RASPD+: Fast Protein-Ligand Binding Free Energy Prediction Using Simplified Physicochemical Features.

ABSTRACT: The virtual screening of large numbers of compounds against target protein binding sites has become an integral component of drug discovery workflows. This screening is often done by computationally docking ligands into a protein binding site of interest, but this has the drawback of a large number of poses that must be evaluated to obtain accurate estimates of protein-ligand binding affinity. We here introduce a fast pre-filtering method for ligand prioritization that is based on a set of machine learning models and uses simple pose-invariant physicochemical descriptors of the ligands and the protein binding pocket. Our method, Rapid Screening with Physicochemical Descriptors + machine learning (RASPD+), is trained on PDBbind data and achieves a regression performance that is better than

SUBMITTER: Holderbach S 

PROVIDER: S-EPMC7773945 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature