Three different datasets were employed. RHEA, exactly as provided in Ref. 44, as well as MetAMDB (which is based on BKMS-react ) and EnzymeMap (this work, based on BRENDA ), since these datasets contain balanced atom mapped reactions. MetAMDB consists of approximately 43,000 reactions with atom mappings generated by the Reaction Decoder Tool, and thus constitutes the largest atom mapped reaction set prior to the current study, serving as a comparison to EnzymeMap regarding quantity. RHEA was chosen because it is highly curated and reliably, thus serving as a comparison regarding quality. Furthermore, it was utilized in a recent bioretrosynthesis study. For MetAMDB and EnzymeMap, we follow the RHEA preparation instructions from Ref. 44 closely. Namely, we removed all products occurring more than 100 times in the dataset to get rid of common cofactors or other frequent molecules such as protons, and then deleted molecules from the reactants that had no matching atoms in the products. We then kept all reactions that had a single product molecule. Importantly, duplicates were removed for all reactions, which might occur where e.g. reactions differed only in their cofactors before cofactor removal. This creation of single-product reactions is necessary for the chosen model architecture, which can only take a single product as input. For EnzymeMap, this led to 18k reactions and 2.8k templates. For MetAMDB, 14k reactions and 5k templates were obtained. For RHEA, 8k reactions and 4k templates were obtained. All datasets were randomly split to 80% training, 10% validation and 10% test sets. We used the entire dataset, including templates that occurred infrequently or even only once. This made our modelling task much more challenging compared to approaches that exclude templates with few reaction precedents. We furthermore report ablation studies with the same test set as EnzymeMap for MetAMDB, RHEA and subsets of EnzymeMap, were all identical reactions to this common test set were removed and the remainder of reactions split into 80% training and 20% validation sets, respectively. In another ablation study, we remapped EnzymeMap (either raw from BRENDA, or after the EnzymeMap workflow) using the popular transformer-based atom mapping software RXN-Mapper, which is the current state-of-the-art mapper regarding versatility and accuracy. We used default settings as provided in the RXNMapper Python package. We report top-N accuracies for all models, i.e. the ratio of test datapoints where the correct template was identified in the top-N suggestions. Although this metric is associated with a few shortcomings concerning its translation to real-world performance for retrosynthesis tasks, it allows us to compare our approach to performances for organic retrosynthesis, which is typically reported using top-N accuracies.