在先进的氧化过程中对新出现的污染物的有毒转化产品进行选,使用3D深度学习和体外分析
Fulin Shao1, Weiying Li2, Zhiwei Liang1
1College of Environmental Science and Engineering, Tongji University, Shanghai, 200092, PR China.
Water research
|February 12, 2026
概括
一个新的深度学习框架,ToxD4C,准确地预测化学毒性,并识别水中的有害副产品. 这种先进的方法改善了环境风险评估和水质管理.
科学领域:
- 环境化学环境化学
- 计算毒理学计算毒理学
- 机器学习 机器学习
背景情况:
- 评估化学污染物的毒性对于环境安全至关重要.
- 传统的定量结构-活性关系 (QSAR) 模型在预测毒性方面存在局限性,原因是简化了分子描述符.
- 需要先进的方法来预测水生环境中的污染物及其转化产品的毒性.
研究的目的:
- 开发和验证一个新的多模式深度学习框架 (ToxD4C) 进行全面的毒性评估.
- 同时分类和回归31个不同的毒性终点.
- 为了使水处理和环境风险评估的快速和可解释的决策.
主要方法:
- 开发了ToxD4C,这是一个多模式的深度学习框架,集成了3D分子几何学,图表注意力网络和SE(3) 等同变压器.
- 在密度函数理论 (DFT) 优化的结构上使用转移学习对预训练的Uni-Mol模型进行了微调.
- 采用特征归属分析 (SHAP) 和受体对接来获得机械洞察力.
- 使用体外测定 (JC-1,CCK-8,记者测定) 的验证预测,并应用于UV/H2O2高级氧化产品.
主要成果:
- ToxD4C和微调的Uni-Mol模型在预测31个毒性终点方面表现优于传统的基于描述器的模型.
- 该框架成功地在真实水矩阵中识别了UV/H2O2高级氧化过程中的高风险转化产品.
- 在体外测试证实了已识别的转化产品的预测毒性.
- SHAP分析揭示了毒性的关键物理化学驱动因素,并提供了机械上下文.
结论:
- 与传统方法相比,开发的深度学习方法为毒性预测提供了更高的可靠性和概括性.
- ToxD4C和集成工具为高效和可解释的环境风险评估提供了一个强大的平台.
- 该框架支持水处理和化学污染物管理方面的知情决策.
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