We have developed a database curation, validation and mapping pipeline for enzymatic reactions, producing an extensive dataset of atom mapped, balanced, validated, and diverse reaction which include correct stereoinformation from BRENDA entries. We showcase that this new dataset, EnzymeMap, is sufficiently large for data-driven deep learning and offers significant performance improvements over all previous databases for retrosynthesis, forward prediction and regioselectivity prediction tasks. For the first time, we report prediction performances on par with organic retrosynthesis tools. This performance boost comes from both better coverage (larger number of total reactions, larger number of reactions per EC class), as well as better quality regarding the reactions itself and their atom mappings. We distribute the full pipeline as easy-to-use Python package, and demonstrated that the workflow can be easily adapted to other databases such as MetaCyc or KEGG. We expect that the EnzymeMap database will spark numerous inventions in the field of computer-aided enzymatic reaction prediction, especially for applications relying on mapped, balanced reactions such as the computer-aided design of enzymatic cascades.