可解释的无代码符合经合组织的机器学习模型,用于预测多环芳和它们的基离子代谢产物多环芳的致变性活性
Andrés Halabi Diaz1, Mario Duque-Noreña2, Elizabeth Rincón3
1Departamento de Ciencias Químicas, Facultad de Ciencias Exactas, Universidad Andrés Bello, Avenida Republica 275, Santiago 8370146, Chile; Departamento de Investigación y Desarrollo, Good Global Research and Science (GGRS), Avenida Ramón Picarte 780, Valdivia 5090000, Chile; Departamento de I+D+i, CatchPredict SpA, Avenida Ramón Picarte 780, Valdivia 5090000, Chile.
The Science of the total environment
|March 18, 2025
概括
这项研究引入了一种使用计算化学和机器学习的新方法,用于预测多环芳 (PAHs) 的突变性. 该方法准确地识别有害的PAH,有助于环境风险评估.
科学领域:
- 环境毒理学环境毒理学
- 计算化学计算化学
- 分子建模分子建模
背景情况:
- 多环芳 (PAH) 是一种持久性环境污染物,已知具有基因毒性和致变性作用.
- 它们在土壤和水中的积累增加了对生态系统和人类健康的暴露风险.
- 对PAHs的代谢激活产生了形成DNA添加物的反应性物种,从而提高了它们的变异性.
研究的目的:
- 开发一种符合经合组织标准的方法来预测PAH的变异性.
- 整合概念密度函数理论 (CDFT) 与机器学习进行风险评估.
- 为了确定关键的电子特性和与PAH变异性相关的代谢激活途径.
主要方法:
- 使用了CDFT计算 (GFN2-xTB) 和机器学习模型 (SPAARC,随机树,JCHAID).
- 采用了前致癌物和激素阴离子代谢物质的量子化学描述剂.
- 对Ames测试数据进行验证的模型用于突变性预测.
主要成果:
- 确定了激素阴离子描述剂作为代谢激活的关键指标.
- 通过机器学习模型实现了超过89%的验证准确性,最大限度地减少了虚假负面.
- 证明了PSL和CDP电友性框架在建模DNA损伤中的有效性.
结论:
- 开发的方法提供了一个可扩展的,具有成本效益的,无代码的工具,用于评估PAH转基因风险.
- 代谢激活和基离子中间体是PAH突变性中的关键因素.
- 电子特性有效地预测突变性,支持QSAR建模和监管风险评估.
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