Despite the availability of numerous AAM algorithms and tools, accurate automatic AAM for complex or unknown reactions remains challenging. In existing approaches, AAM is typically formulated as an NP-hard combinatorial optimization problem, such as finding maximum common subgraphs. Consequently, the computation time required to identify exactly optimal mapping(s) increases exponentially with the number of atoms involved in a chemical reaction. Due to the NPhard nature, existing AAM frameworks often rely on heuristics, such as incorporating known chemical rules and machine learning techniques. However, the criteria for selecting the reaction rule sets are strongly dependent on the individual dataset, and identifying the correct reaction rules for various reaction cases and datasets is still challenging. Furthermore, the accuracy of the rulebased automatic AAM for reaction types not included in the dataset may be low due to the lack of reaction rules. Machine learning-based approaches sometimes show incorrect mapping based on the frequently appearing reaction patterns in the training dataset. Therefore, accurate automatic AAM demands a universal al-gorithm that relies on minimal chemical knowledge, without the need for known reaction rules or training data, particularly for unknown reactions.