The impact of cross-docked poses on performance of machine learning classifier for protein-ligand binding pose prediction.

ABSTRACT: Structure-based drug design depends on the detailed knowledge of the three-dimensional (3D) structures of protein-ligand binding complexes, but accurate prediction of ligand-binding poses is still a major challenge for molecular docking due to deficiency of scoring functions (SFs) and ignorance of protein flexibility upon ligand binding. In this study, based on a cross-docking dataset dedicatedly constructed from the PDBbind database, we developed several XGBoost-trained classifiers to discriminate the near-native binding poses from decoys, and systematically assessed their performance with/without the involvement of the cross-docked poses in the training/test sets. The calculation results illustrate that using Extended Connectivity Interaction Features (ECIF), Vina energy terms and dockin

SUBMITTER: Shen C 

PROVIDER: S-EPMC8520186 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature