These results validate not only our re-parametrization strategy but also demonstrate its efficiency. By strategically combining data from mechanistically similar systems, we achieved accurate parameters for both CCR and DHFR while minimizing the computational costs of system-specific parametrizations. This approach offers a promising pathway for extending semi-empirical methods to other enzymatic systems, particularly those involving similar reaction mechanisms. For the CCR system, PC1 captures 74.6% of the total variance, demonstrating a clear structural evolution from reactant to enolate state. While both Hamiltonians show excellent agreement throughout most of the reaction coordinate, some differences emerge in the reactant region where DFT samples a small conformational space not accessed by GFN2-xTB.