A generalized protein-ligand scoring framework with balanced scoring, docking, ranking and screening powers.

ABSTRACT: Applying machine learning algorithms to protein-ligand scoring functions has aroused widespread attention in recent years due to the high predictive accuracy and affordable computational cost. Nevertheless, most machine learning-based scoring functions are only applicable to a specific task, e.g., binding affinity prediction, binding pose prediction or virtual screening, suggesting that the development of a scoring function with balanced performance in all critical tasks remains a grand challenge. To this end, we propose a novel parameterization strategy by introducing an adjustable binding affinity term that represents the correlation between the predicted outcomes and experimental data into the training of mixture density network. The resulting residue-atom distance likelihood pot

SUBMITTER: Shen C 

PROVIDER: S-EPMC10395315 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature