A machine learning-based method to improve docking scoring functions and its application to drug repurposing.

ABSTRACT: Docking scoring functions are notoriously weak predictors of binding affinity. They typically assign a common set of weights to the individual energy terms that contribute to the overall energy score; however, these weights should be gene family dependent. In addition, they incorrectly assume that individual interactions contribute toward the total binding affinity in an additive manner. In reality, noncovalent interactions often depend on one another in a nonlinear manner. In this paper, we show how the use of support vector machines (SVMs), trained by associating sets of individual energy terms retrieved from molecular docking with the known binding affinity of each compound from high-throughput screening experiments, can be used to improve the correlation between known binding affinitie

SUBMITTER: Kinnings SL 

PROVIDER: S-EPMC3076728 | biostudies-literature | 2011 Feb

REPOSITORIES: biostudies-literature