Improving ensemble docking for drug discovery by machine learning-Reference-Cited by-同舟云学术

Improving ensemble docking for drug discovery by machine learning

Published:2019-05 Issue:03 Volume:18 Page:1920001
ISSN:0219-6336
Container-title:Journal of Theoretical and Computational Chemistry
language:en
Short-container-title:J. Theor. Comput. Chem.

Author:

Wong Chung F.¹

Affiliation:

1. Department of Chemistry and Biochemistry and Center for Nanoscience, University of Missouri-St. Louis, Saint Louis, MO 63121, USA

Abstract

Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking. However, it is still unclear how best to use the docking scores from multiple structures to classify compounds into actives and inactives. Previous studies have also found that the performance of classification could decrease rather than increase with the number of structures included in the ensemble. Machine learning could help to alleviate these problems.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computational Theory and Mathematics,Physical and Theoretical Chemistry,Computer Science Applications

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0219633619200013

Reference38 articles.

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4. Drug Design for Protein Kinases and Phosphatases: Flexible-Receptor Docking, Binding Affinity and Specificity, and Drug-Binding Kinetics

5. Protein flexibility in docking and surface mapping

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