人工智能作为化学风险评估的新前沿
1Center for Alternatives to Animal Testing (CAAT), Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States.
Frontiers in artificial intelligence
|November 2, 2023
概括
人工智能 (AI) 正在彻底改变毒理学,使预测性毒理学成为可能,并加强风险评估. 像RASAR这样的AI工具显示出高精度,将该领域转变为超越传统方法的领域.
科学领域:
- 计算毒理学计算毒理学
- 毒理风险评估 毒理风险评估 毒理风险评估
- 人工智能应用的人工智能应用.
背景情况:
- 毒理学正在从经验观察演变为数据密集型科学.
- 大量,多样化的数据集在现代毒理学中提出了整合挑战.
- 人工智能 (AI) 为数据处理和分析提供解决方案.
研究的目的:
- 突出人工智能在毒理学和风险评估中的变革性作用.
- 展示AI在预测毒理学方面的能力.
- 讨论人工智能的整合,以加强化学安全评估.
主要方法:
- 应用AI算法来分析大型毒理学数据集.
- 开发和验证自动化横读工具 (例如RASAR).
- 利用人工智能进行概率风险评估和处理数据复杂性.
主要成果:
- 人工智能方法有效地处理和整合了大量,异构的毒理学数据.
- 在众多测试和化学品中,RASAR工具实现了87%的平衡精度,超过了动物测试的可重复性.
- 人工智能通过处理大数据和概率输出的能力来促进概率风险评估.
结论:
- 人工智能代表了毒理学的变革性技术,而不仅仅是人类能力的增强.
- 由人工智能驱动的预测毒理学为风险评估提供了更有效,更准确的方法.
- 可解释性AI (xAI) 正在出现,以缓解模型不透明和数据偏差等挑战.
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