MM-DRPNet: A multimodal dynamic radial partitioning network for enhanced protein-ligand binding affinity prediction.

ABSTRACT: Accurate prediction of drug-target binding affinity remains a fundamental challenge in contemporary drug discovery. Despite significant advances in computational methods for protein-ligand binding affinity prediction, current approaches still face substantial limitations in prediction accuracy. Moreover, the prevalent methodologies often overlook critical three-dimensional (3D) structural information, thereby constraining their practical utility in computer-aided drug design (CADD). Here we present MM-DRPNet, a multimodal deep learning framework that enhances binding affinity prediction by integrating protein-ligand structural information with interaction features and physicochemical properties. The core innovation lies in our dynamic radial partitioning (DRP) algorithm, which adaptively s

SUBMITTER: Liu D 

PROVIDER: S-EPMC11683220 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature