在环境空气中对有机化合物的毒性评估的分子表示和闭环验证PM2.5
Yao Lu1,2, Yangyang Wu1,2, Xiangdong Li1,2
1Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon,999077Hong Kong,China.
Environmental science & technology
|February 25, 2026
概括
评估细颗粒物 (PM2.5) 毒性是复杂的. 这项研究开发了一个可解释的机器学习模型来识别有毒的PM2.5化合物,揭示了芳香环和基群作为毒性的关键贡献者.
科学领域:
- 环境化学环境化学
- 毒理学 毒理学 毒理学
- 计算化学计算化学
背景情况:
- 细颗粒物 (PM2.5) 对健康的影响与其化学成分有关,而不仅仅是质量.
- 由于复杂性和实验的局限性,评估PM2.5化合物的毒性具有挑战性.
研究的目的:
- 开发一个可解释的机器学习 (ML) 框架,用于评估PM2.5.5.中的有机化合物的毒性.
- 确定负责PM2.5毒性的关键化学子结构.
主要方法:
- 使用精选的A549细胞毒性数据集 (19,841种化合物) 与分子访问系统 (MACCS) 指纹.
- 采用了一个通过生物目标策略优化的堆叠组合元模型.
- 应用了SHapley添加剂解释 (SHAP) 用于亚结构识别和实验验证.
主要成果:
- 堆叠组合模型实现了令人满意的性能 (测试AUC>0.8) 具有良好的概括性.
- 非目标分析确定并验证了来自香港和南京PM2.5样本的有毒化合物.
- 确定了芳香环和含的功能组作为PM2.5化合物毒性的关键驱动因素.
结论:
- 开发的ML框架提供了一个有效的方法来优先考虑有毒的PM2.5成分.
- 衍生的"结构毒性"规则可以指导未来的研究和改进预测模型.
- 这些发现有助于更深入地了解PM2.5化学毒性机制.
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