We then removed reactions from the EnzymeMap training and validation sets in a further set of ablation studies to benchmark the importance of database quantity. The performance degrades for every loss of information, starting with omitting multi-step reactions, reverse reactions, suggested reactions, and combinations thereof, showcasing the value and need for each of the processing steps in the EnzymeMap workflow. The increased coverage of enzymatic reaction space by adding multi-step reactions, reverse reactions or correcting entries via suggesting outcomes (instead of dropping the entry) is beneficial to the performance of the trained retrosynthesis model, highlighting the need for larger reaction databases.