Reaction rules, by definition, describe bond changes and thus establish a one-to-one mapping between the atoms of the reactants and products in the reaction center. The inference of such rules, therefore, requires reaction data endowed with atom-atom maps (AAMs). Unfortunately, reaction data are most commonly provided only as sets of reactant and product molecules without AAM information. Methods for computing AAMs can be divided into rule-based and machine learning (ML)-based approaches. Rule-based techniques, such as Automapper, Indigo, NameRXN, RDTool, and a complex tool described by Jaworski rely on combinatorial optimization criteria such as minimal chemical distance (MCD) or maximum-common subgraph (MCS). These rule-based solutions face challenges in practice.