基于机器学习的QSAR模型用于预测纳米颗粒混合物毒性
Fan Zhang1, Zhuang Wang2, Willie J G M Peijnenburg3
1Institute of Environmental Sciences (CML), Leiden University, Leiden 2300 RA, the Netherlands.
Environment international
|June 17, 2023
概括
机器学习模型准确地预测了工程纳米粒子 (ENP) 的混合毒性. 这些定量结构-活动关系 (QSAR) 模型的性能优于传统方法,有助于对生态风险进行评估.
科学领域:
- 环境化学和毒理学
- 计算毒理学和化学信息学
- 纳米技术和风险评估
背景情况:
- 预测工程纳米粒子 (ENPs) 的混合物毒性是复杂的.
- 形方法,特别是机器学习 (ML),为毒性预测提供了一个有希望的策略.
- 现有的基于组件的模型在预测复杂混合效应方面存在局限性.
研究的目的:
- 开发和比较基于ML的定量结构-活性关系 (QSAR) 模型,用于预测金属ENP的联合毒性.
- 评估ML模型的性能与传统的基于组件的混合模型相比.
- 为ENP混合物的生态风险评估提供一个强大的方法框架.
主要方法:
- 对7种金属ENP针对大肠杆菌的内部和文献毒性数据的结合.
- 应用机器学习技术:支持矢量机器 (SVM) 和神经网络 (NN).
- 开发并验证了QSAR模型,并将其与独立行动和度添加模型进行比较.
主要成果:
- 开发了几种高性能SVM-QSAR和NN-QSAR模型.
- 基于NN的QSAR模型使用特定的分子描述器实现了出色的预测能力 (R2test = 0.9080.911).
- 开发的QSAR模型与基于组件的混合模型相比,表现出更高的性能,并且在它们的适用性领域内.
结论:
- 机器学习,特别是QSAR建模,对于预测ENP的混合物毒性是有效的.
- 开发的模型为评估ENP混合风险的传统方法提供了可靠的替代方案.
- 这种方法为ENP混合物的生态风险评估提供了理论和方法的基础.
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