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从高分辨率的协同质谱到基于人工智能的污染物毒性预测:对7种内分泌干扰物的案例研究终点

Xin Zhang^1,2, Xiaoxiao Han¹, Tongtong Xiang¹

¹State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China.

Environmental science & technology

|March 3, 2025

概括

这项研究引入了MSFragTox,一种使用质谱碎片化数据预测化学毒性的新方法,改进了环境风险评估. 它可以从MS/MS数据直接预测毒性,用于早期环境警告.

科学领域:

环境化学环境化学
毒理学毒理学毒理学
计算化学是一种计算化学.

背景情况:

高分辨率质谱 (HRMS) 和非目标分析 (NTA) 识别环境危害,但往往未充分利用数据,并未知毒性.
目前的方法很难将已识别的化学结构与特定的毒理效应和健康结果联系起来.

研究的目的:

引入MSFragTox,一种使用MS/MS碎片化光谱进行直接毒性预测的新方法.
将MS/MS数据与高通量查 (HTS) 测定用于内分泌干扰终点进行整合.
开发基于MS衍生指纹的机器学习模型,用于基于MS衍生指纹的毒性预测.

主要方法:

利用高分辨率的双重质谱 (MS/MS) 碎片化光谱.
使用子结构碎片化概率向量作为MS衍生的指纹.
将数据与七个内分泌干扰终点的Tox21高通量查试验集成.
使用机器学习算法进行毒性预测.

主要成果:

最好的MSFragTox模型在测试组中获得了0.845的平均AUROC.
MSFragTox的性能优于传统的基于分子指纹和描述符的模型.
开发了一个网络客户端,使用MS/MS数据进行用户友好的毒性查.
分析揭示了毒性终点的共同点和差异背后的机制.

结论:

MSFragTox能够从MS/MS数据直接预测毒性,克服了传统NTA的局限性.
该方法在真实环境样本中成功验证了潜在的内分泌干扰作用.
在环境监测中,MSFragTox提供了一种可行的风险预测和早期预警方法.

关键词:

高分辨率的双重质谱质谱仪.机器学习是机器学习.毒性预测毒性预测

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