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Description

Abstract: Drug discovery is an expensive, lengthy, and sometimes dangerous process. The ability to make accurate computational predictions of drug binding would greatly improve the cost-effectiveness and safety of drug discovery and development. This study incorporates ensemble docking, the use of multiple protein conformations extracted from a molecular dynamics trajectory to perform docking calculations, with additional biomedical data sources and machine learning algorithms to improve the prediction of drug binding. We found that we can greatly increase the classification accuracy of an active vs a decoy compound using these methods over docking scores alone. The best results seen here come from having an individual protein conformation that produces binding features that correlate well with the active vs. decoy classification, in which case we achieve over 99% accuracy. The ability to confidently make accurate predictions on drug binding would allow for computational polypharamacological networks with insights into side-effect prediction, drug-repurposing, and drug efficacy.

Learning Objective 1: Demostrate how ensemble docking results can be used in machine learning models to predict drug binding

Authors:

Fatemah Alghamedy, University of Kentucky
Jeevith Bopaiah, University of Kentucky
Derek Jones, University of Kentucky
Xiaofei Zhang, University of Kentucky
Heidi Weiss, Markey Cancer Center
Sally Ellingson (Presenter)
University of Kentucky

Presentation Materials:

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