基于不良结果路径的机器学习用于预测新出现的有机污染物的血管毒性
Aubin Siewetcheu Toukak1, Wenjie Gao1, Shouqiang Wan1
1School of Environmental Science and Engineering, Tianjin Key Lab of Biomass/Wastes Utilization, Tianjin University, Tianjin 300350, China. liningec@tju.edu.cn.
Environmental science. Processes & impacts
|March 6, 2026
概括
废水中的新兴有机污染物 (EOP) 构成血管风险. 一个由不良结果路径 (AOP) 告知的机器学习模型准确地预测了EOP的血管毒性,有助于风险评估.
科学领域:
- 环境化学环境化学
- 毒理学 毒理学 毒理学
- 计算生物学 计算生物学
背景情况:
- 新兴有机污染物 (EOP) 在废水中普遍存在.
- 它们的血管毒性风险尚未完全理解或监管.
- 目前的风险评估缺乏对EOPs的全面评估.
研究的目的:
- 开发一种基于不良结果途径 (AOP) 的机器学习 (ML) 方法来评估EOPs的血管毒性.
- 用高通量生物试验数据和ML模型预测312个EOP的血管毒性.
- 优先考虑潜在的血管毒性EPs进行进一步调查.
主要方法:
- 集成的ToxCast高通量生物测试数据与摩根指纹.
- 训练了14个基于AOP 509关键事件的多层感知子 (MLP) 模型.
- 包括Nrf2抑制,氧化应激,线粒体功能障碍,亡,内皮功能障碍和血管生成障碍.
- 已验证的模型用于测试和未测试化学品的活性分类.
主要成果:
- 在血管毒性方面取得了高预测准确度 (70-95%).
- 成功归类了312个EOP的活动.
- 优先考虑特定化学物质,如凯托可纳,塞特拉林和米可纳,因为它们的血管毒性潜力.
- 确定了支持优先级EOPs血管毒性的文献.
结论:
- 以AOP为指导的ML框架有效地预测了EOP的血管毒性.
- 这种方法支持化学风险评估和优先考虑有限数据的污染物.
- 增强环境决策对EOP及其潜在的健康影响.
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