Numerous models have been developed to estimate kinetic parameters, each offering unique approaches and advantages. For instance, the reaction mechanism generator (RMG) software employs a hierarchical decision tree to match a given reaction with similar ones from its database, extracting and averaging their kinetic parameters for estimation. This method provides a convenient and interpretable means to quickly estimate kinetic parameters, particularly for well-characterized reaction types. The quantitative structure-activity relationship (QSAR) model has also been utilized to explore the relationship between kinetic properties and the physico-chemical descriptors of reactants and products. For instance, van Gerwen et al. extend quantum machine learning to predict reaction properties by introducing reaction representations based on atomic charges and coordinates; Hoonakker et al. introduced the condensed graph of reaction (CGR) encoding method, which combines reactants and products into a single pseudomolecule, enabling the use of standard cheminformatics methods to featurize reactions and predict their kinetic parameters .