AI-based prediction of protein-ligand binding affinity and discovery of potential natural product inhibitors against ERK2.

ABSTRACT: Determination of protein-ligand binding affinity (PLA) is a key technological tool in hit discovery and lead optimization, which is critical to the drug development process. PLA can be determined directly by experimental methods, but it is time-consuming and costly. In recent years, deep learning has been widely applied to PLA prediction, the key of which lies in the comprehensive and accurate representation of proteins and ligands. In this study, we proposed a multi-modal deep learning model based on the early fusion strategy, called DeepLIP, to improve PLA prediction by integrating multi-level information, and further used it for virtual screening of extracellular signal-regulated protein kinase 2 (ERK2), an ideal target for cancer treatment. Experimental results from model evaluation sh

SUBMITTER: Yang R 

PROVIDER: S-EPMC11145815 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature