We turned the reaction Simplified Molecular-Input Line-Entry System (SMILES) into reaction fingerprints by subtracting the sum of the reactant fingerprints from the product fingerprint. The reactants and product fingerprint consist of a 512-bit extended connectivity fingerprints with a radius 3, 15 concatenated to a 512-bit RDKit fingerprints with a maximum path length of 7. Because we are working with Buchwald-Hartwig reactions, we constructed chemical contexts based on chemicals categorized as catalyst, pre-catalyst, ligand, base, solvent, or reagent. We identified precatalysts and catalysts from a curated list or flagged any chemical containing Pd or Pt elements. We identified solvents using the assignment in the original ELN record. We used atom mappings from the Biovia software to identify reagents as molecules not contributing to the product. 17 Using a curated list of bases and ligands we assigned these from the list of reagents. We include a "reagent" category since not all chemicals fell into either of these categories. We kept only reaction variations using one of the 30 most common contexts for our model training. If the same reaction occurred multiple times in the dataset, we kept only the variant with the highest yield for each reaction when training the single-label model. Both the trained models had their chemical contexts converted to one-hot vectors. We construct the multi-hot vectors for training the multi-label model by merging one-hot vectors using a bitwise-or operation. We kept unique reactions only for the multi-label model. The single-label data were weighted by the yield, whereas for the multi-label data no weighting was applied. Between 10 -5 and 5*10 -3 Between 10 -5 and 5*10 -3 Dropout rate Between 0 and 0.9 Between 0 and 0.9