  A new research framework is proposed to incorporate machine learning
techniques into the field of experimental chemistry to facilitate
chromatographic enantioseparation. A documentary dataset of chiral molecular
retention times (CMRT dataset) in high-performance liquid chromatography is
established to handle the challenge of data acquisition. Based on the CMRT
dataset, 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
incorporated into the machine learning model to achieve multi-column
prediction, which paves the way for chromatographic enantioseparation
prediction by calculating the separation probability. Experiments confirm that
the proposed research framework works well in retention time prediction and
chromatographic enantioseparation facilitation, which sheds light on the
application of machine learning techniques to the experimental scene and
improves the efficiency of experimenters to speed up scientific discovery.
