However, compared to organic synthesis, where a huge variety of computational tools based on heuristics and machine learning aid the design of synthesis plans, enzymatic synthesis tools are less common and often limited in their applicability and accuracy. Existing tools for bioretrosynthesis planning, enzyme selection, and reaction rule extraction or scoring are often restricted by the available data, both concerning the amount and the quality of reported reactions in databases. Especially deep-learning-based approaches such as reinforcement learning models , or transformer models rely on large amounts of high-quality reaction data, which is easily obtainable for organic reactions, but not for enzymatic reactions. High-quality, curated datasets such as RHEA usually only report one or a few reactions per enzyme class and are thus small, whereas larger, uncurated databases such as BRENDA pose problems to resolve the provided substrate names, and may contain unbalanced or erroneous reactions. In addition, stereochemical aspects of enzymatic reactions are often disregarded, due to missing or erroneous entries in many databases. This is a major limitation of current approaches, since enzymatic reactions are favored especially for products containing multiple stereocenters, which can be difficult to obtain via organic reactions. Furthermore, atom mappings of enzymatic reactions in current databases are often not available, and can be tedious and error-prone to obtain, especially for biochemical transformations. Datasets of enzymatic reactions that include correct stereoinformation and are atom mapped, balanced, validated, diverse and sufficiently large for data-driven deep learning applications are currently not available, severely hindering the development of new tools and models to design enzymatic synthesis pathways. In a sense, the current paradigm of data-driven research falls short in the field of enzymatic synthesis planning due to the limited size and quality of the available data. We therefore propose to instead follow a research-driven data approach, where the data necessary for new developments is identified, created and curated first.