Application of artificial intelligence to quantitative structure-retention relationship calculations in chromatography
10.1016/j.jpha.2024.101155
- Author:
Jingru XIE
1
;
Si CHEN
;
Liang ZHAO
;
Xin DONG
Author Information
1. School of Medicine,Shanghai University,Shanghai,200444,China;Department of Pharmacy,Shanghai Baoshan Luodian Hospital,Baoshan District,Shanghai,201908,China;Luodian Clinical Drug Research Center,Institute for Translational Medicine Research,Shanghai University,Shanghai,200444,China
- Publication Type:Journal Article
- Keywords:
Quantitative structure-retention;
relationship;
Chromatography;
Accuracy;
Machine learning
- From:
Journal of Pharmaceutical Analysis
2025;15(1):4-18
- CountryChina
- Language:English
-
Abstract:
Quantitative structure-retention relationship(QSRR)is an important tool in chromatography.QSRR examines the correlation between molecular structures and their retention behaviors during chro-matographic separation.This approach involves developing models for predicting the retention time(RT)of analytes,thereby accelerating method development and facilitating compound identification.In addition,QSRR can be used to study compound retention mechanisms and support drug screening ef-forts.This review provides a comprehensive analysis of QSRR workflows and applications,with a special focus on the role of artificial intelligence—an area not thoroughly explored in previous reviews.More-over,we discuss current limitations in RT prediction and propose promising solutions.Overall,this re-view offers a fresh perspective on future QSRR research,encouraging the development of innovative strategies that enable the diverse applications of QSRR models in chromatographic analysis.
