Improved Prediction of Ligand-Protein Binding Affinities by Meta-modeling.

ABSTRACT: The accurate screening of candidate drug ligands against target proteins through computational approaches is of prime interest to drug development efforts. Such virtual screening depends in part on methods to predict the binding affinity between ligands and proteins. Many computational models for binding affinity prediction have been developed, but with varying results across targets. Given that ensembling or meta-modeling approaches have shown great promise in reducing model-specific biases, we develop a framework to integrate published force-field-based empirical docking and sequence-based deep learning models. In building this framework, we evaluate many combinations of individual base models, training databases, and several meta-modeling approaches. We show that many of our meta-models

SUBMITTER: Lee HJ 

PROVIDER: S-EPMC11632770 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature