module</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">named_children</span><span class="p">():</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;conv4&#39;</span><span class="p">,</span> <span class="s1">&#39;conv5&#39;</span><span class="p">]:</span>
+<span class="gp">&gt;&gt;&gt; </span>        <span class="nb">print</span><span class="p">(</span><span class="n">module</span><span class="p">)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_modules">
+<span class="sig-name descname"><span class="pre">named_modules</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memo</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_modules" title="Link to this definition"></a></dt>
+<dd><p>Returns an iterator over all modules in the network, yielding
+both the name of the module as well as the module itself.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>memo</strong> (<em>Set</em><em>[</em><em>Module</em><em>] </em><em>| </em><em>None</em>) – a memo to store the set of modules already added to the result</p></li>
+<li><p><strong>prefix</strong> (<em>str</em>) – a prefix that will be added to the name of the module</p></li>
+<li><p><strong>remove_duplicate</strong> (<em>bool</em>) – whether to remove the duplicated module instances in the result
+or not</p></li>
+</ul>
+</dd>
+<dt class="field-even">Yields<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>(str, Module)</em> – Tuple of name and module</p>
+</dd>
+</dl>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>Duplicate modules are returned only once. In the following
+example, <code class="docutils literal notranslate"><span class="pre">l</span></code> will be returned only once.</p>
+</div>
+<p>Example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">l</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">net</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Sequential</span><span class="p">(</span><span class="n">l</span><span class="p">,</span> <span class="n">l</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">idx</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">net</span><span class="o">.</span><span class="n">named_modules</span><span class="p">()):</span>
+<span class="gp">... </span>    <span class="nb">print</span><span class="p">(</span><span class="n">idx</span><span class="p">,</span> <span class="s1">&#39;-&gt;&#39;</span><span class="p">,</span> <span class="n">m</span><span class="p">)</span>
+
+<span class="go">0 -&gt; (&#39;&#39;, Sequential(</span>
+<span class="go">  (0): Linear(in_features=2, out_features=2, bias=True)</span>
+<span class="go">  (1): Linear(in_features=2, out_features=2, bias=True)</span>
+<span class="go">))</span>
+<span class="go">1 -&gt; (&#39;0&#39;, Linear(in_features=2, out_features=2, bias=True))</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_parameters">
+<span class="sig-name descname"><span class="pre">named_parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">remove_duplicate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.named_parameters" title="Link to this definition"></a></dt>
+<dd><p>Returns an iterator over module parameters, yielding both the
+name of the parameter as well as the parameter itself.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>prefix</strong> (<em>str</em>) – prefix to prepend to all parameter names.</p></li>
+<li><p><strong>recurse</strong> (<em>bool</em>) – if True, then yields parameters of this module
+and all submodules. Otherwise, yields only parameters that
+are direct members of this module.</p></li>
+<li><p><strong>remove_duplicate</strong> (<em>bool</em><em>, </em><em>optional</em>) – whether to remove the duplicated
+parameters in the result. Defaults to True.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Yields<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>(str, Parameter)</em> – Tuple containing the name and parameter</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><em>Iterator</em>[<em>Tuple</em>[str, <em>Parameter</em>]]</p>
+</dd>
+</dl>
+<p>Example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +SKIP(&quot;undefined vars&quot;)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">param</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">named_parameters</span><span class="p">():</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="k">if</span> <span class="n">name</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;bias&#39;</span><span class="p">]:</span>
+<span class="gp">&gt;&gt;&gt; </span>        <span class="nb">print</span><span class="p">(</span><span class="n">param</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.parameters">
+<span class="sig-name descname"><span class="pre">parameters</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">recurse</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.parameters" title="Link to this definition"></a></dt>
+<dd><p>Returns an iterator over module parameters.</p>
+<p>This is typically passed to an optimizer.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>recurse</strong> (<em>bool</em>) – if True, then yields parameters of this module
+and all submodules. Otherwise, yields only parameters that
+are direct members of this module.</p>
+</dd>
+<dt class="field-even">Yields<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Parameter</em> – module parameter</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><em>Iterator</em>[<em>Parameter</em>]</p>
+</dd>
+</dl>
+<p>Example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +SKIP(&quot;undefined vars&quot;)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="k">for</span> <span class="n">param</span> <span class="ow">in</span> <span class="n">model</span><span class="o">.</span><span class="n">parameters</span><span class="p">():</span>
+<span class="gp">&gt;&gt;&gt; </span>    <span class="nb">print</span><span class="p">(</span><span class="nb">type</span><span class="p">(</span><span class="n">param</span><span class="p">),</span> <span class="n">param</span><span class="o">.</span><span class="n">size</span><span class="p">())</span>
+<span class="go">&lt;class &#39;torch.Tensor&#39;&gt; (20L,)</span>
+<span class="go">&lt;class &#39;torch.Tensor&#39;&gt; (20L, 1L, 5L, 5L)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_backward_hook">
+<span class="sig-name descname"><span class="pre">register_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_backward_hook" title="Link to this definition"></a></dt>
+<dd><p>Registers a backward hook on the module.</p>
+<p>This function is deprecated in favor of <code class="xref py py-meth docutils literal notranslate"><span class="pre">register_full_backward_hook()</span></code> and
+the behavior of this function will change in future versions.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>a handle that can be used to remove the added hook by calling
+<code class="docutils literal notranslate"><span class="pre">handle.remove()</span></code></p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.hooks.RemovableHandle</span></code></p>
+</dd>
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>hook</strong> (<em>Callable</em><em>[</em><em>[</em><em>Module</em><em>, </em><em>Tuple</em><em>[</em><em>Tensor</em><em>, </em><em>...</em><em>] </em><em>| </em><em>Tensor</em><em>, </em><em>Tuple</em><em>[</em><em>Tensor</em><em>, </em><em>...</em><em>] </em><em>| </em><em>Tensor</em><em>]</em><em>, </em><em>None</em><em> | </em><em>Tuple</em><em>[</em><em>Tensor</em><em>, </em><em>...</em><em>] </em><em>| </em><em>Tensor</em><em>]</em>)</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_buffer">
+<span class="sig-name descname"><span class="pre">register_buffer</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">persistent</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_buffer" title="Link to this definition"></a></dt>
+<dd><p>Adds a buffer to the module.</p>
+<p>This is typically used to register a buffer that should not to be
+considered a model parameter. For example, BatchNorm’s <code class="docutils literal notranslate"><span class="pre">running_mean</span></code>
+is not a parameter, but is part of the module’s state. Buffers, by
+default, are persistent and will be saved alongside parameters. This
+behavior can be changed by setting <code class="xref py py-attr docutils literal notranslate"><span class="pre">persistent</span></code> to <code class="docutils literal notranslate"><span class="pre">False</span></code>. The
+only difference between a persistent buffer and a non-persistent buffer
