基于AI/ML的计算模型用于毒性预测
Sushmita Barua1, Badhrinarayanan Balaji2, Seetharaman Balaji3
1Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
计算毒理学和AI/ML模型正在推进化学安全评估. 这些工具可以预测毒性,帮助监管工作,减少动物试验,以更好地评估化学安全.
科学领域:
- 计算毒理学计算毒理学
- 人工智能 (AI) 是一种人工智能.
- 机器学习 (ML) 是指机器学习.
背景情况:
- 越来越多的对准确毒性评估和减少动物试验的需求推动了计算模型的开发.
- 人工智能/ML模型和在线资源对于现代计算毒理学研究至关重要.
研究的目的:
- 审查用于毒性预测和化学安全评估的计算模型和数据覆盖范围.
- 强调AI/ML工具用于预测各种毒性终点,并讨论监管相关性.
主要方法:
- 专注于计算模型,分子描述器,定量结构-活动关系 (QSAR) 模型.
- 包括基于AI/ML的方法,可解释AI (XAI) 和预测方法.
- 对数据覆盖范围,可访问性和监管考虑的分析.
主要成果:
- 计算模型和AI/ML工具能够识别,预测和分析跨生物终点的化学毒性.
- 人工智能/ML工具对于预测神经毒性,肝毒性,心脏毒性,基因毒性和环境毒性的有效.
- 观察到大量的监管限制和化学品安全评估中缺乏全球合规性.
结论:
- 由于人工智能的快速发展,监管适应性至关重要.
- 整合AI/ML工具和可互操作框架可以显著推进预测毒理学.
- 对监管规范的全球合规性是未来化学品安全评估的关键重点.
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