Drug design and development are expensive and time-consuming processes,
which in many cases result in failures during the clinical investigation steps. In order to
increase the chances to obtain potential drug candidates, several in silico approaches
have emerged in the last years, most of them based on molecular or quantum
mechanics theories. These computational strategies have been developed to treat a
large dataset of chemical information associated with drug candidates. In this context,
quantum chemistry is highlighted since it is based on the Schrödinger equation with
mathematic solutions, especially the Born-Oppenheimer approximation. Among the
Hartree-Fock-based methods, the Density Functional Theory (DFT) of HohenbergKohn represents an interesting and powerful tool to obtain accurate results for
electronic properties of molecules or even solids, which in many cases are corroborated
by experimental data. Additionally, DFT-related methods exhibit a moderate time-consuming cost when compared to other ab initio methods. In this chapter, we provide
a deep overview focused on the formalism behind DFT, including historical aspects of
its development and improvements. Moreover, different examples of the application of
DFT in studies involving GABA inhibitors, or catalytic mechanisms of enzymes, such
as RNA-dependent RNA polymerase (RdRp) of SARS-CoV-2, and different proteases associated impacting diseases, such as malaria, Chagas disease, human African
trypanosomiasis, and others. Moreover, the role of metal ions in catalytic enzymatic
mechanisms is also covered, discussing iron-, copper-, and nickel-catalyzed processes.
Finally, this chapter comprises several aspects associated with the elucidation of
catalytic mechanisms of inhibition, which could be used to develop new potential
pharmacological agents.
Keywords: Catalytic Mechanism, Copper, Hydrolase, Nickel, Protease, Quantum Mechanics.
