Improving scoring-docking-screening powers of protein-ligand scoring functions using random forest.

ABSTRACT: The development of new protein-ligand scoring functions using machine learning algorithms, such as random forest, has been of significant interest. By efficiently utilizing expanded feature sets and a large set of experimental data, random forest based scoring functions (RFbScore) can achieve better correlations to experimental protein-ligand binding data with known crystal structures; however, more extensive tests indicate that such enhancement in scoring power comes with significant under-performance in docking and screening power tests compared to traditional scoring functions. In this work, to improve scoring-docking-screening powers of protein-ligand docking functions simultaneously, we have introduced a Δ_vina RF parameterization and feature selection framework based on ran

SUBMITTER: Wang C 

PROVIDER: S-EPMC5140681 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature