In this study, several quantum mechanical-based computational approaches have been used in order to propose accurate protocols for predicting the pKa’s of quinazoline derivatives, which constitute a very important class of natural and synthetic compounds in organic, pharmaceutical, agricultural and medicinal chemistry areas. Linear relationships between the experimental pKa’s and nine different DFT descriptors (atomic charge on nitrogen atoms (Q(N), ionization energy (I), electron affinity (A), chemical potential (m), hardness (h), electrophilicity index (w), fukui functions (f +, f -), condensed dual descriptor (Df) and local hypersoftness (s(2))) were considered. Several DFT methods (a combination of five DFT functionals and two basis sets) in conjunction with two different implicit solvent models were tested, and among them, M06L/6-311++G(d,p) level of theory employing the CPCM solvation model was found to give the strongest correlations between the DFT descriptors and the experimental pKa’s of the quinazoline derivatives. The calculated atomic charge on N1 atom (Q(N1)) was shown to be the best descriptor to reproduce the experimental pKa’s (R2=0.927), whereas strong correlations were also derived for A, w, m, and Δf. In the last part, the applicability of isodesmic reaction scheme to the pKa prediction of quinazoline derivatives was tested, and the calculated A was shown to be a well-established method for the classification of molecules, and thus, for the identification of a suitable reference molecule for the calculations. The QM-based protocols presented in this study will enable fast and accurate high-throughput pKa predictions of quinazoline derivatives and the relationships derived can be effectively used in data generation for successful machine learning models for pKa predictions.