用图神经网络预测全氟化合物肝毒性，助力新化学品安全设计
Uncovering the Mechanism of Hepatotoxiciy of PFAS Targeting L-FABP Using GCN and Computational Modeling
- 通过分子指纹构建图结构，节点为分子描述符，避免特征过拟合
- 模型准确预测全氟化合物对L-FABP蛋白的结合亲和力，揭示肝毒性机制
- 识别代表性化合物用于深入研究，适合环境毒理与药物研发人员
全氟及多氟烷基物质（PFAS）是持久性环境污染物，具有已知毒性与生物累积性。尽管部分PFAS被广泛研究，但多数化合物的毒性仍不明确，因缺乏直接毒理数据。本研究结合半监督图卷积网络（GCN）与分子描述符、指纹，提出新方法：通过分离分子指纹构建图结构，将描述符作为节点特征，精准捕捉PFAS的结构、理化及拓扑特性，有效避免高维特征导致的过拟合。无监督聚类进一步识别出代表性化合物，用于详细结合分析。结果表明，该模型显著提升对PFAS肝毒性预测的准确性，为新型PFAS化学设计与新安全法规制定提供依据。
原文摘要 · Abstract (English)
Per- and polyfluoroalkyl substances (PFAS) are persistent environmental pollutants with known toxicity and bioaccumulation issues. Their widespread industrial use and resistance to degradation have led to global environmental contamination and significant health concerns. While a minority of PFAS have been extensively studied, the toxicity of many PFAS remains poorly understood due to limited direct toxicological data. This study advances the predictive modeling of PFAS toxicity by combining semi-supervised graph convolutional networks (GCNs) with molecular descriptors and fingerprints. We propose a novel approach to enhance the prediction of PFAS binding affinities by isolating molecular fingerprints to construct graphs where then descriptors are set as the node features. This approach specifically captures the structural, physicochemical, and topological features of PFAS without overfitting due to an abundance of features. Unsupervised clustering then identifies representative compounds for detailed binding studies. Our results provide a more accurate ability to estimate PFAS hepatotoxicity to provide guidance in chemical discovery of new PFAS and the development of new safety regulations.
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