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通过配对学习改进化学品的生态毒理危害评估

Leo Posthuma^1,2, Tadeusz Price^3,4, Markus Viljanen³

¹Centre for Sustainability, Environment and Health (DMG), National Institute for Public Health and the Environment, P.O. Box 1, Bilthoven 3720 BA, The Netherlands.

Environmental science & technology

|July 31, 2025

概括

机器学习可以预测各种物种的化学污染物毒性,填补关键的生态毒理学数据缺口. 这使得更好的环境风险评估和生物多样性保护战略成为可能.

科学领域:

环境科学环境科学
毒理学毒理学毒理学
计算生物学计算生物学

背景情况:

生态毒理危害评估依赖于广泛的实验数据,往往导致许多化学物种相互作用的数据缺口.
现有的数据显示了物种和化学品选择的偏差,限制了全面的风险评估.
预测建模提供了一种潜在的解决方案,可以弥合这些数据缺口,并提高评估准确性.

研究的目的:

开发和验证一种机器学习方法,用于对各种物种的化学污染物预测生态毒理学毒性数据 (LC50).
创建包括物种敏感性分布 (SSD) 和化学危险分布 (CHD) 在内的综合数据集,以提高危险评估.
为了说明环境风险评估中预测数据的实际应用,例如安全和可持续设计 (SSbD) 和生命周期评估 (LCA).

主要方法:

应用双向学习对观察到的LC50值的大数据集,只有0.5%的实验数据覆盖率.
预测了超过400万个LC50值,用于3295种化学品和1267种物种的各种暴露时间.
开发了新的工具:危险热图,化学品和分类组的物种敏感性分布 (SSD) 和化学危险分布 (CHD).

主要成果:

成功预测了大量的LC50值,显著扩大了可用的生态毒理学数据.
生成了全面的SSD和CHD,为化学危险和物种敏感性提供了新的见解.
通过验证证明预测数据的实用性,突出输入数据偏差和生成资源的实际适用性.

结论:

机器学习有效地弥合了生态毒理危害评估中的数据缺口,提供了有价值的预测LC50值.
产生的危险热图,SSD和CHD提供了强大的工具,用于风险评估,标准制定和环境影响分析.
该方法广泛适用于SSbD,LCA和减轻化学污染对生物多样性的影响等领域.

关键词:

生物多样性生物多样性化学化学化学化学化学化学化学化学危险的分布化学危险的分布生态毒性生态毒性环境质量标准环境质量标准危险的危险性危险的危险性危险热图危险热图机器学习是机器学习.双向学习是双向学习.通过设计,安全和可持续.种类敏感度分布的分布.有毒的压力有毒的压力.

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