Estimation of Mayr Electrophilicity with a Quantitative Structure–Property Relationship Approach Using Empirical and DFT Descriptors

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 R2 of 0.92 was obtained
for the test set (21 compounds). The final seven descriptors were
the Parr electrophilicity index, εLUMO, 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 εLUMO (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 εLUMO and 0.94 for the electrophilicity index).