Retention Time Prediction For Chromatographic Enantioseparation By Quantile Geometry-enhanced Graph Neural Network

This article discusses the challenge of separating chiral molecules, which are mirror images of each other, but not superimposable. High-performance liquid chromatography (HPLC) is the mainstream way to separate and analyze the chiral compounds. This work attempts to construct a machine learning prediction model to assist in the choice of experimental conditions for HPLC, such as the column type, flow speed, and elution proportion.