Machine learning accelerates MD-based binding pose prediction between ligands and proteins.

ABSTRACT:

Motivation

Fast and accurate prediction of protein-ligand binding structures is indispensable for structure-based drug design and accurate estimation of binding free energy of drug candidate molecules in drug discovery. Recently, accurate pose prediction methods based on short Molecular Dynamics (MD) simulations, such as MM-PBSA and MM-GBSA, among generated docking poses have been used. Since molecular structures obtained from MD simulation depend on the initial condition, taking the average over different initial conditions leads to better accuracy. Prediction accuracy of protein-ligand binding poses can be improved with multiple runs at different initial velocity.

Results

This paper shows that a machine learning method, called Best Arm Identification, can optimally control

SUBMITTER: Terayama K 

PROVIDER: S-EPMC6030886 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature