Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks.

ABSTRACT: Computational prediction of ligand-target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions th

SUBMITTER: Pandey M 

PROVIDER: S-EPMC9416537 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature