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探索RPLC的化学子空间:一个数据驱动的方法

Denice van Herwerden¹, Alexandros Nikolopoulos¹, Leon P Barron²

¹Van 't Hoff Institute for Molecular Sciences (HIMS), University of Amsterdam, Amsterdam, 1098 XH, the Netherlands.

Analytica chimica acta

|July 19, 2024

概括

一个新的数据驱动框架预测分子是否可通过反相液态色谱 (RPLC) 进行测量. 该模型准确地对RPLC化学空间中的化学物质进行分类,识别了19.1%的小分子是RPLC无法测量的.

科学领域:

分析化学分析化学
环境科学环境科学
计算化学的计算化学

背景情况:

人类和环境暴露组包含许多未知的化学结构.
使用液态色谱 (LC) 和高分辨率质谱的非定向分析是分析复杂样品的常见方法.
化学空间被逆相液态染色学 (RPLC) 保留和解的部分在很大程度上是未知的.

研究的目的:

开发一个预测模型来评估化学化合物是否属于RPLC可接受的化学空间.
建立一个数据驱动的框架,根据其RPLC保留行为对分子进行分类.

主要方法:

使用分子指纹来预测RPLC保留的三个保留指数随机森林 (RF) 回归模型的构建.
基于回归模型的RPLC射频分类模型的开发.
将分类模型应用于小型分子的大数据集 (NORMAN SusDat).

主要成果:

分子指纹有效地预测RPLC保留行为.
该RPLC分类模型在预测某种化学物质是否属于RPLC子空间方面达到92%的准确性.
在NORMAN SusDat数据集中的91,737个小分子 (≤1,000 Da) 中,预计有19.1%的小分子"落在RPLC子空间之外".

结论:

开发的RPLC化学空间模型是绘制化学空间的重大进步.
该模型使用RPLC方法评估化学品的潜在可测量性.
在RPLC子空间之外识别化学物质有助于减少图书馆搜索候选人,并避免对不可测量的化合物进行选.

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