Fig. depicts the top-N accuracy of neural networks trained on identifying templates which lead to the recorded precursors, i.e. predicting retrosynthetic single-step pathways. The gray area corresponds to top-N accuracies typically achieved by organic retrosynthesis models for the USPTO-50k dataset, with values taken from Refs. 82, 83 and 75, including neural network models on fingerprints, graph-convolutional neural networks on graphs and transformer models on strings or graphs. Although these are different models trained on different datasets (i.e. not directly comparable), they showcase an important prerequisite for computeraided retrosynthesis, namely that powerful one-step synthesis models are needed, which can subsequently be used in multistep synthesis planning tools. Using EnzymeMap, for the first time, an enzymatic retrosynthesis model is able to compete with the accuracy of organic retrosynthesis tools, which is remarkable given that the model is very simple, and was not optimized in any way. In comparison, models trained on MetAMDB or RHEA feature a relatively low accuracy. A low top-N accuracy is especially problematic for designing multi-step synthesis pathways, where clever ranking algorithms are essential to navigate the combinatorially explosive number of template application possibilities at each reaction step. For instance, using the low top-1-accuracy of 0.18 for RHEA from Table , designing a pathway of three independent steps by taking the top-1 template at each step, only 0.18 3 = 0.006 = 0.6% of test products would yield the correct synthesis pathway. In practice, this would be even lower since we do not want to find just any pathway consisting of three reactions that produce the product, but pathways starting from e.g. buyable materials, or having no unnecessary loops of protection and deprotection. In comparison, 9.7% of test products (0.46 3 = 0.097) would be recovered using the EnzymeMap model (taking only the top-1 recommendation), which is an about twenty-fold increase in success rate. We therefore anticipate EnzymeMap to also perform well with multi-step retrosynthesis models, although the training of such models via e.g. Monte Carlo Tree Search or Reinforcement Learning is beyond the scope of the current study. Table furthermore lists several ablation studies to showcase the importance of the data curation, cleaning and validation routines developed in this study. First, we explore whether mapping the resolved raw reaction SMILES from BRENDA via an alternative route to Fig. affects the performance of retrosynthesis models. To this end, we map the first reaction SMILES per BRENDA entry using the state-of-the-art transformer-based atom mapper RXNMapper. This leads to a large loss in performance yielding top-N accuracies close to those achieved by the MetAMDB model. We therefore conclude that the careful mapping, correction and validation approach for enzymatic reactions developed in this work is essential for a good performance of reaction prediction models. Second, we take the cleaned and curated reactions from EnzymeMap and re-map them with RXNMapper. This also leads to a large loss in performance, although to a less extent, resulting in top-N accuracies below the performances typically achieved for organic reactions. We therefore conclude that also the quality of atom maps is essential for training reaction prediction models, and that simple, uncurated mapping impacts the performance negatively. This ablation study therefore showcases the importance of database quality.