化学
保留时间
注释
色谱法
过程(计算)
质谱法
分子描述符
分辨率(逻辑)
特征选择
特征(语言学)
人工智能
数量结构-活动关系
计算机科学
哲学
操作系统
立体化学
语言学
作者
Julien Parinet,Yassine Makni,Thierno Diallo,Thierry Guérin
出处
期刊:Talanta
[Elsevier]
日期:2024-01-01
卷期号:267: 125214-125214
标识
DOI:10.1016/j.talanta.2023.125214
摘要
The development of quantitative structure-retention relationship (QSRR) models has, until recently, required an adequate selection of molecular descriptors necessarily obtained based on a known chemical structure. However, these complex descriptors are not always available nor calculable when the high-resolution mass spectrometry (HRMS) annotation process is underway. Depending on the level of annotation, many structures or even various molecular formulas could be candidates. To secure and improve the annotation process and to save time, a QSRR model (using only 0D molecular descriptors) to predict retention times in reverse-phase liquid chromatography (RPLC) based on the molecular formula was developed, and a general QSRR annotation-based methodology was also proposed.
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