We started out by training and testing different models using the O-pyranoside dataset which contains 9039 Oglycosylations. Details on the diversity of the dataset can be found in the SI (Section S1.2). To evaluate the importance of each descriptor, we train and test several models using the condensed graph of reaction (CGR) as the representation for the reaction and include just one additional descriptor each time. Models were trained using 3 different splits: random split, leaving group split, or scaffold split. The random split mimics the use case of predicting the outcome of established glycosylations procedures, while the LG and scaffold splits mimic predicting the stereoselectivity for a new methodology or an unknown substrate, respectively.