Computational chemistry serves as a powerful tool for analyzing catalytic systems and molecular properties without the need for extensive laboratory experimentation. Leveraging modern electronic structure theory and density functional theory (DFT), researchers can model catalysts, predict activation energies, evaluate site reactivity, and calculate other critical thermodynamic parameters. In the field of drug development, computational methods accelerate the discovery process by simulating drug molecules, estimating difficult-to-measure values such as pKa, and optimizing synthetic routes, thereby reducing both time and cost. These simulations are grounded in quantum chemical approaches that solve the molecular Schrödinger equation. Among these, ab initio methods—derived purely from theoretical principles without empirical data—play a central role. Such methods require the specification of a level of theory and a basis set, which together enable the description of molecular orbitals through the linear combination of atomic orbitals (LCAO). A widely used ab initio technique is the Hartree–Fock method, which approximates electron-electron interactions by averaging them rather than treating them explicitly. As basis set complexity increases, the results converge towards the Hartree–Fock limit, providing increasingly accurate models of molecular behavior. Overall, computational chemistry not only enhances understanding of chemical systems but also supports the development of efficient, cost-effective solutions in catalysis and pharmaceutical research.