Evaluation of the pKa's of Quinazoline Derivatives : Usage of Quantum Mechanical Based Descriptors
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In this study, several quantum mechanical-based computational approaches have been used in order to propose accurate protocols for predicting the p<em>K<sub>a</sub></em>’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 p<em>K<sub>a</sub></em>’s and nine different DFT descriptors (atomic charge on nitrogen atoms (<em>Q</em>(N), ionization energy (<em>I</em>), electron affinity (<em>A</em>), chemical potential (m), hardness (h), electrophilicity index (w), fukui functions (<em>f <sup>+</sup></em>, <em>f <sup>-</sup></em>), condensed dual descriptor (D<em>f</em>) and local hypersoftness (s<sup>(2)</sup>)) 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 p<em>K<sub>a</sub></em>’s of the quinazoline derivatives. The calculated atomic charge on N<sub>1</sub> atom (<em>Q</em>(N<sub>1</sub>)) was shown to be the best descriptor to reproduce the experimental p<em>K<sub>a</sub></em>’s (R<sup>2</sup>=0.927), whereas strong correlations were also derived for <em>A</em>, w, m, and Δ<em>f</em>. In the last part, the applicability of isodesmic reaction scheme to the p<em>K<sub>a</sub></em> prediction of quinazoline derivatives was tested, and the calculated <em>A</em> 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 p<em>K<sub>a</sub></em> predictions of quinazoline derivatives and the relationships derived can be effectively used in data generation for successful machine learning models for p<em>K<sub>a</sub></em> predictions.
