The overall success of our approach is evidenced by the improved energy predictions and PES descriptions for the CCR system. While atomic gradient predictions showed mixed results, the combination of more balanced charge distributions, reduced extreme force errors, and enhanced energy landscape representation indicates that our optimized GFN2-xTB parameters (see Tables and in SI) provide a more reliable tool for modeling this enzymatic reaction. The DHFR System. The DHFR re-parametrization comprised eight optimization steps (see Table in "GFN2-xTB's parameters optimization setup" section in SI) using CCR-IRC data set supplemented with just 40 additional DHFR-specific geometries from scan calculations (DHFR-SC data set), starting from CCR's best parameters set rather than GFN2-xTB's original parametrization. As shown in Figure , the initial GS using CCR's optimized parameters (BestCCR) already showed substantial improvement over the original GFN2-xTB parametrization, demonstrating the advantage of our sequential optimization strategy. The GS continued to improve through subsequent optimization steps, albeit with smaller incremental gains, indicating rapid convergence to an optimal parameter set. This efficient approach, requiring minimal additional training data, highlights the transferability of parameters between mechanistically similar reactions.