DOI: 10.4231/scf2-qj02
title: Deep Learning of CYP450 Binding of Small Molecules by Quantum Information

description: &lt;p&gt;It is currently considered by JCIM or Journal of Chemical Information and Modeling.&lt;/p&gt;

&lt;div&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z"&gt;The data&lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z s-lparen"&gt; &lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z h-lparen"&gt;(13,443&lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z"&gt; numpy files) contains the representation of the molecular electronic properties from the curated AID1851 dataset. Out of the initial dataset, 13,443 molecules successfully underwent quantum mechanical calculations to generate the manifold embedding of molecular surfaces&lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z s-lparen"&gt; &lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z h-lparen"&gt;(MEMS),&lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z"&gt; which include electronic descriptors such as electrostatic potential&lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z s-lparen"&gt; &lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z h-lparen"&gt;(ESP),&lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z"&gt; nucleophilic and electrophilic Fukui functions&lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z s-lparen"&gt; &lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z h-lparen"&gt;(F+&lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z"&gt; and F-), and the dual descriptor&lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z s-lparen"&gt; &lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z h-lparen"&gt;(F&lt;sup&gt;2&lt;/sup&gt;).&lt;/span&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z"&gt; These quantum-based descriptors are crucial for capturing the nuanced electronic properties of the molecules, which are pivotal in understanding their interactions with CYP450 enzymes. Each file contains the MEMS for an individual molecule, effectively translating high-dimensional quantum electronic properties into a structured and lower-dimensional format, specifically designed for machine learning applications. The detailed electronic structure encoded in these numpy files allows us to predict whether a molecule is likely to inhibit specific CYP450 isozymes, such as 1A2, 2C9, 2C19, 2D6, and 3A4.&lt;/span&gt;&lt;/div&gt;

&lt;div&gt; &lt;/div&gt;

&lt;div&gt;&lt;span class="ace-all-bold-hthree"&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z"&gt;&lt;b&gt;Key features:&lt;/b&gt;&lt;/span&gt;&lt;/span&gt;&lt;/div&gt;

&lt;ol class="listtype-number listindent1 list-number1" start="1" style="list-style-type: decimal;"&gt;
 &lt;li&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z"&gt;Each numpy file holds the MEMS derived from key electronic descriptors which are essential for capturing molecular interactions at a detailed quantum level.&lt;/span&gt;&lt;/li&gt;
 &lt;li&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z"&gt;MEMS not only encode the magnitude of these electronic properties but also their spatial distribution on the molecular surface, which is critical for understanding how molecules interact with the active sites of CYP450 enzymes.&lt;/span&gt;&lt;/li&gt;
 &lt;li&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z"&gt;The data was used to train and test deep learning models, where the numpy files served as inputs for predicting CYP450 inhibition.&lt;/span&gt;&lt;/li&gt;
 &lt;li&gt;&lt;span class="author-d-1gg9uz65z1iz85zgdz68zmqkz84zo2qotvohlxz76zz82zz80zz80zgqq2bqz68zv4yi8dz87z10z74zhz78zxz73zz77z"&gt;MEMS from these files were used for shape-context analysis, enabling the comparison of the spatial distribution of electronic properties across molecules. &lt;/span&gt;&lt;/li&gt;
&lt;/ol&gt;
description: We implemented the Manifold Embedding of Molecular Surface approach, which retains the quantum mechanical characteristics of molecules, to predict a drug's likelihood of binding to cytochrome P450 enzymes by deep learning.