In drug discovery, determining the binding affinity and functional effects of small-molecule ligands on proteins is critical. Current computational methods can predict these protein–ligand interaction properties but often lose accuracy without high-resolution protein structures and falter in predicting functional effects. Here we introduce PSICHIC (PhySIcoCHemICal graph neural network), a framework incorporating physicochemical constraints to decode interaction fingerprints directly from sequence data alone. This enables PSICHIC to attain capabilities in decoding mechanisms underlying protein–ligand interactions, achieving state-of-the-art accuracy and interpretability. Trained on identical protein–ligand pairs without structural data, PSICHIC matched and even surpassed leading structure-based methods in binding-affinity prediction. In an experimental library screening for adenosine A₁ receptor agonists, PSICHIC discerned functional effects effectively, ranking the sole novel agonist within the top three. PSICHIC’s interpretable fingerprints identified protein residues and ligand atoms involved in interactions, and helped in unveiling selectivity determinants of protein–ligand interaction. We foresee PSICHIC reshaping virtual screening and deepening our understanding of protein–ligand interactions.

在药物发现中，确定小分子配体对蛋白质的结合亲和力和功能效应至关重要。目前的计算方法可以预测这些蛋白质-配体相互作用特性，但在没有高分辨率蛋白质结构的情况下常常会失去准确性，并且在预测功能效应方面表现不佳。在这里，我们介绍 PSICHIC（PhySIcoCHemICal 图神经网络），这是一个结合物理化学约束的框架，可以直接从序列数据中解码相互作用指纹。这使得 PSICHIC 能够解码蛋白质-配体相互作用的机制，从而实现最先进的准确性和可解释性。在没有结构数据的情况下对相同的蛋白质-配体对进行训练，PSICHIC 在结合亲和力预测方面匹配甚至超越了基于结构的领先方法。在腺苷 A ₁ 受体激动剂的实验库筛选中，PSICHIC 有效地识别了功能效应，将唯一的新型激动剂排在前三名之内。 PSICHIC 的可解释指纹识别了参与相互作用的蛋白质残基和配体原子，并有助于揭示蛋白质-配体相互作用的选择性决定因素。我们预计 PSICHIC 将重塑虚拟筛选并加深我们对蛋白质-配体相互作用的理解。