+is that the latter will not be a part of this module’s
+<a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a>.</p>
+<p>Buffers can be accessed as attributes using given names.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>name</strong> (<em>str</em>) – name of the buffer. The buffer can be accessed
+from this module using the given name</p></li>
+<li><p><strong>tensor</strong> (<em>Tensor</em><em> or </em><em>None</em>) – buffer to be registered. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, then operations
+that run on buffers, such as <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.cuda" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.cuda"><code class="xref py py-attr docutils literal notranslate"><span class="pre">cuda</span></code></a>, are ignored. If <code class="docutils literal notranslate"><span class="pre">None</span></code>,
+the buffer is <strong>not</strong> included in the module’s <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a>.</p></li>
+<li><p><strong>persistent</strong> (<em>bool</em>) – whether the buffer is part of this module’s
+<a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+<p>Example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +SKIP(&quot;undefined vars&quot;)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="bp">self</span><span class="o">.</span><span class="n">register_buffer</span><span class="p">(</span><span class="s1">&#39;running_mean&#39;</span><span class="p">,</span> <span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">num_features</span><span class="p">))</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_forward_hook">
+<span class="sig-name descname"><span class="pre">register_forward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_forward_hook" title="Link to this definition"></a></dt>
+<dd><p>Registers a forward hook on the module.</p>
+<p>The hook will be called every time after <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.forward" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> has computed an output.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is <code class="docutils literal notranslate"><span class="pre">False</span></code> or not specified, the input contains only
+the positional arguments given to the module. Keyword arguments won’t be
+passed to the hooks and only to the <code class="docutils literal notranslate"><span class="pre">forward</span></code>. The hook can modify the
+output. It can modify the input inplace but it will not have effect on
+forward since this is called after <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.forward" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> is called. The hook
+should have the following signature:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">output</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">output</span>
+</pre></div>
+</div>
+<p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, the forward hook will be passed the
+<code class="docutils literal notranslate"><span class="pre">kwargs</span></code> given to the forward function and be expected to return the
+output possibly modified. The hook should have the following signature:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">,</span> <span class="n">output</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="n">output</span>
+</pre></div>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>hook</strong> (<em>Callable</em>) – The user defined hook to be registered.</p></li>
+<li><p><strong>prepend</strong> (<em>bool</em>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, the provided <code class="docutils literal notranslate"><span class="pre">hook</span></code> will be fired
+before all existing <code class="docutils literal notranslate"><span class="pre">forward</span></code> hooks on this
+<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.Module</span></code>. Otherwise, the provided
+<code class="docutils literal notranslate"><span class="pre">hook</span></code> will be fired after all existing <code class="docutils literal notranslate"><span class="pre">forward</span></code> hooks on
+this <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.Module</span></code>. Note that global
+<code class="docutils literal notranslate"><span class="pre">forward</span></code> hooks registered with
+<code class="xref py py-func docutils literal notranslate"><span class="pre">register_module_forward_hook()</span></code> will fire before all hooks
+registered by this method.
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
+<li><p><strong>with_kwargs</strong> (<em>bool</em>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, the <code class="docutils literal notranslate"><span class="pre">hook</span></code> will be passed the
+kwargs given to the forward function.
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>a handle that can be used to remove the added hook by calling
+<code class="docutils literal notranslate"><span class="pre">handle.remove()</span></code></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.hooks.RemovableHandle</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_forward_pre_hook">
+<span class="sig-name descname"><span class="pre">register_forward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">with_kwargs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_forward_pre_hook" title="Link to this definition"></a></dt>
+<dd><p>Registers a forward pre-hook on the module.</p>
+<p>The hook will be called every time before <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.forward" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.forward"><code class="xref py py-func docutils literal notranslate"><span class="pre">forward()</span></code></a> is invoked.</p>
+<p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is false or not specified, the input contains only
+the positional arguments given to the module. Keyword arguments won’t be
+passed to the hooks and only to the <code class="docutils literal notranslate"><span class="pre">forward</span></code>. The hook can modify the
+input. User can either return a tuple or a single modified value in the
+hook. We will wrap the value into a tuple if a single value is returned
+(unless that value is already a tuple). The hook should have the
+following signature:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">args</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">modified</span> <span class="nb">input</span>
+</pre></div>
+</div>
+<p>If <code class="docutils literal notranslate"><span class="pre">with_kwargs</span></code> is true, the forward pre-hook will be passed the
+kwargs given to the forward function. And if the hook modifies the
+input, both the args and kwargs should be returned. The hook should have
+the following signature:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">args</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">a</span> <span class="nb">tuple</span> <span class="n">of</span> <span class="n">modified</span> <span class="nb">input</span> <span class="ow">and</span> <span class="n">kwargs</span>
+</pre></div>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>hook</strong> (<em>Callable</em>) – The user defined hook to be registered.</p></li>
+<li><p><strong>prepend</strong> (<em>bool</em>) – If true, the provided <code class="docutils literal notranslate"><span class="pre">hook</span></code> will be fired before
+all existing <code class="docutils literal notranslate"><span class="pre">forward_pre</span></code> hooks on this
+<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.Module</span></code>. Otherwise, the provided
+<code class="docutils literal notranslate"><span class="pre">hook</span></code> will be fired after all existing <code class="docutils literal notranslate"><span class="pre">forward_pre</span></code> hooks
+on this <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.Module</span></code>. Note that global
+<code class="docutils literal notranslate"><span class="pre">forward_pre</span></code> hooks registered with
+<code class="xref py py-func docutils literal notranslate"><span class="pre">register_module_forward_pre_hook()</span></code> will fire before all
+hooks registered by this method.
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
+<li><p><strong>with_kwargs</strong> (<em>bool</em>) – If true, the <code class="docutils literal notranslate"><span class="pre">hook</span></code> will be passed the kwargs
+given to the forward function.
