Accurate prediction of protein-ligand interactions by combining physical energy functions and graph-neural networks.

ABSTRACT: We introduce an advanced model for predicting protein-ligand interactions. Our approach combines the strengths of graph neural networks with physics-based scoring methods. Existing structure-based machine-learning models for protein-ligand binding prediction often fall short in practical virtual screening scenarios, hindered by the intricacies of binding poses, the chemical diversity of drug-like molecules, and the scarcity of crystallographic data for protein-ligand complexes. To overcome the limitations of existing machine learning-based prediction models, we propose a novel approach that fuses three independent neural network models. One classification model is designed to perform binary prediction of a given protein-ligand complex pose. The other two regression models are trained to pr

SUBMITTER: Hong Y 

PROVIDER: S-EPMC11536843 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature