塑料添加剂的AI驱动的危险优先级使用Tox21生物测试和自我监督的图形变压器
Donghyeon Kim1, Eungyeong Lee2, Youngmin Yi3
1School of Environmental Engineering.
Environmental toxicology and chemistry
|September 19, 2025
概括
这项研究使用AI模型和Tox21生物测试对400多种塑料添加剂进行了毒性选. 研究人员确定了78种高度活性化学物质,揭示了数据缺口并支持更安全的化学品评估.
科学领域:
- 环境化学环境化学
- 毒理学 毒理学 毒理学
- 人工智能的人工智能
背景情况:
- 塑料添加剂可能会入环境,带来潜在的毒性风险.
- 超过400种大量塑料添加剂被ECHA塑料添加剂倡议列出.
- 评估这些添加剂的毒性对于环境和人类健康至关重要.
研究的目的:
- 为了选众多塑料添加剂的潜在毒性.
- 应用深度学习模型和Tox21生物测试数据进行化学危害评估.
- 识别高活性塑料添加剂并调查它们现有的危害数据.
主要方法:
- 收集并使用了具有广泛生物活性概况的Tox21数据集.
- 在Tox21生物试验数据上训练和微调GROVER深度学习算法.
- 使用F1得分评估模型性能,并与基线算法进行比较.
- 对已识别的活性化学物质进行调查的危险信息和GHS分类.
主要成果:
- 与传统的机器学习算法相比,GROVER模型表现出卓越的性能.
- 在171种选的塑料添加剂中确定了78种高度活性化学物质.
- 揭示了潜在有毒塑料添加剂的危险信息中的重大数据缺口.
- 强调了人工智能模型作为危险评估新方法方法 (NAM) 的实用性.
结论:
- 像GROVER这样的先进的人工智能模型可以有效地选塑料添加剂的毒性.
- 该研究确定了需要进一步毒理研究的关键塑料添加剂.
- 这种方法支持监管决策,并与动物试验的3R原则保持一致.
- 通过人工智能实现化学危险评估的现代化是可行的和有益的.
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