Accurately identifying sites of metabolism (SoM) mediated by cytochrome P450 (CYP) enzymes, which are responsible for drug metabolism in the body, is critical in the early stage of drug discovery and development. Current computational methods for CYP-mediated SoM prediction face several challenges, including limitations to traditional machine learning models at the atomic level, heavy reliance on complex feature engineering, and the lack of interpretability relevant to medicinal chemistry. Here, we propose GraphCySoM, a novel molecule-level modeling approach based on graph neural networks, utilizing lightweight features and interpretable annotations on substructures, to effectively and interpretably predict CYP-mediated SoM. Unlike computationally expensive atomic descriptors derived from resource-intensive chemistry or even quantum chemistry calculations, we emphasize that graph-based molecular modeling initialized solely with lightweight features enables the adaptive learning of molecular topology through message-passing mechanisms combined with various aggregation kernels. Extensive ablation experiments demonstrate that GraphCySoM significantly outperforms baseline models and achieves superior performance compared with competing methods while exhibiting advantages in computational efficiency. Moreover, the attention mechanism and subgraph information bottlenecks are incorporated to analyze node importance and feature significance, resulting in mining substructures associated with the SoM. To the best of our knowledge, this is the first comprehensive study of CYP-mediated SoM using molecule-level modeling and interpretable technology. Our method achieves new state-of-the-art performance and provides potential insights into the molecular and pharmacological mechanisms underlying drug metabolism catalyzed by CYP enzymes. All source files and trained models are freely available at https://github.com/liyigerry/GraphCySoM.

中文翻译：

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通过分子水平图建模和子图信息瓶颈对细胞色素 p450 介导的代谢的改进和可解释预测。


准确识别细胞色素 P450 （CYP） 酶介导的代谢位点 （SoM），细胞色素 P450 （CYP） 酶负责体内药物代谢，在药物发现和开发的早期阶段至关重要。当前 CYP 介导的 SoM 预测的计算方法面临一些挑战，包括传统机器学习模型在原子水平上的局限性、对复杂特征工程的严重依赖以及缺乏与药物化学相关的可解释性。在这里，我们提出了 GraphCySoM，这是一种基于图神经网络的新型分子水平建模方法，利用轻量级特征和子结构上的可解释注释，以有效和可解释地预测 cyp 介导的 SoM。与从资源密集型化学甚至量子化学计算中得出的计算成本高昂的原子描述符不同，我们强调仅使用轻量级特征初始化的基于图的分子建模能够通过消息传递机制与各种聚合内核相结合来自适应学习分子拓扑。广泛的消融实验表明，GraphCySoM 的性能明显优于基线模型，与竞争方法相比，它实现了卓越的性能，同时在计算效率方面表现出优势。此外，结合注意力机制和子图信息瓶颈来分析节点重要性和特征显著性，从而挖掘与 SoM 相关的子结构。据我们所知，这是首次使用分子水平建模和可解释技术对 CYP 介导的 SoM 进行全面研究。 我们的方法实现了新的最先进的性能，并为 CYP 酶催化的药物代谢的分子和药理学机制提供了潜在的见解。所有源文件和训练的模型都可以在 https://github.com/liyigerry/GraphCySoM 免费获得。