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

作者：Qun Su, Jike Wang, Qiaolin Gou, Renling Hu, Linlong Jiang, Hui Zhang, Tianyue Wang, Yifei Liu, Chao Shen, Yu Kang, Chang‐Yu Hsieh, Tingjun Hou · 发表于：Chemical Science · 年份：2025 · DOI：10.1039/d4sc07405j · 被引用次数：9 · 研究领域：Computational Drug Discovery Methods、Protein Structure and Dynamics、Bioinformatics and Genomic Networks

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 ...