Protein-ligand binding affinity (PLA) prediction is critical in drug discovery. Despite the notable advancements in machine learning-based approaches, existing methods struggle to jointly characterize local geometric organization and globally coordinated cross-molecular interactions, limiting their ability to model complex binding mechanisms. Here, we propose RicciBind, a geometric representation framework that integrates curvature-guided hierarchical structure learning with optimal transport (OT)-based cross-domain alignment to model molecular interactions. Specifically, RicciBind leverages Ricci curvature to capture local interaction tightness within molecular structures, enhancing structural awareness and organizing atomic interactions into curvature-aware hierarchical representations. An OT-based cluster matching mechanism then aligns protein and ligand clusters across heterogeneous domains under geometric constraints, enabling globally consistent correspondences and revealing higher-order interaction patterns beyond local neighborhoods. By coupling curvature-guided structure encoding with OT-driven cross-domain alignment, RicciBind effectively models complex interaction semantics and substantially improves both the accuracy and interpretability of binding affinity prediction. Extensive experiments demonstrate that RicciBind achieved superior predictive performance and generalization across PLA benchmarks and virtual screening tasks. Ablation studies further confirmed the essential role of Ricci curvature in enhancing molecular interaction representations.

翻译：蛋白质-配体结合亲和力预测是药物发现中的关键环节。尽管基于机器学习的方法取得了显著进展，现有方法仍难以共同表征局部几何组织与全局协调的跨分子相互作用，这限制了其建模复杂结合机制的能力。为此，本文提出RicciBind——一种几何表征框架，该框架将曲率引导的层次化结构学习与基于最优传输（OT）的跨域对齐相结合，以建模分子相互作用。具体地，RicciBind利用里奇曲率捕捉分子结构内的局部相互作用紧密度，增强结构感知能力，并将原子相互作用组织成曲率感知的层次化表征。随后，基于OT的聚类匹配机制在几何约束下对齐蛋白质与配体在异质域中的聚类，从而实现全局一致的对应关系，并揭示超越局部邻域的高阶相互作用模式。通过将曲率引导的结构编码与OT驱动的跨域对齐相结合，RicciBind有效建模了复杂的相互作用语义，并显著提升了结合亲和力预测的准确性与可解释性。大量实验表明，RicciBind在PLA基准测试和虚拟筛选任务中均实现了卓越的预测性能与泛化能力。消融研究进一步证实了里奇曲率在增强分子相互作用表征中的关键作用。