不确定性意识机器学习预测全球化学品市场的非癌症人类毒性
Kerstin von Borries1, Katie V Beckwith2, Jonathan M Goodman2
1Quantitative Sustainability Assessment, Department of Environmental and Resource Engineering, Technical University of Denmark, Bygningstorvet 115, 2800, Kgs. Lyngby, Denmark. kejbo@dtu.dk.
机器学习模型现在可以通过不确定性估计来预测化学毒性,从而提高了对超过10万种化学品对人类健康风险的评估信心.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学计算化学
- 数据科学数据科学数据科学
背景情况:
- 对许多化学品的有限毒性数据阻碍了对人类健康的风险评估.
- 机器学习 (ML) 模型具有潜力,但缺乏特征性性能和信心.
- 在各种化学空间的ML预测中的不确定性是一个重大挑战.
研究的目的:
- 开发不确定性意识的ML模型来预测人类毒性效应剂量.
- 量化对生殖/发育和一般非癌症毒性的预测不确定性.
- 增强对基于ML的化学毒性评估的信心.
主要方法:
- 开发并校准了不确定性意识的ML模型.
- 预测的毒性效应剂量和95%的置信区间对于100,000以上的化学物质.
- 对预测错误和化学熟悉性进行评估模型校准.
主要成果:
- 精确校准的模型提供可靠的不确定性估计.
- 在全球化学品市场中确定了毒性和不确定性热点.
- 通过量化置信区间实现了高性能预测.
结论:
- 不确定性量化建立了对ML毒性预测的信心.
- 结果支持化学风险管理的知情决策.
- 指南针对数据生成和ML模型改进工作.
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