通过层次化 Grad-CAM 图可解释性揭示分子基团
Unveiling Molecular Moieties through Hierarchical Grad-CAM Graph Explainability
AI总结：
本研究提出层次化Grad-CAM图解释器（HGE），用于解释图神经网络在虚拟筛选中的预测，识别驱动蛋白质-配体结合的分子基团，经实验验证可辅助药物筛选与重定位。
摘要：
背景：虚拟筛选（VS）已成为药物发现中的重要工具，能够快速且经济地识别潜在的生物活性分子。在最近的进展中，图神经网络（GNNs）因其使用基于图的表示来建模复杂分子结构的能力而受到重视。然而，整合可解释性方法以阐明分子亚结构对生物活性的具体贡献仍然是一个重大挑战。这一局限性阻碍了预测模型的可解释性以及新型治疗药物的合理设计。结果：我们在一个小分子数据集上训练了20个GNN模型，目标是预测它们对激酶家族中20个不同蛋白质靶标的活性。这些分类器在虚拟筛选任务中达到了最先进的性能，在不同靶标上表现出高准确性和鲁棒性。基于这些模型，我们实现了层次化Grad-CAM图解释器（HGE）框架，能够深入分析驱动蛋白质-配体结合稳定的分子基团。HGE利用原子、环和整个分子层面的Grad-CAM解释，借助消息传递机制突出最相关的化学基团。与文献中实验数据的验证证实了该解释器能够识别药物的分子模式，并将其正确注释到已知靶标。结论：我们的方法可能为缩短筛选和命中发现过程提供有效支持。对在结合过程中起作用的分子亚结构的详细了解，可以帮助计算化学家深入了解结构优化以及药物重定位任务。
英文摘要：
Background: Virtual Screening (VS) has become an essential tool in drug discovery, enabling the rapid and cost-effective identification of potential bioactive molecules. Among recent advancements, Graph Neural Networks (GNNs) have gained prominence for their ability to model complex molecular structures using graph-based representations. However, the integration of explainable methods to elucidate the specific contributions of molecular substructures to biological activity remains a significant challenge. This limitation hampers both the interpretability of predictive models and the rational design of novel therapeutics. Results: We trained 20 GNN models on a dataset of small molecules with the goal of predicting their activity on 20 distinct protein targets from the Kinase family. These classifiers achieved state-of-the-art performance in virtual screening tasks, demonstrating high accuracy and robustness on different targets. Building upon these models, we implemented the Hierarchical Grad-CAM graph Explainer (HGE) framework, enabling an in-depth analysis of the molecular moieties driving protein-ligand binding stabilization. HGE exploits Grad-CAM explanations at the atom, ring, and whole-molecule levels, leveraging the message-passing mechanism to highlight the most relevant chemical moieties. Validation against experimental data from the literature confirmed the ability of the explainer to recognize a molecular pattern of drugs and correctly annotate them to the known target. Conclusion: Our approach may represent a valid support to shorten both the screening and the hit discovery process. Detailed knowledge of the molecular substructures that play a role in the binding process can help the computational chemist to gain insights into the structure optimization, as well as in drug repurposing tasks.
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