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.