McGPCR:一种多模式学习模型,具有改进的适用性域特性,用于预测塑料化学品的G蛋白结合受体亲和力
Wenjia Liu1, Haobo Wang1, Zhiqiang Fu1
1Key Laboratory of Industrial Ecology and Environmental Engineering (Ministry of Education), Dalian Key Laboratory on Chemicals Risk Control and Pollution Prevention Technology, School of Environmental Science and Technology, Dalian University of Technology, Dalian 116024, China.
Environmental science & technology
|November 18, 2025
概括
研究人员开发了McGPCR,这是一种新型模型,可以预测与G蛋白结合受体 (GPCRs) 的化学结合. 该工具有助于识别塑料化学品带来的潜在健康风险,改善安全评估.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学计算化学
- 药理学 药理学是指药理学的学科.
背景情况:
- 塑料中的化学物质对人类健康构成潜在风险,但毒性数据有限.
- 结合G蛋白结合受体 (GPCR) 是化学毒性的关键事件.
- 预测各种化学物质和GPCR之间的结合亲和力需要先进的计算模型.
研究的目的:
- 开发一个高通量模型来预测化学-GPCR结合亲和力.
- 整合化学和受体特征,以便准确的结合预测.
- 使用开发的模型,识别潜在的有害塑料化学品.
主要方法:
- 构建了一个人类GPCR亲和数据集,包含96,776条记录 (59,599个化合物,109个GPCR).
- 开发了一个多式学习模型 (McGPCR),使用分子图和受体结合部位特征.
- 为预测可靠性实施的适用性域 (AD) 特性.
主要成果:
- 与仅使用化学结构的模型相比,McGPCR模型表现出更高的性能.
- 使用McGPCR和AD来预测9000多种塑料化学品的亲和力.
- 通过整合亲和力,持久性,生物积累和生产量,确定了具有高潜在环境风险的30种塑料化学品.
结论:
- 具有AD特征的McGPCR是预测化学-GPCR结合亲和力的有效工具.
- 该模型有助于识别有毒化学物质,特别是塑料中发现的有毒化学物质.
- 这种方法提高了与化学品相关的环境和人类健康风险的评估.
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