DART预测器:用于对具有发育和生殖毒性 (DART) 的化学品进行高通量查的多标签注意力模型
Yuchen Gao1, Yizhou Huang1, Xuanlin Chen1
1State Key Laboratory of Soil Pollution Control and Safety, and Women's Hospital, School of Medicine, Zhejiang University, Hangzhou 310058, China.
Environmental science & technology
|November 13, 2025
概括
一个新的深度学习模型,DART预测器,准确识别发育和生殖毒性 (DART) 化学物质. 整合暴露数据可以改善预测,帮助更安全的化学风险评估.
科学领域:
- 环境健康 环境健康
- 毒理学 毒理学 毒理学
- 计算化学的计算化学
背景情况:
- 具有发育和生殖毒性 (DART) 的化学物质对人类健康构成重大风险,特别是在胚胎和胎儿发育期间.
- 目前对DART化学品的预测模型往往缺乏暴露信息,这限制了在现实场景中准确的风险评估.
研究的目的:
- 开发和验证用于识别DART化学品的先进预测模型.
- 将暴露参数整合到深度学习框架中,以加强风险评估.
- 为DART化学物质识别提供高通量选服务.
主要方法:
- 开发了DART预测器,这是一个使用标签意识注意力机制的多标签深度学习模型.
- 在25175个记录中训练了模型,其中包括分子描述符,生物测试活动和暴露参数.
- 在外部验证数据集上评估模型性能,评估准确性,可解释性和可概括性.
主要成果:
- 达特预测器实现了最先进的性能,平均AUC为0.964和平均回忆为0.923.
- 该模型在外部数据集上显示出强大的解释性和通用性 (AUC: 0.889,回忆: 0.959).
- 结合暴露参数,模型性能提高了高达8.6%的AUC,突显了暴露数据的重要性.
结论:
- 达特预测器为达特风险评估提供了一个新的,基于暴露的框架.
- 该模型推进了评估DART相关风险的新方法方法.
- 一个基于云的平台为DART化学品识别提供了高通量选服务.
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