使用结构警报和机器学习建模,预测化学物质的呼吸刺激和呼吸敏感性
Yaroslav Chushak1,2, Andrew Keebaugh1,3, Rebecca A Clewell1,4
1711 Human Performance Wing, Air Force Research Laboratory, Wright-Patterson AFB, OH 45433, USA.
Toxics
|April 25, 2025
概括
研究人员开发了机器学习模型,以预测化学呼吸道危害. 这些模型准确地识别了潜在的呼吸道刺激物和敏感剂,有助于化学安全评估.
科学领域:
- 毒理学 毒理学 毒理学
- 计算化学计算化学
- 数据科学数据科学数据科学
背景情况:
- 吸入的有毒物质对呼吸系统健康构成重大风险,包括刺激,炎症,喘和肺损伤.
- 由于大量使用的化学品,因此无法对所有物质进行全面的实验性毒性测试.
- 预测模型对于有效评估化学品的呼吸道危害至关重要.
研究的目的:
- 开发和验证机器学习 (ML) 模型,用于预测化学呼吸道刺激和敏感化危害.
- 识别与呼吸系统毒性相关的结构性警报.
- 选职业和军事相关的化学品,以检测潜在的呼吸道危害.
主要方法:
- 从实验数据中生成呼吸道刺激剂和敏感剂的结构性警报.
- 开发了使用各种分子描述符和技术的多个ML分类模型.
- 通过结合表现最佳的个体模型,创建了呼吸道刺激和敏感化的共识模型.
- 使用训练和测试数据集验证模型性能.
主要成果:
- 确定了13个结构性警报,涉及呼吸道刺激物,18个涉及呼吸道敏感剂.
- 获得了高预测准确度:在试验组上88%的呼吸道刺激和86%的呼吸道敏感性.
- 选了687种职业化学品,确定了62种刺激剂和121种敏感剂.
- 选了525种与军事有关的化学物质,预测47种是刺激物,36种是敏感剂.
结论:
- 开发的ML模型和结构性警报为预测化学呼吸道毒性提供了可靠的工具.
- 这些预测模型可以大大帮助在职业和军事环境中对化学品的风险评估.
- 该研究强调了计算方法在提高化学安全评估方面的潜力.
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