Improving protein-ligand docking and screening accuracies by incorporating a scoring function correction term.

ABSTRACT: Scoring functions are important components in molecular docking for structure-based drug discovery. Traditional scoring functions, generally empirical- or force field-based, are robust and have proven to be useful for identifying hits and lead optimizations. Although multiple highly accurate deep learning- or machine learning-based scoring functions have been developed, their direct applications for docking and screening are limited. We describe a novel strategy to develop a reliable protein-ligand scoring function by augmenting the traditional scoring function Vina score using a correction term (OnionNet-SFCT). The correction term is developed based on an AdaBoost random forest model, utilizing multiple layers of contacts formed between protein residues and ligand atoms. In addition to th

SUBMITTER: Zheng L 

PROVIDER: S-EPMC9116214 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature