We build three different types of models: 1. Graph-based: For our graph-based model, we used Chemprop where the donor and acceptor are represented as a condensed graph of reaction (CGR) which represents the change in atoms and bonds between the reactants and products (illustrated in Figure GNN), the activators are described as an additional graph, and the solvent as Morgan fingerprints. In the case of multiple solvents, the sum of the fingerprints is used. 2. Fingerprint-based: For the fingerprint-based models, we utilized scikit-learn with all chemical species being represented as fingerprints. 3. OHE: For the one-hot-encoded (OHE) models we also utilize scikit-learn, and all species are represented using OHE. For the donors, each substituent is one-hot-encoded by breaking each bond in the ring, as illustrated in Figure (OHE), to still allow a model to learn to generalize between donors that share substituents. To describe the stereochemistry of the donors, we introduced a donor vector feature as depicted in Figure (Donor vector). Here each stereocenter in the pyranose ring is assigned a number according to the substituent's orientation in a Mills projection. If the substituent is pointing out the plane it is assigned 1 and if it is pointing into the plane -1. In the case of deoxy sugars, it is assigned 0. In this way, each specific stereoconfiguration has a unique vector that corresponds to specific glycoside identities (e.g. gluco, manno, etc.). For instance, all a-D-glucopyranosides would be assigned the vector [-1, -1, 1, -1, 1], and D-, and L-sugars have antiparallel vectors. For all models the temperature is included as a scalar in Celsius and for the full dataset, two additional features are included: the donor type (pyranoside or furanoside) and acceptor type (S, N, or O) both as OHE. We intentionally avoided using computed (physical organic) descriptors for chemical species to avoid introducing biases about which features we would expect to be important.