Using machine learning to improve ensemble docking for drug discovery.

ABSTRACT: Ensemble docking has provided an inexpensive method to account for receptor flexibility in molecular docking for virtual screening. Unfortunately, as there is no rigorous theory to connect the docking scores from multiple structures to measured activity, researchers have not yet come up with effective ways to use these scores to classify compounds into actives and inactives. This shortcoming has led to the decrease, rather than an increase in the performance of classifying compounds when more structures are added to the ensemble. Previously, we suggested machine learning, implemented in the form of a naïve Bayesian model could alleviate this problem. However, the naïve Bayesian model assumed that the probabilities of observing the docking scores to different structures to be independent. T

SUBMITTER: Chandak T 

PROVIDER: S-EPMC7815257 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature