因果推理机器学习用于揭示在净化水中的抗生素与基激素反应性的抗生素
Shihua Zou1, Zonglin Li1, Yicen Dai1
1Shanghai Key Lab of Chemical Assessment and Sustainability, Key Laboratory of Yangtze River Water Environment, School of Chemical Science and Engineering, Tongji University, 1239 Siping Road, Shanghai 200092, China.
Environmental science & technology
|March 16, 2026
概括
这项研究开发了一个可解释的机器学习框架,以预测基 (HO·) 与抗生素的反应性. 它确定了设计更好的净水策略的关键分子因素.
科学领域:
- 环境化学环境化学
- 计算化学计算化学
- 水处理 处理水的方法
背景情况:
- 基 (HO·) 与有机污染物的反应性对于通过先进的氧化过程进行水净化至关重要.
- 预测HO·反应性的现有机器学习 (ML) 模型往往缺乏可解释性.
- 了解影响HO·反应性的分子因素是优化水处理的关键.
研究的目的:
- 开发一个可解释的ML框架,以确定控制HO·抗生素污染物的反应性的分子因素.
- 为合理的水净化设计建立因果发现框架.
主要方法:
- 使用DFT衍生描述符 (宪法,量子化学,亚伯拉罕) 来描述抗生素.
- 利用了以注意力驱动的特征交互方法来实现最佳的特征子集生成.
- 应用夏普利添加式解释 (SHAP) 和因果界面ML用于解释性和因果推断.
主要成果:
- 确定了关键的分子特性,如体积调节的电子迁移能力 (V_VIP) 和素介导的电子吸引力 (#X_ME).
- 开发了一个优化的随机森林模型,具有很高的预测准确性 (实验相对误差<6%).
- 建立了一个因果发现框架,甚至适用于小数据集.
结论:
- 可解释的ML框架成功地揭示了HO·反应性的内在分子因素.
- 开发的因果发现方法使得水净化策略的设计更合理.
- 这项工作为推进水处理中的AOP提供了坚实的基础.
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