新型图形神经网络揭示了PAH与雌激素受体B相互作用的结合机制和环境风险
Ying Ren1, Xuan Wu1, Ziming Xi1
1College of Natural Resources and Environment, Northwest A&F University, Yangling, 712100, China.
Environmental pollution (Barking, Essex : 1987)
|August 28, 2025
概括
一个新的AI模型MS-DSGAT准确地预测了多环芳 (PAH) 如何与雌激素受体β (ERβ) 结合,确定了驱动这些相互作用的关键分子特征. 这有助于了解和减轻这些污染物的环境健康风险.
科学领域:
- 环境化学
- 计算毒理学
- 分子生物学
背景情况:
- 多环芳 (PAH) 是普遍存在的环境污染物,对生态系统和人类健康有重大影响.
- 通过与雌激素受体β (ERβ) 相互作用,PAHs可以破坏内分泌功能,这对准确的预测构成了挑战.
- 了解这些相互作用对于环境风险评估和管理至关重要.
研究的目的:
- 开发和验证一种用于预测PAHs和ERβ之间的结合亲和力的新计算模型.
- 确定影响PAH与ERβ相互作用的关键分子特征.
- 虚拟选一个大型的化学库以寻找潜在的ERβ结合剂.
主要方法:
- 开发一个多尺度双流图注意网络 (MS-DSGAT) 模型.
- 使用分子图表来预测PAH-ERβ结合亲和力.
- 采用分子对接和分析定位注意力重量 (PAW) 进行解释.
主要成果:
- MS-DSGAT实现了高预测精度 (R2=0.95),超过了传统的机器学习方法.
- 该模型确定了特定的功能组 (例如, -OH, -NH2, -SO3H) 作为结合亲和力的关键决定因素.
- 在6357种化学物质中,虚拟查发现了6.6%的高亲和力和66.4%的中等亲和力结合剂.
结论:
- MS-DSGAT提供了一种有效和可解释的方法来预测PAH-ERβ相互作用.
- 这些发现为设计具有减少内分泌干扰潜力的分子提供了洞察力.
- 该模型的框架可以扩展到调查其他配体-受体相互作用并识别环境毒素.
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