预测分类,标签和包装法规的H语句使用N-grams和分子指纹的符合性预测
Ulf Norinder1,2, Ziye Zheng3,4,5, Ian Cotgreave4
1Department of Computer and Systems Sciences, Stockholm University, P.O. Box 1073, SE-164 25 Kista, Sweden.
Current research in toxicology
|June 16, 2025
概括
机器学习模型使用N-grams预测化学危险标签,减少动物试验. 组合方法和符合性预测提高了分类,标签和包装 (CLP) 规则H声明的准确性.
科学领域:
- 计算毒理学计算毒理学
- 化学信息学 化学信息学
- 监管科学是一种监管科学.
背景情况:
- 有效的化学危险沟通对于环境和人类健康的保护至关重要.
- 机器学习 (ML) 为预测化学危险提供了一个有希望的途径,可能减少对动物试验的依赖.
- 准确的分类,标签和包装 (CLP) 规定H声明对于安全的化学品管理至关重要.
研究的目的:
- 评估N-grams和其他指纹特色化方法的有效性,以预测CLP调节的H陈述.
- 调查集合 (共识) 建模和符合性预测对危险分类的好处.
- 为化学危险评估和风险管理开发高效的ML工具.
主要方法:
- 在化学结构数据 (SMILES字符串) 上使用N-gram和其他指纹特征化技术.
- 采用集体建模,包括按类的共识和与中位数p值的合规预测.
- 使用诸如平衡精度,灵敏度,特异性以及符合性预测有效性和效率等指标评估模型性能.
主要成果:
- N-grams有效地捕获了与毒性相关的化学信息,包括金属和盐等元素.
- 整体建模,特别是按类或合规预测的共识,显著提高了预测的准确性和可靠性.
- 在预测CLP调节H语句方面取得了高性能,证明了开发的模型的实用性.
结论:
- N-gram特色化与合并和合规预测方法相结合,为预测化学危险标签提供了一种高效的方法.
- 这些ML模型可以作为化学危险评估,交叉阅读和风险管理的宝贵工具,支持监管合规.
- 该研究强调了通过准确的in silico危险预测来减少动物试验的潜力.
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