Overall, EnzymeMap leads to a large performance increase in both the forward and retro model. The retrained models (on the previous version 1 of EnzymeMap) have been made available as the default biocatalysis models on the IBM RXN-for-Chemistry platform (). furthermore lists the achieved accuracy and top-1 accuracy. As evident in Table , EnzymeMap offers a large performance boost for regioselectivity predictions for the full datasets (right-most data points in Fig. ), but also for random subsets of the training data. Interestingly, we find a better top-1 accuracy for RHEA vs. MetAMDB when trained on the same number of datapoints (right panel in Fig. , which underpins the quality of RHEA which is heavily curated. Similarly, we conclude that EnzymeMap not only offers a benefit in size and diversity (i.e. coverage of reactions), but also in its low inherent noise obtained via the extensive validation and curation efforts described in the Methods section. Through comparison of different training sizes, we conclude that both the quantity and quality of recorded reactions influence the performance of reaction prediction models, giving EnzymeMap a large benefit over other databases. With the full dataset utilized, we obtain a flat accuracy of 87% to discern between reactive and non-reactive reaction instances, as well as a top-1 accuracy of 76% to identify the correct regioselective reaction outcome given a set of product options. Together with the high accuracies reported for retrosynthesis and forward prediction, this lays the groundwork of successful data-driven biocatalytic synthesis design. We anticipate further performance improvement upon inclusion of enzyme information to the model input, so that the model can learn different regioselectivies exhibited by different enzymes (which in this study only manifests as aleatoric, irreproducible error).