Accurate computational modeling of enzymatic reactions is a fundamental challenge in biochemistry, with profound implications for understanding enzyme mechanisms, designing inhibitors, and engineering novel enzymes. Quantum mechanics/molecular mechanics (QM/MM) simulations have emerged as a powerful tool for this purpose, offering a balanced trade-off between accuracy and computational cost. However, the reliability of QM/MM simulations heavily depends on the accuracy of the quantum mechanical (QM) methods used to describe the electronic structure of the active site. The computational efficiency of semiempirical QM methods has made them increasingly attractive for QM/MM simulations. These methods enable extensive conformational sampling and the study of large molecular systems, capabilities essential for understanding complex biochemical processes. However, when compared to more accurate ab initio or density functional theory (DFT) calculations, semiempirical methods often struggle to accurately reproduce potential energy surfaces (PES), molecular geometries and reaction energy changes (barriers), particularly in complex enzymatic systems. These limitations have been welldocumented in the literature. For example, traditional semiempirical methods such as AM1 and PM3 have been shown mean absolute errors between 5-10 kcal/mol relative to DFT calculations in reaction energy calculations in enzymatic systems. Even newer methods like GFN2-xTB, despite their improved description of ground state properties, can show significant deviations when modeling transition states and reaction mechanisms, as we depicted in our recent work. Addressing these limitations requires the development of systematic approaches to enhance the accuracy of semiempirical methods while preserving their computational efficiency.