For several reasons we chose to map reactions by applying known rules instead of mapping via conventional atom mapping tools. First of all, the available tools feature an imperfect accuracy especially for biochemical transformations. Second, recording the reaction rule used to map a reaction allows for further processing steps, such as judging the quality of a mapping, as well as the quality of the reaction itself by comparing reaction rule counts across enzyme classes. We can furthermore correct wrong stereoinformation in the products. Finally, we can easily group reactions into classes by comparing their reaction rules. Mapping via known rules thus offers the possibility to construct a higher quality dataset compared to simply mapping a known dataset with conventional mapping tools. We demonstrate the benefit of our mapping scheme for subsequent machine learning tasks later in this manuscript. However, we note that the coverage of reactions that can be mapped heavily depends on the chosen rule set. To the best of our knowledge, the Broadbelt rule set from Ref. it the most complete and validated set currently available, but even for this set a number of missing rules was identified in the course of the present study, and will be addressed in future work.