Machine learning for predicting retention times of chiral analytes chromatographically separated by CMPA technique

Abstract

Chiral mobile phase additive (CMPA) technique is an attractive method for chromatographic enantioseparation of chiral analytes. However, establishing chromatographic separation and analysis methods for given chiral analytes often requires extensive trial-and-error experiments, leading to time-consuming processes with high experimental costs. To address this challenge, machine learning (ML) was employed for the prediction of retention times of R and S-analytes to facilitate chromatographic enantioseparation. In this study, the enantiomeric retention times of chiral analytes enantioseparated by HPLC using cyclodextrin derivatives as CMPA were recorded, and the molecular descriptors of both the chiral analytes and the CMPA were calculated. Subsequently, several algorithms were employed for model development, with the coefficient of determination (R²) serving as the metric to assess the precision of these models. The findings indicate that the CatBoost model works well in predicting retention times and separability of chiral analytes. This study provides a rapid and efficient method to facilitate the development of CMPA technique.