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code></p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>a handle that can be used to remove the added hook by calling
+<code class="docutils literal notranslate"><span class="pre">handle.remove()</span></code></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.hooks.RemovableHandle</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_full_backward_hook">
+<span class="sig-name descname"><span class="pre">register_full_backward_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_full_backward_hook" title="Link to this definition"></a></dt>
+<dd><p>Registers a backward hook on the module.</p>
+<p>The hook will be called every time the gradients with respect to a module
+are computed, i.e. the hook will execute if and only if the gradients with
+respect to module outputs are computed. The hook should have the following
+signature:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">grad_input</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">Tensor</span><span class="p">)</span> <span class="ow">or</span> <span class="kc">None</span>
+</pre></div>
+</div>
+<p>The <code class="xref py py-attr docutils literal notranslate"><span class="pre">grad_input</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">grad_output</span></code> are tuples that contain the gradients
+with respect to the inputs and outputs respectively. The hook should
+not modify its arguments, but it can optionally return a new gradient with
+respect to the input that will be used in place of <code class="xref py py-attr docutils literal notranslate"><span class="pre">grad_input</span></code> in
+subsequent computations. <code class="xref py py-attr docutils literal notranslate"><span class="pre">grad_input</span></code> will only correspond to the inputs given
+as positional arguments and all kwarg arguments are ignored. Entries
+in <code class="xref py py-attr docutils literal notranslate"><span class="pre">grad_input</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">grad_output</span></code> will be <code class="docutils literal notranslate"><span class="pre">None</span></code> for all non-Tensor
+arguments.</p>
+<p>For technical reasons, when this hook is applied to a Module, its forward function will
+receive a view of each Tensor passed to the Module. Similarly the caller will receive a view
+of each Tensor returned by the Module’s forward function.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>Modifying inputs or outputs inplace is not allowed when using backward hooks and
+will raise an error.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>hook</strong> (<em>Callable</em>) – The user-defined hook to be registered.</p></li>
+<li><p><strong>prepend</strong> (<em>bool</em>) – If true, the provided <code class="docutils literal notranslate"><span class="pre">hook</span></code> will be fired before
+all existing <code class="docutils literal notranslate"><span class="pre">backward</span></code> hooks on this
+<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.Module</span></code>. Otherwise, the provided
+<code class="docutils literal notranslate"><span class="pre">hook</span></code> will be fired after all existing <code class="docutils literal notranslate"><span class="pre">backward</span></code> hooks on
+this <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.Module</span></code>. Note that global
+<code class="docutils literal notranslate"><span class="pre">backward</span></code> hooks registered with
+<code class="xref py py-func docutils literal notranslate"><span class="pre">register_module_full_backward_hook()</span></code> will fire before
+all hooks registered by this method.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>a handle that can be used to remove the added hook by calling
+<code class="docutils literal notranslate"><span class="pre">handle.remove()</span></code></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.hooks.RemovableHandle</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_full_backward_pre_hook">
+<span class="sig-name descname"><span class="pre">register_full_backward_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prepend</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_full_backward_pre_hook" title="Link to this definition"></a></dt>
+<dd><p>Registers a backward pre-hook on the module.</p>
+<p>The hook will be called every time the gradients for the module are computed.
+The hook should have the following signature:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="n">hook</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">grad_output</span><span class="p">)</span> <span class="o">-&gt;</span> <span class="n">Tensor</span> <span class="ow">or</span> <span class="kc">None</span>
+</pre></div>
+</div>
+<p>The <code class="xref py py-attr docutils literal notranslate"><span class="pre">grad_output</span></code> is a tuple. The hook should
+not modify its arguments, but it can optionally return a new gradient with
+respect to the output that will be used in place of <code class="xref py py-attr docutils literal notranslate"><span class="pre">grad_output</span></code> in
+subsequent computations. Entries in <code class="xref py py-attr docutils literal notranslate"><span class="pre">grad_output</span></code> will be <code class="docutils literal notranslate"><span class="pre">None</span></code> for
+all non-Tensor arguments.</p>
+<p>For technical reasons, when this hook is applied to a Module, its forward function will
+receive a view of each Tensor passed to the Module. Similarly the caller will receive a view
+of each Tensor returned by the Module’s forward function.</p>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>Modifying inputs inplace is not allowed when using backward hooks and
+will raise an error.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>hook</strong> (<em>Callable</em>) – The user-defined hook to be registered.</p></li>
+<li><p><strong>prepend</strong> (<em>bool</em>) – If true, the provided <code class="docutils literal notranslate"><span class="pre">hook</span></code> will be fired before
+all existing <code class="docutils literal notranslate"><span class="pre">backward_pre</span></code> hooks on this
+<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.Module</span></code>. Otherwise, the provided
+<code class="docutils literal notranslate"><span class="pre">hook</span></code> will be fired after all existing <code class="docutils literal notranslate"><span class="pre">backward_pre</span></code> hooks
+on this <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.nn.modules.Module</span></code>. Note that global
+<code class="docutils literal notranslate"><span class="pre">backward_pre</span></code> hooks registered with
+<code class="xref py py-func docutils literal notranslate"><span class="pre">register_module_full_backward_pre_hook()</span></code> will fire before
+all hooks registered by this method.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>a handle that can be used to remove the added hook by calling
+<code class="docutils literal notranslate"><span class="pre">handle.remove()</span></code></p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.hooks.RemovableHandle</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_load_state_dict_post_hook">
+<span class="sig-name descname"><span class="pre">register_load_state_dict_post_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_load_state_dict_post_hook" title="Link to this definition"></a></dt>
+<dd><p>Registers a post hook to be run after module’s <code class="docutils literal notranslate"><span class="pre">load_state_dict</span></code>
+is called.</p>
+<dl class="simple">
+<dt>It should have the following signature::</dt><dd><p>hook(module, incompatible_keys) -&gt; None</p>
+</dd>
+</dl>
+<p>The <code class="docutils literal notranslate"><span class="pre">module</span></code> argument is the current module that this hook is registered
+on, and the <code class="docutils literal notranslate"><span class="pre">incompatible_keys</span></code> argument is a <code class="docutils literal notranslate"><span class="pre">NamedTuple</span></code> consisting
+of attributes <code class="docutils literal notranslate"><span class="pre">missing_keys</span></code> and <code class="docutils literal notranslate"><span class="pre">unexpected_keys</span></code>. <code class="docutils literal notranslate"><span class="pre">missing_keys</span></code>
+is a <code class="docutils literal notranslate"><span class="pre">list</span></code> of <code class="docutils literal notranslate"><span class="pre">str</span></code> containing the missing keys and
+<code class="docutils literal notranslate"><span class="pre">unexpected_keys</span></code> is a <code class="docutils literal notranslate"><span class="pre">list</span></code> of <code class="docutils literal notranslate"><span class="pre">str</span></code> containing the unexpected keys.</p>
+<p>The given incompatible_keys can be modified inplace if needed.</p>
+<p>Note that the checks performed when calling <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.load_state_dict" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.load_state_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> with
+<code class="docutils literal notranslate"><span class="pre">strict=True</span></code> are affected by modifications the hook makes to
+<code class="docutils literal notranslate"><span class="pre">missing_keys</span></code> or <code class="docutils literal notranslate"><span class="pre">unexpected_keys</span></code>, as expected. Additions to either
