开发基于分子结构的机器学习方法来识别候选持久,移动和有毒 (PMT) 和非常持久和非常移动 (vPvM) 的物质
Min Han1, Biao Jin1, Jun Liang2
1State Key Laboratory of Organic Geochemistry, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou, 510640, China; CAS Center for Excellence in Deep Earth Science, Guangzhou, 510640, China; University of Chinese Academy of Sciences, Beijing, 10069, China.
Water research
|August 18, 2023
概括
机器学习模型准确地识别了持久性,移动性和有毒性 (PMT) 或非常持久,非常移动性 (vPvM) 的物质. 这种方法有助于通过有效选化学品来防止饮用水污染.
科学领域:
- 环境化学环境化学
- 计算毒理学计算毒理学
- 预测建模预测建模
背景情况:
- 持久性,移动性和有毒性 (PMT) 和非常持久,非常移动性 (vPvM) 的物质对饮用水质量构成风险.
- 准确识别这些物质对于环境保护和公共卫生至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型来选潜在的PMT/vPvM物质.
- 为开发的ML模型建立适用性领域.
- 将一个一步预测模型与三步预测方法进行比较.
主要方法:
- 在模型培训中使用了311种具有专家推导的PMT/vPvM危险分类的物质.
- 采用了各种分子描述器 (MD),数据平衡策略和ML算法.
- 综合的沙普利添加式解释 (SHAP) 用于模型的解释性.
主要成果:
- 在使用一步式模型的内部测试集上,实现了PMT/vPvM识别的92%准确性.
- 在对台湖污染物的外部测试组上以90%的准确度验证了模型.
- 证明了一步预测模型在三步方法上的优越性.
结论:
- 开发的ML模型为识别候选PMT/vPvM物质提供了一个高效的in silico工具.
- 这种大数据方法有助于对化学品进行潜在的环境和健康风险的选.
- 这些发现支持采取主动措施,防止饮用水污染.
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