GAABind: a geometry-aware attention-based network for accurate protein-ligand binding pose and binding affinity prediction.

ABSTRACT: Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket-ligand binding pose and binding affinity within a multi-task learning framework. Specifical

SUBMITTER: Tan H 

PROVIDER: S-EPMC10724026 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature