A Point Cloud Graph Neural Network for Protein-Ligand Binding Site Prediction.

ABSTRACT: Predicting protein-ligand binding sites is an integral part of structural biology and drug design. A comprehensive understanding of these binding sites is essential for advancing drug innovation, elucidating mechanisms of biological function, and exploring the nature of disease. However, accurately identifying protein-ligand binding sites remains a challenging task. To address this, we propose PGpocket, a geometric deep learning-based framework to improve protein-ligand binding site prediction. Initially, the protein surface is converted into a point cloud, and then the geometric and chemical properties of each point are calculated. Subsequently, the point cloud graph is constructed based on the inter-point distances, and the point cloud graph neural network (GNN) is applied to extract and

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC11394757 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature