Retention time prediction for chromatographic enantioseparation by quantile geometry-enhanced graph neural network.

ABSTRACT: The enantioseparation of chiral molecules is a crucial and challenging task in the field of experimental chemistry, often requiring extensive trial and error with different experimental settings. To overcome this challenge, here we show a research framework that employs machine learning techniques to predict retention times of enantiomers and facilitate chromatographic enantioseparation. A documentary dataset of chiral molecular retention times in high-performance liquid chromatography (CMRT dataset) is established to handle the challenge of data acquisition. A quantile geometry-enhanced graph neural network is proposed to learn the molecular structure-retention time relationship, which shows a satisfactory predictive ability for enantiomers. The domain knowledge of chromatography is incor

SUBMITTER: Xu H 

PROVIDER: S-EPMC10227049 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature