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分子网络,符合性预测和修订的指纹模型,用于在混合物中发现内分泌干扰物

Yvonne Kreutzer¹, Ida Rahu^1,2, Ulf Norinder^3,4

¹Department of Chemistry, Stockholm University, Svante Arrhenius Väg 16, 106 91, Stockholm, Sweden.

Analytical and bioanalytical chemistry

|January 21, 2026

概括

使用机器学习和新方法,如分子网络,优先考虑高风险的环境污染物. 基于指纹的模型显示了最低的假阳性率,改善了废水中的污染物识别.

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:

环境污染物的非目标查 (NTS) 需要优先考虑高风险特征,以管理工作量.
现有的机器学习模型 (MS2Tox) 依赖于精确的分子公式和来自SIRIUS+CSI:FingerID.等工具的指纹.

研究的目的:

介绍和评估分子网络 (MN) 和合规预测 (CP) 以识别具有潜在内分泌干扰活性的未知化合物.
用分子指纹对Tox21数据挑战的终点进行MS2Tox模型的修订.
评估废水中的环境污染物的优先级策略.

主要方法:

实施了分子网络 (MN) 和符合性预测 (CP) 以及使用分子指纹的修订后的MS2Tox模型.
基于虚假阳性率和召回的评估模型性能.
在废水样本中的转化产品和持久化学物质的LC / HRMS特征上应用优先级方法.

主要成果:

基于指纹的MS2Tox模型实现了最低的假阳性率 (0.35在90%的召回).
MN和CP产生更高的虚假阳性率 (分别为0.82和0.68).
在一项废水案例研究中,优先考虑了29个可能与AhR激动性相关的特征,候选结构显示AhR结合亲和力. 在1级确定了三个特征.

结论:

经过修订的具有分子指纹的MS2Tox模型在NTS中为特征优先设置提供了卓越的性能.
综合优先级策略,包括MN和CP,显示了识别环境污染物的潜力.
该研究成功地确定了废水中的潜在内分泌干扰化合物,突出了综合计算方法的实用性.

关键词:

危险的危险性危险的危险性高分辨率质谱测量高分辨率质谱测量机器学习机器学习有毒性有毒性没有针对性的查.

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