SG-ML-PLAP: A structure-guided machine learning-based scoring function for protein-ligand binding affinity prediction.

ABSTRACT: Computational methods to predict binding affinity of protein-ligand complex have been used extensively to design inhibitors for proteins selected as drug targets. In recent years machine learning (ML) is being increasingly used for design of drugs/inhibitors. However, ranking compounds as per their experimental binding affinity has remained a major challenge. Therefore, it is necessary to develop ML-based scoring function (MLSF) for predicting the binding affinity of protein-ligand complexes. In this work, protein-ligand interaction features, namely, extended connectivity interaction fingerprints (ECIF), derived from the PDBbind dataset have been used to build ML models for binding affinity prediction. The benchmarking has been done on the Comparative Assessment of Scoring Functions (CASF)

SUBMITTER: Pal S 

PROVIDER: S-EPMC11633052 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature