Most importantly, the optimization objectives of MCD and MCS only approximate the inference of the actual chemical mechanism. Therefore, even exact solutions of the corresponding combinatorial optimization problem may yield a chemically incorrect AAM. Computational cost, moreover, may become an issue since various variants of MCS are well-known NP-complete problems. As an alternative, several ML-based tools have become available in recent years, and most prominently AMLGAM, RXNMapper, GraphormerMapper, and LocalMapper. These methods bypass the formulation of the task as a combinatorial optimization problem and avoid the computationally expensive subgraph matching process by utilizing data-driven methods. Despite significant progress, AAMs obtained from machine learning approaches are not perfect either, as demonstrated both by recent benchmarking efforts and the performance data published with the individual tools.