The original model was retrained, too, to ensure that the correct values were reproduced. In detail, the models 'EC3' were retrained, which utilize the first three digit of the EC number in the reaction SMILES, for example for the EC number 1.1. Since the models do not require atom mapped reactions, we use the full set of reactions after step 2 in Fig. , amounting to 100k reactions, where only duplicates within an EC class (but not across EC classed) were removed, since the model makes use of the EC class. After tokenization at the third-digit EC class, and adding the ECREACT reactions, we arrive at 90k unique reactions (as opposed to 57k reactions with only ECREACT). In this benchmark, we do not showcase the capabilities of the EnzymeMap correction and curation pipeline (since the reactions are unmapped and uncorrected), but only the influence of the size of the dataset and the number of reactions per EC class. The 100k reactions contain also unbalanced reactions, as well as those that could not be mapped in our processing pipeline, since we expect the transformer model to still be able to make use of them. Furthermore, if different options to resolve a trivial name to a SMILES string were found, the dataset contained all options, which explains the large size of 100k versus 63k reactions in EnzymeMap.