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

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 structures into key physical parameters of non-bonded interactions in classical force fields, thereby enhancing accurate absolute binding free energy (ABFE) calculations. LumiNet is designed to be highly interpretable, offering detailed insights into atomic interactions within protein-ligand complexes, pinpointing relatively important atom pairs or groups. Our semi-supervised learning strategy enables LumiNet to adapt to new targets with fewer data points than other data-driven methods, making it more relevant for real-world drug discovery. Benchmarks show that LumiNet outperforms the current state-of-the-art model by 18.5% on the PDE10A dataset, and rivals the FEP+ method in some tests with a speed improvement of several orders of magnitude. We applied LumiNet in the scaffold hopping process, which accurately guided the discovery of the optimal ligands. Furthermore, we provide a web service for the research community to test LumiNet. The visualization of predicted inter-molecular energy contributions is expected to provide practical value in drug discovery projects.