+set of keys will result in an error being thrown when <code class="docutils literal notranslate"><span class="pre">strict=True</span></code>, and
+clearing out both missing and unexpected keys will avoid an error.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Returns<span class="colon">:</span></dt>
+<dd class="field-odd"><p>a handle that can be used to remove the added hook by calling
+<code class="docutils literal notranslate"><span class="pre">handle.remove()</span></code></p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.utils.hooks.RemovableHandle</span></code></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_module">
+<span class="sig-name descname"><span class="pre">register_module</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">module</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_module" title="Link to this definition"></a></dt>
+<dd><p>Alias for <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.add_module" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.add_module"><code class="xref py py-func docutils literal notranslate"><span class="pre">add_module()</span></code></a>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>name</strong> (<em>str</em>)</p></li>
+<li><p><strong>module</strong> (<em>Module</em><em> | </em><em>None</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_parameter">
+<span class="sig-name descname"><span class="pre">register_parameter</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">name</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">param</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_parameter" title="Link to this definition"></a></dt>
+<dd><p>Adds a parameter to the module.</p>
+<p>The parameter can be accessed as an attribute using given name.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>name</strong> (<em>str</em>) – name of the parameter. The parameter can be accessed
+from this module using the given name</p></li>
+<li><p><strong>param</strong> (<em>Parameter</em><em> or </em><em>None</em>) – parameter to be added to the module. If
+<code class="docutils literal notranslate"><span class="pre">None</span></code>, then operations that run on parameters, such as <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.cuda" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.cuda"><code class="xref py py-attr docutils literal notranslate"><span class="pre">cuda</span></code></a>,
+are ignored. If <code class="docutils literal notranslate"><span class="pre">None</span></code>, the parameter is <strong>not</strong> included in the
+module’s <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict"><code class="xref py py-attr docutils literal notranslate"><span class="pre">state_dict</span></code></a>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_state_dict_pre_hook">
+<span class="sig-name descname"><span class="pre">register_state_dict_pre_hook</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">hook</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.register_state_dict_pre_hook" title="Link to this definition"></a></dt>
+<dd><p>These hooks will be called with arguments: <code class="docutils literal notranslate"><span class="pre">self</span></code>, <code class="docutils literal notranslate"><span class="pre">prefix</span></code>,
+and <code class="docutils literal notranslate"><span class="pre">keep_vars</span></code> before calling <code class="docutils literal notranslate"><span class="pre">state_dict</span></code> on <code class="docutils literal notranslate"><span class="pre">self</span></code>. The registered
+hooks can be used to perform pre-processing before the <code class="docutils literal notranslate"><span class="pre">state_dict</span></code>
+call is made.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.requires_grad_">
+<span class="sig-name descname"><span class="pre">requires_grad_</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">requires_grad</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.requires_grad_" title="Link to this definition"></a></dt>
+<dd><p>Change if autograd should record operations on parameters in this
+module.</p>
+<p>This method sets the parameters’ <code class="xref py py-attr docutils literal notranslate"><span class="pre">requires_grad</span></code> attributes
+in-place.</p>
+<p>This method is helpful for freezing part of the module for finetuning
+or training parts of a model individually (e.g., GAN training).</p>
+<p>See <span class="xref std std-ref">locally-disable-grad-doc</span> for a comparison between
+<cite>.requires_grad_()</cite> and several similar mechanisms that may be confused with it.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>requires_grad</strong> (<em>bool</em>) – whether autograd should record operations on
+parameters in this module. Default: <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
+<li><p><strong>self</strong> (<em>T</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>self</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Module</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.set_extra_state">
+<span class="sig-name descname"><span class="pre">set_extra_state</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">state</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.set_extra_state" title="Link to this definition"></a></dt>
+<dd><p>This function is called from <a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.load_state_dict" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.load_state_dict"><code class="xref py py-func docutils literal notranslate"><span class="pre">load_state_dict()</span></code></a> to handle any extra state
+found within the <cite>state_dict</cite>. Implement this function and a corresponding
+<a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_extra_state" title="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.get_extra_state"><code class="xref py py-func docutils literal notranslate"><span class="pre">get_extra_state()</span></code></a> for your module if you need to store extra state within its
+<cite>state_dict</cite>.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>state</strong> (<em>dict</em>) – Extra state from the <cite>state_dict</cite></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.share_memory">
+<span class="sig-name descname"><span class="pre">share_memory</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.share_memory" title="Link to this definition"></a></dt>
+<dd><p>See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.share_memory_()</span></code></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>self</strong> (<em>T</em>)</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>T</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict">
+<span class="sig-name descname"><span class="pre">state_dict</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">destination</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prefix</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">keep_vars</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.state_dict" title="Link to this definition"></a></dt>
+<dd><p>Returns a dictionary containing references to the whole state of the module.</p>
+<p>Both parameters and persistent buffers (e.g. running averages) are
+included. Keys are corresponding parameter and buffer names.
+Parameters and buffers set to <code class="docutils literal notranslate"><span class="pre">None</span></code> are not included.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>The returned object is a shallow copy. It contains references
+to the module’s parameters and buffers.</p>
+</div>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>Currently <code class="docutils literal notranslate"><span class="pre">state_dict()</span></code> also accepts positional arguments for
+<code class="docutils literal notranslate"><span class="pre">destination</span></code>, <code class="docutils literal notranslate"><span class="pre">prefix</span></code> and <code class="docutils literal notranslate"><span class="pre">keep_vars</span></code> in order. However,
+this is being deprecated and keyword arguments will be enforced in
+future releases.</p>
+</div>
+<div class="admonition warning">
+<p class="admonition-title">Warning</p>
+<p>Please avoid the use of argument <code class="docutils literal notranslate"><span class="pre">destination</span></code> as it is not
+designed for end-users.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>destination</strong> (<em>dict</em><em>, </em><em>optional</em>) – If provided, the state of module will
+be updated into the dict and the same object is returned.
+Otherwise, an <code class="docutils literal notranslate"><span class="pre">OrderedDict</span></code> will be created and returned.
+Default: <code class="docutils literal notranslate"><span class="pre">None</span></code>.</p></li>
+<li><p><strong>prefix</strong> (<em>str</em><em>, </em><em>optional</em>) – a prefix added to parameter and buffer
+names to compose the keys in state_dict. Default: <code class="docutils literal notranslate"><span class="pre">''</span></code>.</p></li>
+<li><p><strong>keep_vars</strong> (<em>bool</em><em>, </em><em>optional</em>) – by default the <code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code> s
+returned in the state dict are detached from autograd. If it’s
+set to <code class="docutils literal notranslate"><span class="pre">True</span></code>, detaching will not be performed.
+Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>a dictionary containing a whole state of the module</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>dict</p>
+</dd>
+</dl>
+<p>Example:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +SKIP(&quot;undefined vars&quot;)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">module</span><span class="o">.</span><span class="n">state_dict</span><span class="p">()</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span>
+<span class="go">[&#39;bias&#39;, &#39;weight&#39;]</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.to">
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.to" title="Link to this definition"></a></dt>
+<dd><p>Moves and/or casts the parameters and buffers.</p>
+<p>This can be called as</p>
+<dl class="py function">
+<dt class="sig sig-object py">
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dtype</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py">
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dtype</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py">
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">non_blocking</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py">
+<span class="sig-name descname"><span class="pre">to</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.channels_last</span></span></em><span class="sig-paren">)</span></dt>
+<dd></dd></dl>
+
+<p>Its signature is similar to <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.to()</span></code>, but only accepts
+floating point or complex <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code>s. In addition, this method will
+only cast the floating point or complex parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code>
+(if given). The integral parameters and buffers will be moved
+<code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code>, if that is given, but with dtypes unchanged. When
+<code class="xref py py-attr docutils literal notranslate"><span class="pre">non_blocking</span></code> is set, it tries to convert/move asynchronously
+with respect to the host if possible, e.g., moving CPU Tensors with
+pinned memory to CUDA devices.</p>
+<p>See below for examples.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method modifies the module in-place.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>device</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code>) – the desired device of the parameters
+and buffers in this module</p></li>
+<li><p><strong>dtype</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code>) – the desired floating point or complex dtype of
+the parameters and buffers in this module</p></li>
+<li><p><strong>tensor</strong> (<em>torch.Tensor</em>) – Tensor whose dtype and device are the desired
+dtype and device for all parameters and buffers in this module</p></li>
+<li><p><strong>memory_format</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.memory_format</span></code>) – the desired memory
+format for 4D parameters and buffers in this module (keyword
+only argument)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>self</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Module</p>
+</dd>
+</dl>
+<p>Examples:</p>
+<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +IGNORE_WANT(&quot;non-deterministic&quot;)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">linear</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">linear</span><span class="o">.</span><span class="n">weight</span>
+<span class="go">Parameter containing:</span>
+<span class="go">tensor([[ 0.1913, -0.3420],</span>
+<span class="go">        [-0.5113, -0.2325]])</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">linear</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">double</span><span class="p">)</span>
+<span class="go">Linear(in_features=2, out_features=2, bias=True)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">linear</span><span class="o">.</span><span class="n">weight</span>
+<span class="go">Parameter containing:</span>
+<span class="go">tensor([[ 0.1913, -0.3420],</span>
+<span class="go">        [-0.5113, -0.2325]], dtype=torch.float64)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="c1"># xdoctest: +REQUIRES(env:TORCH_DOCTEST_CUDA1)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">gpu1</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cuda:1&quot;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">linear</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">gpu1</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">half</span><span class="p">,</span> <span class="n">non_blocking</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+<span class="go">Linear(in_features=2, out_features=2, bias=True)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">linear</span><span class="o">.</span><span class="n">weight</span>
+<span class="go">Parameter containing:</span>
+<span class="go">tensor([[ 0.1914, -0.3420],</span>
+<span class="go">        [-0.5112, -0.2324]], dtype=torch.float16, device=&#39;cuda:1&#39;)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">cpu</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s2">&quot;cpu&quot;</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">linear</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">cpu</span><span class="p">)</span>
+<span class="go">Linear(in_features=2, out_features=2, bias=True)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">linear</span><span class="o">.</span><span class="n">weight</span>
+<span class="go">Parameter containing:</span>
+<span class="go">tensor([[ 0.1914, -0.3420],</span>
+<span class="go">        [-0.5112, -0.2324]], dtype=torch.float16)</span>
+
+<span class="gp">&gt;&gt;&gt; </span><span class="n">linear</span> <span class="o">=</span> <span class="n">nn</span><span class="o">.</span><span class="n">Linear</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">bias</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">cdouble</span><span class="p">)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">linear</span><span class="o">.</span><span class="n">weight</span>
+<span class="go">Parameter containing:</span>
+<span class="go">tensor([[ 0.3741+0.j,  0.2382+0.j],</span>
+<span class="go">        [ 0.5593+0.j, -0.4443+0.j]], dtype=torch.complex128)</span>
+<span class="gp">&gt;&gt;&gt; </span><span class="n">linear</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">ones</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">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cdouble</span><span class="p">))</span>
+<span class="go">tensor([[0.6122+0.j, 0.1150+0.j],</span>
+<span class="go">        [0.6122+0.j, 0.1150+0.j],</span>
+<span class="go">        [0.6122+0.j, 0.1150+0.j]], dtype=torch.complex128)</span>
+</pre></div>
+</div>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.to_empty">
+<span class="sig-name descname"><span class="pre">to_empty</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">device</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.to_empty" title="Link to this definition"></a></dt>
+<dd><p>Moves the parameters and buffers to the specified device without copying storage.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>device</strong> (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code>) – The desired device of the parameters
+and buffers in this module.</p></li>
+<li><p><strong>self</strong> (<em>T</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>self</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Module</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.train">
+<span class="sig-name descname"><span class="pre">train</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">mode</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.train" title="Link to this definition"></a></dt>
+<dd><p>Sets the module in training mode.</p>
+<p>This has any effect only on certain modules. See documentations of
+particular modules for details of their behaviors in training/evaluation
+mode, if they are affected, e.g. <code class="xref py py-class docutils literal notranslate"><span class="pre">Dropout</span></code>, <code class="xref py py-class docutils literal notranslate"><span class="pre">BatchNorm</span></code>,
+etc.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>mode</strong> (<em>bool</em>) – whether to set training mode (<code class="docutils literal notranslate"><span class="pre">True</span></code>) or evaluation
+mode (<code class="docutils literal notranslate"><span class="pre">False</span></code>). Default: <code class="docutils literal notranslate"><span class="pre">True</span></code>.</p></li>
+<li><p><strong>self</strong> (<em>T</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>self</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Module</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.type">
+<span class="sig-name descname"><span class="pre">type</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">dst_type</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.type" title="Link to this definition"></a></dt>
+<dd><p>Casts all parameters and buffers to <code class="xref py py-attr docutils literal notranslate"><span class="pre">dst_type</span></code>.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method modifies the module in-place.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>dst_type</strong> (<em>type</em><em> or </em><em>string</em>) – the desired type</p></li>
+<li><p><strong>self</strong> (<em>T</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>self</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Module</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.xpu">
+<span class="sig-name descname"><span class="pre">xpu</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">device</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.xpu" title="Link to this definition"></a></dt>
+<dd><p>Moves all model parameters and buffers to the XPU.</p>
+<p>This also makes associated parameters and buffers different objects. So
+it should be called before constructing optimizer if the module will
+live on XPU while being optimized.</p>
+<div class="admonition note">
+<p class="admonition-title">Note</p>
+<p>This method modifies the module in-place.</p>
+</div>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>device</strong> (<em>int</em><em>, </em><em>optional</em>) – if specified, all parameters will be
+copied to that device</p></li>
+<li><p><strong>self</strong> (<em>T</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>self</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Module</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.zero_grad">
+<span class="sig-name descname"><span class="pre">zero_grad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">set_to_none</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.zero_grad" title="Link to this definition"></a></dt>
+<dd><p>Sets gradients of all model parameters to zero. See similar function
+under <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.optim.Optimizer</span></code> for more context.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>set_to_none</strong> (<em>bool</em>) – instead of setting to zero, set the grads to None.
+See <code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.optim.Optimizer.zero_grad()</span></code> for details.</p>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py attribute">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Predictor.training">
+<span class="sig-name descname"><span class="pre">training</span></span><em class="property"><span class="p"><span class="pre">:</span></span><span class="w"> </span><span class="pre">bool</span></em><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Predictor.training" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.pytorch_models.</span></span><span class="sig-name descname"><span class="pre">PROTAC_Model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kwargs</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model" title="Link to this definition"></a></dt>
+<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">LightningModule</span></code></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>hidden_dim</strong> (<em>int</em>)</p></li>
+<li><p><strong>smiles_emb_dim</strong> (<em>int</em>)</p></li>
+<li><p><strong>poi_emb_dim</strong> (<em>int</em>)</p></li>
+<li><p><strong>e3_emb_dim</strong> (<em>int</em>)</p></li>
+<li><p><strong>cell_emb_dim</strong> (<em>int</em>)</p></li>
+<li><p><strong>batch_size</strong> (<em>int</em>)</p></li>
+<li><p><strong>learning_rate</strong> (<em>float</em>)</p></li>
+<li><p><strong>dropout</strong> (<em>float</em>)</p></li>
+<li><p><strong>use_batch_norm</strong> (<em>bool</em>)</p></li>
+<li><p><strong>join_embeddings</strong> (<em>Literal</em><em>[</em><em>'beginning'</em><em>, </em><em>'concat'</em><em>, </em><em>'sum'</em><em>]</em>)</p></li>
+<li><p><strong>train_dataset</strong> (<a class="reference internal" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset" title="protac_degradation_predictor.protac_dataset.PROTAC_Dataset"><em>PROTAC_Dataset</em></a>)</p></li>
+<li><p><strong>val_dataset</strong> (<a class="reference internal" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset" title="protac_degradation_predictor.protac_dataset.PROTAC_Dataset"><em>PROTAC_Dataset</em></a>)</p></li>
+<li><p><strong>test_dataset</strong> (<a class="reference internal" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset" title="protac_degradation_predictor.protac_dataset.PROTAC_Dataset"><em>PROTAC_Dataset</em></a>)</p></li>
+<li><p><strong>disabled_embeddings</strong> (<em>List</em><em>[</em><em>Literal</em><em>[</em><em>'smiles'</em><em>, </em><em>'poi'</em><em>, </em><em>'e3'</em><em>, </em><em>'cell'</em><em>]</em><em>]</em>)</p></li>
+<li><p><strong>apply_scaling</strong> (<em>bool</em>)</p></li>
+<li><p><strong>extra_optim_params</strong> (<em>dict</em><em> | </em><em>None</em>)</p></li>
+</ul>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.initialize_scalers">
+<span class="sig-name descname"><span class="pre">initialize_scalers</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.initialize_scalers" title="Link to this definition"></a></dt>
+<dd><p>Initialize or reinitialize scalers based on dataset properties.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.apply_scalers">
+<span class="sig-name descname"><span class="pre">apply_scalers</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.apply_scalers" title="Link to this definition"></a></dt>
+<dd><p>Apply scalers to all datasets.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.scale_tensor">
+<span class="sig-name descname"><span class="pre">scale_tensor</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">tensor</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">scaler</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-10</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.scale_tensor" title="Link to this definition"></a></dt>
+<dd><p>Scale a tensor using a scaler. This is done to avoid using numpy
+arrays (and stay on the same device).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>tensor</strong> (<em>torch.Tensor</em>) – The tensor to scale.</p></li>
+<li><p><strong>scaler</strong> (<em>StandardScaler</em>) – The scaler to use.</p></li>
+<li><p><strong>alpha</strong> (<em>float</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The scaled tensor.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>torch.Tensor</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.forward">
+<span class="sig-name descname"><span class="pre">forward</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">poi_emb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">e3_emb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell_emb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smiles_emb</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">prescaled_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.forward" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.step">
+<span class="sig-name descname"><span class="pre">step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_idx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">stage</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.step" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.training_step">
+<span class="sig-name descname"><span class="pre">training_step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_idx</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.training_step" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.validation_step">
+<span class="sig-name descname"><span class="pre">validation_step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_idx</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.validation_step" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.test_step">
+<span class="sig-name descname"><span class="pre">test_step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_idx</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.test_step" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.configure_optimizers">
+<span class="sig-name descname"><span class="pre">configure_optimizers</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.configure_optimizers" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.predict_step">
+<span class="sig-name descname"><span class="pre">predict_step</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">batch</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_idx</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.predict_step" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.train_dataloader">
+<span class="sig-name descname"><span class="pre">train_dataloader</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.train_dataloader" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.val_dataloader">
+<span class="sig-name descname"><span class="pre">val_dataloader</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.val_dataloader" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.test_dataloader">
+<span class="sig-name descname"><span class="pre">test_dataloader</span></span><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.test_dataloader" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.on_save_checkpoint">
+<span class="sig-name descname"><span class="pre">on_save_checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.on_save_checkpoint" title="Link to this definition"></a></dt>
+<dd><p>Serialize the scalers to the checkpoint.</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.PROTAC_Model.on_load_checkpoint">
+<span class="sig-name descname"><span class="pre">on_load_checkpoint</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">checkpoint</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model.on_load_checkpoint" title="Link to this definition"></a></dt>
+<dd><p>Deserialize the scalers from the checkpoint.</p>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.get_confidence_scores">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.pytorch_models.</span></span><span class="sig-name descname"><span class="pre">get_confidence_scores</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">true_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">y_preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">threshold</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.get_confidence_scores" title="Link to this definition"></a></dt>
+<dd></dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.train_model">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.pytorch_models.</span></span><span class="sig-name descname"><span class="pre">train_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">protein2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smiles2fp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">hidden_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">768</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">learning_rate</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">2e-05</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta1</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.9</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">beta2</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.999</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">eps</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1e-08</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dropout</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.2</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">max_epochs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_batch_norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">join_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'sum'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smote_k_neighbors</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">apply_scaling</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Active'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">fast_dev_run</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_logger</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_save_dir</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'../logs'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logger_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'protac'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">enable_checkpointing</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">checkpoint_model_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'protac'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">disabled_embeddings</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">return_predictions</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">shuffle_embedding_prob</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_smote</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.train_model" title="Link to this definition"></a></dt>
+<dd><p>Train a PROTAC model using the given datasets and hyperparameters.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>protein2embedding</strong> (<em>dict</em>) – A dictionary mapping protein identifiers to embeddings.</p></li>
+<li><p><strong>cell2embedding</strong> (<em>dict</em>) – A dictionary mapping cell line identifiers to embeddings.</p></li>
+<li><p><strong>smiles2fp</strong> (<em>dict</em>) – A dictionary mapping SMILES strings to fingerprints.</p></li>
+<li><p><strong>train_df</strong> (<em>pd.DataFrame</em>) – The training dataframe.</p></li>
+<li><p><strong>val_df</strong> (<em>pd.DataFrame</em>) – The validation dataframe.</p></li>
+<li><p><strong>test_df</strong> (<em>Optional</em><em>[</em><em>pd.DataFrame</em><em>]</em>) – The test dataframe.</p></li>
+<li><p><strong>hidden_dim</strong> (<em>int</em>) – The hidden dimension of the model</p></li>
+<li><p><strong>batch_size</strong> (<em>int</em>) – The batch size</p></li>
+<li><p><strong>learning_rate</strong> (<em>float</em>) – The learning rate</p></li>
+<li><p><strong>dropout</strong> (<em>float</em>) – The dropout rate</p></li>
+<li><p><strong>max_epochs</strong> (<em>int</em>) – The maximum number of epochs</p></li>
+<li><p><strong>use_batch_norm</strong> (<em>bool</em>) – Whether to use batch normalization</p></li>
+<li><p><strong>join_embeddings</strong> (<em>Literal</em><em>[</em><em>'beginning'</em><em>, </em><em>'concat'</em><em>, </em><em>'sum'</em><em>]</em>) – How to join the embeddings</p></li>
+<li><p><strong>smote_k_neighbors</strong> (<em>int</em>) – The number of neighbors to use in SMOTE</p></li>
+<li><p><strong>use_smote</strong> (<em>bool</em>) – Whether to use SMOTE</p></li>
+<li><p><strong>apply_scaling</strong> (<em>bool</em>) – Whether to apply scaling to the embeddings</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>) – The name of the active label. Default: ‘Active’</p></li>
+<li><p><strong>fast_dev_run</strong> (<em>bool</em>) – Whether to run a fast development run (see PyTorch Lightning documentation)</p></li>
+<li><p><strong>use_logger</strong> (<em>bool</em>) – Whether to use a logger</p></li>
+<li><p><strong>logger_save_dir</strong> (<em>str</em>) – The directory to save the logs</p></li>
+<li><p><strong>logger_name</strong> (<em>str</em>) – The name of the logger</p></li>
+<li><p><strong>enable_checkpointing</strong> (<em>bool</em>) – Whether to enable checkpointing</p></li>
+<li><p><strong>checkpoint_model_name</strong> (<em>str</em>) – The name of the model for checkpointing</p></li>
+<li><p><strong>disabled_embeddings</strong> (<em>list</em>) – List of disabled embeddings. Can be ‘poi’, ‘e3’, ‘cell’, ‘smiles’</p></li>
+<li><p><strong>return_predictions</strong> (<em>bool</em>) – Whether to return predictions on the validation and test sets</p></li>
+<li><p><strong>beta1</strong> (<em>float</em>)</p></li>
+<li><p><strong>beta2</strong> (<em>float</em>)</p></li>
+<li><p><strong>eps</strong> (<em>float</em>)</p></li>
+<li><p><strong>shuffle_embedding_prob</strong> (<em>float</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The trained model, the trainer, and the metrics over the validation and test sets.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>tuple</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.evaluate_model">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.pytorch_models.</span></span><span class="sig-name descname"><span class="pre">evaluate_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">model</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trainer</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_ds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_ds</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">batch_size</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">128</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.evaluate_model" title="Link to this definition"></a></dt>
+<dd><p>Evaluate a PROTAC model using the given datasets.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>model</strong> (<a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model" title="protac_degradation_predictor.pytorch_models.PROTAC_Model"><em>PROTAC_Model</em></a>)</p></li>
+<li><p><strong>trainer</strong> (<em>pytorch_lightning.Trainer</em>)</p></li>
+<li><p><strong>val_ds</strong> (<a class="reference internal" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset" title="protac_degradation_predictor.protac_dataset.PROTAC_Dataset"><em>PROTAC_Dataset</em></a>)</p></li>
+<li><p><strong>test_ds</strong> (<a class="reference internal" href="#protac_degradation_predictor.protac_dataset.PROTAC_Dataset" title="protac_degradation_predictor.protac_dataset.PROTAC_Dataset"><em>PROTAC_Dataset</em></a><em> | </em><em>None</em>)</p></li>
+<li><p><strong>batch_size</strong> (<em>int</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p>tuple</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.pytorch_models.load_model">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.pytorch_models.</span></span><span class="sig-name descname"><span class="pre">load_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ckpt_path</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.pytorch_models.load_model" title="Link to this definition"></a></dt>
+<dd><p>Load a PROTAC model from a checkpoint.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>ckpt_path</strong> (<em>str</em>) – The path to the checkpoint.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The loaded model.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><a class="reference internal" href="#protac_degradation_predictor.pytorch_models.PROTAC_Model" title="protac_degradation_predictor.pytorch_models.PROTAC_Model">PROTAC_Model</a></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-protac_degradation_predictor.sklearn_models">
+<span id="protac-degradation-predictor-sklearn-models-module"></span><h2>protac_degradation_predictor.sklearn_models module<a class="headerlink" href="#module-protac_degradation_predictor.sklearn_models" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.sklearn_models.train_sklearn_model">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.sklearn_models.</span></span><span class="sig-name descname"><span class="pre">train_sklearn_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">clf</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">protein2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cell2embedding</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">smiles2fp</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">train_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Active'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">use_single_scaler</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">True</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.sklearn_models.train_sklearn_model" title="Link to this definition"></a></dt>
+<dd><p>Train a classifier model on train and val sets and evaluate it on a test set.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>clf</strong> (<em>ClassifierMixin</em>) – The classifier model to train and evaluate.</p></li>
+<li><p><strong>train_df</strong> (<em>pd.DataFrame</em>) – The training set.</p></li>
+<li><p><strong>val_df</strong> (<em>pd.DataFrame</em>) – The validation set.</p></li>
+<li><p><strong>test_df</strong> (<em>Optional</em><em>[</em><em>pd.DataFrame</em><em>]</em>) – The test set.</p></li>
+<li><p><strong>protein2embedding</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>cell2embedding</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>smiles2fp</strong> (<em>Dict</em>)</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>)</p></li>
+<li><p><strong>use_single_scaler</strong> (<em>bool</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The trained model and the metrics.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>Tuple[ClassifierMixin, nn.ModuleDict]</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.sklearn_models.suggest_random_forest">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.sklearn_models.</span></span><span class="sig-name descname"><span class="pre">suggest_random_forest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">trial</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.sklearn_models.suggest_random_forest" title="Link to this definition"></a></dt>
+<dd><p>Suggest hyperparameters for a Random Forest classifier.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>trial</strong> (<em>optuna.Trial</em>) – The Optuna trial object.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The Random Forest classifier with the suggested hyperparameters.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ClassifierMixin</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.sklearn_models.suggest_logistic_regression">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.sklearn_models.</span></span><span class="sig-name descname"><span class="pre">suggest_logistic_regression</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">trial</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.sklearn_models.suggest_logistic_regression" title="Link to this definition"></a></dt>
