DR-Predictor: Incorporating Flexible Docking with Specialized Electronic Reactivity and Machine Learning Techniques to Predict CYP-Mediated Sites of Metabolism-Reference-Cited by-同舟云学术

DR-Predictor: Incorporating Flexible Docking with Specialized Electronic Reactivity and Machine Learning Techniques to Predict CYP-Mediated Sites of Metabolism

Published:2013-11-21 Issue:12 Volume:53 Page:3352-3366
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Huang Tao-wei,Zaretzki Jed¹,Bergeron Charles²,Bennett Kristin P.,Breneman Curt M.

Affiliation:

1. Laboratory and Genomic Medicine Division, Washington University, St. Louis, Missouri 63130, United States

2. Albany College of Pharmacy and Health Sciences, Union University, Albany, New York 12208, United States

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/ci4004688

Reference49 articles.

1. Virtual Screening for Cytochromes P450: Successes of Machine Learning Filters

2. SMARTCyp: A 2D Method for Prediction of Cytochrome P450-Mediated Drug Metabolism

3. Prediction of Metabolism by Cytochrome P450 2C9: Alignment and Docking Studies of a Validated Database of Substrates

4. A Model for Predicting Likely Sites of CYP3A4-mediated Metabolism on Drug-like Molecules

5. Empirical Regioselectivity Models for Human Cytochromes P450 3A4, 2D6, and 2C9

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