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MLinvitroTox重新加载用于基于高通量危险的高分辨率质谱数据的优先级

Katarzyna Arturi¹, Eliza J Harris^2,3, Lilian Gasser²

¹Department of Environmental Chemistry, Swiss Federal Institute of Aquatic Science and Technology (Eawag), Überlandstrasse 133, 8600, Dübendorf, Switzerland. kasia.arturi@eawag.ch.

Journal of cheminformatics

|January 31, 2025

概括

MLinvitroTox v2是一个Python工具,使用机器学习从质谱数据中预测化学毒性,优先考虑危险的环境污染物以进行高效的风险评估.

科学领域:

环境化学和毒理学
计算毒理学和化学信息学
分析化学和质谱学.

背景情况:

使用高分辨率双重质谱 (HRMS/MS) 对环境样本的高通量选产生了大量的数据.
从复杂的环境矩阵中优先考虑毒理学相关信号是具有挑战性和耗时的.
现有的方法通常依赖于化学结构识别,这可能是一个瓶.

研究的目的:

引入MLinvitroTox v2,一个自动化的Python管道,用于从HRMS/MS数据中对毒理学相关信号的危险驱动优先级.
从光谱数据直接提高生物活性的预测,绕过了立即结构识别的需要.
通过将分析力度集中在优先级高的特征上,为化学风险评估提供一个具有成本效益的工具.

主要方法:

开发了一个机器学习 (ML) 框架,其中有490个XGBoost分类器训练使用化学指纹和ToxCast/Tox21邀请roDBv4.1数据.
基于MS2碎片化光谱,为每个HRMS特征生成490位生物活性指纹.
将MLinvitroTox v2应用于地下水的HRMS数据,以确定危险优先级.

主要成果:

874个HRMS特征的预测生物活性,约4%的特征/终点对被预测为活性.
根据invitroDB数据验证了预测,确认了120个活跃对和6791个非活跃对.
通过过,将潜在有毒特征的数量减少了一倍以上,从而使可行的分析确认成为可能.

结论:

MLinvitroTox v2有效地从HRMS/MS光谱数据中预测生物活性,使毒理学上相关的环境污染物能够有效地确定优先级.
增强的Python包提供了扩展的功能,包括基于结构的毒性预测和定制模型重新训练.
这种方法显著提高了复杂环境样本化学风险评估的成本效益和可行性.

关键词:

活动预测活动预测.二元分类二元分类二元分类.人力资源管理系统/管理系统在体外测定试验.西里乌斯 (Sirius) 是一个神话.在 Tox2121 中.在 ToxCast 里面,你会看到 ToxCast.有毒性有毒性在XGBoost上使用.

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