+<dd><p>Suggest hyperparameters for a Logistic Regression classifier.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>trial</strong> (<em>optuna.Trial</em>) – The Optuna trial object.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The Logistic Regression classifier with the suggested hyperparameters.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ClassifierMixin</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.sklearn_models.suggest_svc">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.sklearn_models.</span></span><span class="sig-name descname"><span class="pre">suggest_svc</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">trial</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.sklearn_models.suggest_svc" title="Link to this definition"></a></dt>
+<dd><p>Suggest hyperparameters for an SVC classifier.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>trial</strong> (<em>optuna.Trial</em>) – The Optuna trial object.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The SVC classifier with the suggested hyperparameters.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ClassifierMixin</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.sklearn_models.suggest_gradient_boosting">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.sklearn_models.</span></span><span class="sig-name descname"><span class="pre">suggest_gradient_boosting</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">trial</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.sklearn_models.suggest_gradient_boosting" title="Link to this definition"></a></dt>
+<dd><p>Suggest hyperparameters for a Gradient Boosting classifier.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>trial</strong> (<em>optuna.Trial</em>) – The Optuna trial object.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>The Gradient Boosting classifier with the suggested hyperparameters.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>ClassifierMixin</p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="module-protac_degradation_predictor">
+<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-protac_degradation_predictor" title="Link to this heading"></a></h2>
+</section>
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+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#module-protac_degradation_predictor.config">protac_degradation_predictor.config module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#module-protac_degradation_predictor.data_utils">protac_degradation_predictor.data_utils module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#module-protac_degradation_predictor.optuna_utils">protac_degradation_predictor.optuna_utils module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#module-protac_degradation_predictor.optuna_utils_xgboost">protac_degradation_predictor.optuna_utils_xgboost module</a></li>
+<li class="toctree-l3"><a class="reference internal" href="protac_degradation_predictor.html#module-protac_degradation_predictor.protac_dataset">protac_degradation_predictor.protac_dataset module</a></li>
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+             
+  <section id="protac-degradation-predictor-optuna-package">
+<h1>protac_degradation_predictor.optuna package<a class="headerlink" href="#protac-degradation-predictor-optuna-package" title="Link to this heading"></a></h1>
+<section id="submodules">
+<h2>Submodules<a class="headerlink" href="#submodules" title="Link to this heading"></a></h2>
+</section>
+<section id="protac-degradation-predictor-optuna-pytorch-models-module">
+<h2>protac_degradation_predictor.optuna.pytorch_models module<a class="headerlink" href="#protac-degradation-predictor-optuna-pytorch-models-module" title="Link to this heading"></a></h2>
+</section>
+<section id="protac-degradation-predictor-optuna-sklearn-models-module">
+<h2>protac_degradation_predictor.optuna.sklearn_models module<a class="headerlink" href="#protac-degradation-predictor-optuna-sklearn-models-module" title="Link to this heading"></a></h2>
+</section>
+<section id="module-protac_degradation_predictor.optuna.utils">
+<span id="protac-degradation-predictor-optuna-utils-module"></span><h2>protac_degradation_predictor.optuna.utils module<a class="headerlink" href="#module-protac_degradation_predictor.optuna.utils" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.optuna.utils.get_dataframe_stats">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.optuna.utils.</span></span><span class="sig-name descname"><span class="pre">get_dataframe_stats</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">train_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">val_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_df</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Active'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.optuna.utils.get_dataframe_stats" title="Link to this definition"></a></dt>
+<dd><p>Get some statistics from the dataframes.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>train_df</strong> (<em>pd.DataFrame</em>) – The training set.</p></li>
+<li><p><strong>val_df</strong> (<em>pd.DataFrame</em>) – The validation set.</p></li>
+<li><p><strong>test_df</strong> (<em>pd.DataFrame</em>) – The test set.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Dict</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="protac_degradation_predictor.optuna.utils.get_majority_vote_metrics">
+<span class="sig-prename descclassname"><span class="pre">protac_degradation_predictor.optuna.utils.</span></span><span class="sig-name descname"><span class="pre">get_majority_vote_metrics</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">test_preds</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">test_df</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">active_label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Active'</span></span></em><span class="sig-paren">)</span><a class="headerlink" href="#protac_degradation_predictor.optuna.utils.get_majority_vote_metrics" title="Link to this definition"></a></dt>
+<dd><p>Get the majority vote metrics.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>test_preds</strong> (<em>List</em>)</p></li>
+<li><p><strong>test_df</strong> (<em>DataFrame</em>)</p></li>
+<li><p><strong>active_label</strong> (<em>str</em>)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Return type<span class="colon">:</span></dt>
+<dd class="field-even"><p><em>Dict</em></p>
+</dd>
+</dl>
+</dd></dl>
+
+</section>
+<section id="protac-degradation-predictor-optuna-xgboost-models-module">
+<h2>protac_degradation_predictor.optuna.xgboost_models module<a class="headerlink" href="#protac-degradation-predictor-optuna-xgboost-models-module" title="Link to this heading"></a></h2>
+</section>
+<section id="module-protac_degradation_predictor.optuna">
+<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-protac_degradation_predictor.optuna" title="Link to this heading"></a></h2>
+</section>
+</section>
+
+
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diff --git a/docs/conf.py b/docs/conf.py
index 8a814789547c20d2040b9f3a2315250f35c7aa37..7b1ab7ea57ea3cf89c2d045c14b203c059cdf122 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -35,6 +35,14 @@ autodoc_mock_imports = [
     'pytorch_lightning',
 ]
 
+autodoc_default_options = {
+    'members': True,
+    'undoc-members': True,
+    'show-inheritance': True,
+    'inherited-members': True,
+    'member-order': 'bysource',
+ }
+
 # -- Options for HTML output -------------------------------------------------
 # https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
 
diff --git a/tests/test_degradation_prediction.py b/tests/test_degradation_prediction.py
index 416f851bbcb04d15246455219ec444ae2c8ad738..9fc40105c1872ec953fc4443fff92743ee274142 100644
--- a/tests/test_degradation_prediction.py
+++ b/tests/test_degradation_prediction.py
@@ -8,6 +8,7 @@ sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
 from protac_degradation_predictor import (
     get_protac_active_proba,
     is_protac_active,
+    get_protac_embedding,
 )
 
 import torch
@@ -114,4 +115,38 @@ def test_active_proba_xgboost():
         study_type='similarity',
     )
 
-    print(f'[XGBoost] Active probability: {active_prob} (CPU)')
\ No newline at end of file
+    print(f'[XGBoost] Active probability: {active_prob} (CPU)')
+
+
+def test_embeddings():
+    protac_smiles = 'Cc1ncsc1-c1ccc([C@H](C)NC(=O)[C@@H]2C[C@@H](O)CN2C(=O)[C@@H](NC(=O)CC(=O)N2CCN(CC[C@H](CSc3ccccc3)Nc3ccc(S(=O)(=O)NC(=O)c4ccc(N5CCN(CC6=C(c7ccc(Cl)cc7)CCC(C)(C)C6)CC5)cc4)cc3S(=O)(=O)C(F)(F)F)CC2)C(C)(C)C)cc1'
+    e3_ligase = 'VHL'
+    target_uniprot = 'Q07817'
+    cell_line = 'MOLT-4'
+    device = 'cpu'
+
+    protac_emb = get_protac_embedding(
+        protac_smiles=protac_smiles,
+        e3_ligase=e3_ligase,
+        target_uniprot=target_uniprot,
+        cell_line=cell_line,
+        device=device,
+        study_type='similarity',
+    )
+
+    print(f'Embeddings: {protac_emb}')
+    for model, emb in protac_emb.items():
+        print(f'\t- shape: {emb.shape}')
+
+    protac_emb = get_protac_embedding(
+        protac_smiles=[protac_smiles] * 8,
+        e3_ligase=[e3_ligase] * 8,
+        target_uniprot=[target_uniprot] * 8,
+        cell_line=[cell_line] * 8,
+        device=device,
+        study_type='similarity',
+    )
+
+    print(f'Embeddings (batched): {protac_emb}')
+    for model, emb in protac_emb.items():
+        print(f'\t- shape: {emb.shape}')
\ No newline at end of file