The inference of reaction rules crucially depends on the accuracy of the ITS and thus on the underlying AAMs. While recent machine learning-based methods have been reported to yield improvement over older methods that solve combinatorial optimization problems, they are still subject to a non-negligible degree of uncertainty which may result in unequivalent AAMs and very different reaction centers. Figure shows an example of a reaction for which results of RXNMapper, GraphormerMapper, and LocalMapper are different.