Robust protein-ligand interaction modeling through integrating physical laws and geometric knowledge for absolute binding free energy calculation.

ABSTRACT: Accurate estimation of protein-ligand (PL) binding free energies is a crucial task in medicinal chemistry and a critical measure of PL interaction modeling effectiveness. However, traditional computational methods are often computationally expensive and prone to errors. Recently, deep learning (DL)-based approaches for predicting PL interactions have gained enormous attention, but their accuracy and generalizability are hindered by data scarcity. In this study, we propose LumiNet, a versatile PL interaction modeling framework that bridges the gap between physics-based models and black-box algorithms. LumiNet utilizes a subgraph transformer to extract multiscale information from molecular graphs and employs geometric neural networks to integrate PL information, mapping atomic pair structure

SUBMITTER: Su Q 

PROVIDER: S-EPMC11848741 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature