For the combination of the three models to create the "GlycoPredictor", the random forest and molecular fingerprints-based models were chosen. Fingerprints were chosen instead of OHE as the input substituents and acceptors are not required to be present in the training data, and no large difference in performance is observed for the OHE and FP-based models in our retrospective evaluation.