数字农科院2.0

Novel graph neural network reveals binding mechanisms and environmental risks of PAHs interaction with estrogen receptor B

文献类型： 外文期刊

作者： Ying Ren;Xuan Wu;Ziming Xi;Kuok Ho Daniel Tang;Xianlai Zeng;Ronghua Li;Junting Pan

作者机构：

关键词： Estrogen receptor β;Machine learning;Molecular docking;Polycyclic aromatic hydrocarbons

期刊名称： Environmental Pollution

ISSN： 1873-6424

年卷期： 2025 年 384 卷

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Polycyclic aromatic hydrocarbons (PAHs) are widespread environmental contaminants that threaten ecosystems and human health by binding to estrogen receptor β (ERβ) and disrupting endocrine function. Accurately identifying and predicting the interactions between PAHs and ERβ remains a key challenge in environmental science. To address this, we propose a Multi-Scale Dual-Stream Graph Attention Network (MS-DSGAT) for predicting PAHs-ERβ binding affinity. MS-DSGAT outperforms traditional machine learning models, achieving the highest prediction accuracy (R2 = 0.95) while offering strong interpretability. MS-DSGAT assigns Positional Attention Weights (PAW) to atoms in each PAH molecule, highlighting the critical influence of functional groups such as hydroxyl (-OH), amino (-NH2), and sulfonic acid (-SO3H) on binding affinity. These insights provide valuable guidance for targeted molecular modifications. Virtual screening of 6357 external chemicals using MS-DSGAT identified approximately 6.6 % of the chemicals as high-affinity binders and 66.4 % as moderate binders. Molecular docking results further validate the model's interpretations, confirming functional groups as key determinants of binding affinity. By leveraging molecular graph representation, MS-DSGAT effectively predicts PAHs-ERβ interactions and can be extended to study other ligand-receptor interactions to identify potential endocrine disruptors, toxicants, and related compounds.

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