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Classification of Cytochrome P450 Activities Using Machine Learning Methods

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Department of Gastroenterology & Hepatology, University Hospital Basel, University of Basel, Basel, Switzerland, and Freiburg Center for Data Analysis and Modelling, Albert-Ludwigs-University, Freiburg, Germany
* Corresponding author: Prof. Dr. Juergen Drewe, Department of Gastroenterology & Hepatology, University Hospital of Basel, Petersgraben 4, CH-4031 Basel, Switzerland. E-mail: [email protected]. Phone: +41-61-265 3848. Fax: +41-61-265 8581.
†University of Basel.
‡Albert-Ludwigs-University.
Cite this: Mol. Pharmaceutics 2009, 6, 6, 1920–1926
Publication Date (Web):October 8, 2009
https://doi.org/10.1021/mp900217x
Copyright © 2009 American Chemical Society

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    Abstract

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    The cytochrome P450 (CYP) system plays an integral part in the metabolism of drugs and other xenobiotics. Knowledge of the structural features required for interaction with any of the different isoforms of the CYP system is therefore immensely valuable in early drug discovery. In this paper, we focus on three major isoforms (CYP 1A2, CYP 2D6, and CYP 3A4) and present a data set of 335 structurally diverse drug compounds classified for their interaction (as substrate, inhibitor, or any interaction) with these isoforms. We also present machine learning models using a variety of commonly used methods (k-nearest neighbors, decision tree induction using the CHAID and CRT algorithms, random forests, artificial neural networks, and support vector machines using the radial basis function (RBF) and homogeneous polynomials as kernel functions). We discuss the physicochemical features relevant for each end point and compare it to similar studies. Many of these models perform exceptionally well, even with 10-fold cross-validation, yielding corrected classification rates of 81.7 to 91.9% for CYP 1A2, 89.2 to 92.9% for CYP 2D6, and 87.4 to 89.9% for CYP3A4. Our models help in understanding the structural requirements for CYP interactions and can serve as sensitive tools in virtual screenings and lead optimization for toxicological profiles in drug discovery.

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    Table of predictions for 353 compounds. This material is available free of charge via the Internet at http://pubs.acs.org.

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