Developing robust methods for evaluating protein–ligand interactions has been a long-standing problem. Data-driven methods may memorize ligand and protein training data rather than learning protein–ligand interactions. Here we show a scoring approach called EquiScore, which utilizes a heterogeneous graph neural network to integrate physical prior knowledge and characterize protein–ligand interactions in equivariant geometric space. EquiScore is trained based on a new dataset constructed with multiple data augmentation strategies and a stringent redundancy-removal scheme. On two large external test sets, EquiScore consistently achieved top-ranking performance compared to 21 other methods. When EquiScore is used alongside different docking methods, it can effectively enhance the screening ability of these docking methods. EquiScore also showed good performance on the activity-ranking task of a series of structural analogues, indicating its potential to guide lead compound optimization. Finally, we investigated different levels of interpretability of EquiScore, which may provide more insights into structure-based drug design.

开发评估蛋白质-配体相互作用的可靠方法一直是一个长期存在的问题。数据驱动的方法可以记住配体和蛋白质训练数据，而不是学习蛋白质-配体相互作用。在这里，我们展示了一种称为 EquiScore 的评分方法，它利用异构图神经网络来整合物理先验知识并表征等变几何空间中的蛋白质-配体相互作用。 EquiScore 是基于使用多种数据增强策略和严格的冗余删除方案构建的新数据集进行训练的。在两个大型外部测试集上，与其他 21 种方法相比，EquiScore 始终获得一流的性能。当EquiScore与不同对接方法结合使用时，可以有效增强这些对接方法的筛选能力。 EquiScore 在一系列结构类似物的活性排序任务中也表现出了良好的性能，表明其指导先导化合物优化的潜力。最后，我们研究了 EquiScore 不同级别的可解释性，这可能为基于结构的药物设计提供更多见解。