Background: Graph Neural Networks (GNN) have emerged in very recent years as a powerful tool for supporting in silico Virtual Screening. In this work we present a GNN which uses Graph Convolutional architectures to achieve very accurate multi-target screening. We also devised a hierarchical Explainable Artificial Intelligence (XAI) technique to catch information directly at atom, ring, and whole molecule level by leveraging the message passing mechanism. In this way, we find the most relevant moieties involved in bioactivity prediction. Results: We report a state-of-the-art GNN classifier on twenty Cyclin-dependent Kinase targets in support of VS. Our classifier outperforms previous SOTA approaches proposed by the authors. Moreover, a CDK1-only high-sensitivity version of the GNN has been designed to use our explainer in order to avoid the inherent bias of multi-class models. The hierarchical explainer has been validated by an expert chemist on 19 approved drugs on CDK1. Our explainer provided information in accordance to the docking analysis for 17 out of the 19 test drugs. Conclusion: Our approach is a valid support for shortening both the screening and the hit-to-lead phase. Detailed knowledge about the molecular substructures that play a role in the inhibitory action, can help the computational chemist to gain insights into the pharmacophoric function of the molecule also for repurposing purposes.

翻译：背景：近年来，图神经网络已成为支持计算虚拟筛选的强大工具。本研究提出了一种采用图卷积架构的图神经网络，实现了高精度的多靶点筛选。同时，我们设计了一种层级可解释人工智能技术，通过利用消息传递机制直接获取原子、环及整个分子层面的信息，从而找到与生物活性预测最相关的分子基团。结果：我们针对二十个细胞周期蛋白依赖性激酶靶点，报告了支持虚拟筛选的最先进图神经网络分类器。该分类器性能优于作者先前提出的最高基准方法。此外，为规避多类别模型的固有偏差，我们设计了仅针对CDK1的高灵敏度版本图神经网络，并借助可解释器进行优化。该层级可解释器由资深化学家基于19种已获批CDK1药物完成验证，其中17种测试药物的解释结果与分子对接分析一致。结论：我们的方法可有效缩短虚拟筛选及先导化合物优化阶段。深入解析参与抑制作用的分子亚结构，有助于计算化学家理解药效团功能，并为药物重定位提供新视角。