In this work, we obtain a broad, high-quality dataset of atom mapped and balanced enzymatic reactions including stereoinformation by developing a large toolbox of automated reaction curation and correction steps, which we apply to BRENDA entries on natural and non-natural substrate-product pairs. The obtained reactions comprise, to the best of our knowledge, the currently largest atom mapped database of biocatalytic transformations. The newly introduced Python code underlying this study furthermore bridges several current gaps concerning the handling of reactions, and tracking of atoms through reactions which current packages such as RDChiral or RDKit do not address yet. We then showcase how the size and quality of the obtained database, EnzymeMap, substantially improves deep learning models of enzymatic reactivity for a broad range of tasks and model archi-tectures, leading to previously unseen accuracies, as well as performances on par with state-of-the-art organic synthesis planning tools. We make the full database and processing steps freely available.