Next, we showcase the merits of EnzymeMap for large deeplearning models not dependent on atom mappings, i.e. benchmark only the effect of increased coverage, but not quality. The IBM RXN-for-Chemistry platform provisions forward and backward models for biocatalysed reaction predictions. Both models have been trained using a multi-task transfer learning approach on a transformer architecture. The multi-task transfer learning regime consisted of samples from two training sets: the USPTO data set containing 1 million organic reactions and the data set ECREACT consisting of 56,579 biocatalysed reactions sourced from the databases RHEA, BRENDA, PathBank, and MetaNetX. Including the enzyme commission numbers during training enables the backward model to not only predict the substrates based on a product, but also the class of the catalysing enzyme. While the model showed state-of-the-art performance, it was held back by the limited availability of enzyme-catalysed reactions. As ECREACT contained only 11,130 reactions extracted from BRENDA, retraining the models using the considerably larger non-atom mapped version of EnzymeMap in addition to ECREACT leads to a 59% increase in training set size (at an overall dataset size of 90,028), which results in an increase in the mean predictive accuracy of sub-classes in the forward model across all enzyme classes and an increase in the mean predictive accuracy of the retro model with the exception of transferases and ligases, see Table . As the new data introduced with the inclu- sion of EnzymeMap not only adds new samples for the regions of reaction space covered by ECREACT but also increases the covered space, the increase in dataset size does not always lead to an increase in predictive accuracy. Indeed, the addition of diverse data can even lead to a (insignificant) decrease in predictive accuracy as is the case for ligases in the backwards model. The above benchmarks showcase the effect of a sheer increase in dataset size, but not necessarily dataset quality. We therefore also retrained all models with only the processed and validated EnzymeMap reactions + ECREACT (with an overall dataset size of 83,470). Here, we find even further improvements compared to the raw EnzymeMap dataset for nearly all enzyme classes in both the forward and reverse model. Thus, even for unmapped reactions, curating a high-quality dataset can have beneficial effects to models trained on them, highlighting the need of both high-quality and high-quantity datasets for chemical deep learning.