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使用机器学习从物理化学性质测量中提取结构信息――在非定向分析中用于结构阐明的新方法

Dimitri Abrahamsson^1,2, Christopher L Brueck^3,4, Carsten Prasse^3,5

¹Department of Pediatrics, New York University Grossman School of Medicine, New York, New York 10016, United States.

Environmental science & technology

|September 25, 2023

概括

这项研究引入了一种新的方法,用于识别环境化学物质,使用分区实验和机器学习. 这种方法有助于在没有分析标准的情况下用于非目标分析的结构阐明.

科学领域:

环境化学环境化学
环境健康环境健康
分析化学分析化学
计算化学的计算化学

背景情况:

非定向分析 (NTA) 对于环境化学和健康评估至关重要.
在NTA的一个主要挑战是环境化学品分析标准的稀缺性.
准确的化学物质识别对于了解环境污染和健康风险至关重要.

研究的目的:

开发一种新的方法,用平衡分区比率来预测分子结构.
将溶剂-水分区系数 (K_SW) 的测量与用于化学识别的机器学习相结合.
探索使用K_SW作为用于数据库搜索的化学指纹.

主要方法:

分区实验是使用10种有机溶剂和水中185种化学物质的混合物进行的.
均衡分区比 (K_SW) 用液态染色学与质谱学 (LC-QTOF MS和LC-Orbitrap MS) 结合测量.
一个机器学习算法将K_SW数据转换为分子功能组 (RDKit片段) 用于数据库搜索.

主要成果:

LC-QTOF MS和LC-Orbitrap MS产生了不同的log K_SW值,平均绝对误差分别为0.22和0.33.
对于RDKit碎片的预测准确性也因方法而异,平均绝对误差为0.23 (QTOF) 和0.31 (Orbitrap).
开发的方法证明了在预测化学结构阐释的功能群的可行性.

结论:

这项研究提出了一个有前途的新策略,用于结构阐明在非目标分析.
分区实验和机器学习的整合为化合物识别提供了一个可行的替代方案.
该方法显示出在环境分析中克服不可用分析标准的瓶的潜力.

关键词:

机器学习是机器学习.非有针对性的分析.物理化学特性物理化学特性结构阐明结构阐明

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