Machine learning-assisted retention time predictions on a cellulose Tris(3,5)-dimethylphenylcarbamate column in polar organic mode

Our research presents a novel approach for predicting chiral separations, offering an easy-to-use, open-access web tool to the scientific community. The robust GNN model was used to create a web server (https://chiralscreen.com) that enables the prediction of retention times, separation capabilities, and elution orders for various compounds. Furthermore, the software helps determine optimal initial chromatographic conditions for separations.