机器学习用于基于保留行为预测环境流动性
Tobias Hulleman1,2, Saer Samanipour1,3,4, Paul R Haddad5
1Queensland Alliance for Environmental Health Sciences (QAEHS), 20 Cornwall Street, Woolloongabba, Brisbane, QLD 4102, Australia.
Environmental science & technology
|October 22, 2025
概括
识别非常持久和非常移动 (vPvM) 的物质对于环境保护至关重要. 这项研究开发了一种化学信息模型,使用染色学数据来预测化学流动性,从而能够早期识别vPvM化合物.
科学领域:
- 环境化学环境化学
- 化学信息学 化学信息学
- 毒理学 毒理学 毒理学
背景情况:
- 非常持久和非常移动 (vPvM) 的物质对生态系统和人类健康构成风险.
- 评估化学流动性至关重要,但对于大多数化学物质,有机碳水分区系数 (Koc) 等实验数据很少.
- 数以千计的新化学品需要高效的优先级工具.
研究的目的:
- 开发和验证化学环境流动性的预测模型,使用随时可用的染色学数据.
- 建立一个可扩展的化学信息学方法来识别vPvM物质.
主要方法:
- 利用了146,902种化学品的逆相液态染色学 (RPLC) 数据来分配移动性标签.
- 计算了每种化学物质的881个PubChem指纹,以表示结构特征.
- 训练了一种随机森林分类器,根据RPLC保留行为和化学指纹来预测移动性.
主要成果:
- 随机森林模型获得了高的F1分:0.87 (非常移动),0.81 (移动) 和0.96 (非移动) 在测试组.
- 该模型应用于REACH注册的64,492种化学品,该模型将20%的化学品归类为非常可移动的化学品,26%的化学品归类为可移动的化学品,而53%的化学品归类为不可移动的化学品.
- 证明了模型的可扩展性,用于早期识别潜在有害的vPvM物质.
结论:
- 一个强大的化学信息模型有效地使用RPLC数据和结构指纹预测化学环境流动性.
- 这种方法为优先考虑化学品提供了一个可扩展的解决方案,有助于早期识别vPvM物质.
- 这些发现支持积极的环境风险评估和化学物质管理.
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