给定化学物质对给定水生物种的生态毒性是多少? 使用推系统技术预测物种和化学物质之间的相互作用
M Viljanen1, J Minnema2, P N H Wassenaar2
1Department of Statistics, Data Science and Modelling, National Institute of Public Health and the Environment, Bilthoven, The Netherlands.
SAR and QSAR in environmental research
|September 6, 2023
概括
机器学习 (ML) 模型可以准确地预测水生物种的化学毒性 (LC50值),即使数据稀少. 一种新的因子化机器方法显示了与复杂的ML方法可比的性能.
科学领域:
- 环境毒理学环境毒理学
- 计算化学是一种计算化学.
- 生态毒理学 生态毒理学
背景情况:
- 化学安全评估需要多种物种的毒性数据,但希望尽量减少动物试验.
- 机器学习 (ML) 通过将化学品和物种的毒性模式概括为解决方案.
- 大量的公共毒性数据集往往稀疏,阻碍了有效的ML模型开发.
研究的目的:
- 调查ML在使用大而稀疏的数据集预测化学毒性的能力.
- 开发和评估ML模型来预测LC50值 (致死度导致50%的死亡率).
主要方法:
- 利用来自ECOTOX数据库的2431种有机化学品和1506种水生物种的实验LC50数据.
- 评估了已建立的ML技术,并引入了一种由推系统启发的新型因子化机器模型.
- 通过预测未见的化学物种对和未见的化学物质来评估模型性能.
主要成果:
- 机器学习模型在两种验证设置中都能准确预测LC50值.
- 新型因子化机器方法实现了与复杂,调整良好的ML模型相提并论的性能.
- 这项研究证实了ML在预测生态毒理学终点方面的实用性,尽管数据稀疏.
结论:
- ML,特别是因子化机器方法,为预测化学生态毒性提供了一种可行的方法.
- 这种方法可以帮助减少动物试验,同时保持强有力的安全评估.
- 即使有不完整的化学物种相互作用数据,也可以实现准确的毒性预测.
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