大数据和人工智能:毒理学数字化转型的潜力和挑战
1School of Environmental Engineering, University of Seoul, Seoul, Republic of Korea.
Environmental analysis, health and toxicology
|November 20, 2025
概括
人工智能 (AI) 正通过启用预测模型来彻底改变毒理学和化学风险评估. 解决数据质量和模型透明度等挑战是监管接受和在安全科学中推进AI的关键.
科学领域:
- 计算毒理学和风险评估
- 科学研究中的人工智能
- 监管科学和化学安全
背景情况:
- 毒理学正在从观察转向预测科学,这是由计算进步和大数据推动的.
- 增加的计算能力和毒理学数据使化学风险评估使用人工智能 (AI) 的现代化成为可能.
- 人工智能为化学品优先级和安全评估提供了新的机会.
研究的目的:
- 探索当前的毒性数据库和人工智能方法 (机器学习,深度学习,大语言模型) 的现状.
- 检查案例研究,证明AI在毒性预测和化学物质优先级的应用.
- 确定挑战,并提出将人工智能整合到监管科学中的解决方案.
主要方法:
- 审查当前的毒性数据库和人工智能技术.
- 对使用人工智能用于毒性预测的代表性案例研究的分析.
- 讨论挑战,包括数据限制和模型可解释性.
主要成果:
- 人工智能,包括机器学习和深度学习,在毒性预测和化学品优先级方面显示出前景.
- 仍然存在重大挑战,特别是高质量的标准化数据集的稀缺性.
- 人工智能模型的"黑盒子"性质阻碍了监管机构的接受和信任.
结论:
- 可解释人工智能 (XAI) 和不利结果途径 (AOP) 框架对于提高模型解释性和监管接受度至关重要.
- 未来的研究应该专注于开发透明,可重复的AI模型,以进行可靠的化学安全评估.
- 推进人工智能作为新方法方法 (NAM) 对于下一代风险评估 (NGRA) 是必不可少的.
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