Quantitative structure-property relationships (QSPRs) were investigated for the estimation of the Mayr electrophilicity parameter using a data set of 64 compounds, all currently available uncharged electrophiles in Mayr's Database of Reactivity Parameters. Three collections of empirical descriptors were employed, from Dragon, Adriana.Code, and CDK. Models were built with multilinear regressions, k nearest neighbors, model trees, random forests, support vector machines (SVMs), associative neural networks, and counterpropagation neural networks. Quantum chemical descriptors were calculated with density functional theory (DFT) methods and incorporated in QSPR models. The best results were achieved with SVM using seven empirical and DFT descriptors; an R(2) of 0.92 was obtained for the test set (21 compounds). The final seven descriptors were the Parr electrophilicity index, ε(LUMO), hardness, and four CDK descriptors (FNSA-3, ATSc5, Kier2, and nAtomLAC). Screening of correlations between individual descriptors and Mayr electrophilicity revealed the highest absolute value of correlation for DFT ε(LUMO) (R = -0.82) and comparable correlations for some empirical descriptors, e.g., Dragon's folding degree index (R = -0.80), Kier flexibility index (R = -0.78), and Kier S2K index (R = -0.78). High correlations were observed in the training set between reactivity descriptors calculated by the PM6 semiempirical and DFT methods (R = 0.96 for ε(LUMO) and 0.94 for the electrophilicity index).