环境化合物的呼吸系统毒性查基于多式特征融合模型的多式特征
Zhiyu Xu1, Zehong Wu1, Hualin Tan1
1Hubei Key Laboratory of Environmental and Health Effects of Persistent Toxic Substances, School of Environment and Health, Jianghan University, Wuhan 430056, China.
Environmental science & technology
|March 5, 2026
概括
深度学习模型GFEnet通过整合分子,结构和电子特性,准确预测呼吸道毒性. 这种计算工具可以识别高风险的化学物质,改善早期检测有毒物质和环境安全.
科学领域:
- 计算毒理学计算毒理学
- 环境健康 环境健康
- 监管科学是一种监管科学.
背景情况:
- 呼吸系统容易受到化学毒素的影响,但目前的评估方法有限.
- 现有的监管框架依赖于昂贵,低通量动物试验,缺乏市场前评估.
- 需要有效的,系统的方法来评估呼吸道毒性.
研究的目的:
- 开发一个创新的多式联网深度学习 (DL) 框架,GFEnet,用于呼吸系统毒性预测.
- 整合不同的分子特征,以进行全面的跨度毒性评估.
- 建立一个高通量查平台,用于早期识别呼吸道毒素.
主要方法:
- 开发了GFEnet,这是一个多式联网DL框架,集成分子图形特征,结构指纹和电子层属性.
- 在体内哺乳动物呼吸系统毒性,体内A549细胞细胞毒性和ACE2基因调节方面接受过GFEnet的培训和评估.
- 通过体内小鼠模型验证了GFEnet的预测.
主要成果:
- GFEnet实现了高预测性能,其AUC值为0.986,0.965和0.919.9,其AUC值为0.986,0.965和0.919.
- 废弃性研究证实了每个特征模式的重要性.
- GFEnet确定烯-9-双和米切勒作为高风险候选物,由小鼠模型研究证实.
结论:
- GFEnet是一个强大的,高通量查平台,用于早期呼吸道毒性物质的识别.
- 该框架有效地将计算毒理学与环境健康和监管科学相结合.
- GFEnet推进了对呼吸系统健康的化学风险评估.
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