Computing the relative binding affinity of ligands based on a pairwise binding comparison network

作者：Jie Yu, Zhaojun Li, Geng Chen, Xiangtai Kong, Jie Hu, Dingyan Wang, Duanhua Cao, Yanbei Li, Ruifeng Huo, Gang Wang, Xiaohong Liu, Hualiang Jiang, Xutong Li, Xiaomin Luo, Mingyue Zheng · 发表于：Nature Computational Science · 年份：2023 · DOI：10.1038/s43588-023-00529-9 · 被引用次数：40 · 研究领域：Computational Drug Discovery Methods、Machine Learning in Materials Science、Protein Structure and Dynamics

Structure-based lead optimization is an open challenge in drug discovery, which is still largely driven by hypotheses and depends on the experience of medicinal chemists. Here we propose a pairwise binding comparison network (PBCNet) based on a physics-informed graph attention mechanism, specifically tailored for ranking the relative binding affinity among congeneric ligands. Benchmarking on two held-out sets (provided by Schrödinger and Merck) containing over 460 ligands and 16 targets, PBCNet demonstrated substantial advantages in terms of both prediction accuracy and computational efficiency. Equipped with a fine-tuning operation, the performance of PBCNet reaches that of Schrödinger's FEP+, which is much more computationally intensive and requires substantial expert intervention. A further simulation-based experiment showed that active learning-optimized PBCNet may accelerate lead optimization campaigns by 473%. Finally, for the convenience of users, a web service for PBCNet is established to facilitate complex relative binding affinity prediction through an easy-to-operate graphical interface.