Predictive QSPR modeling of the acidic dissociation constant (pk a) of phenols in different solvents

Given the importance of ionization constant (pKa) of phenols in explaining the mechanism of their toxicity, it is of interest to develop theoretical models for the prediction of pKa values of phenols in different solvent systems. In the present communication, we developed predictive QSPR models for pKa values of substituted phenols in seven different solvent systems such as water, dimethyl sulfoxide (DMSO), methanol, dimethylformamide (DMF), acetonitrile (AN), isopropanol, and ferf-butanol using quantum topological molecular similarity (QTMS) descriptors. The data set was divided into training and test sets, and models were developed using partial least squares (PLS) regression from the training set. The predictive potential of the developed models was assessed by the prediction of pKa values of the test set compounds. Root mean square error of prediction (RMSEP) values were used as objective function for selection of the best models in different solvent systems. Good predictive models were developed in all solvent systems except isopropanol. Considering all seven solvent systems, distance descriptors give consistently good results whereas ellipticity descriptors are of less importance. Moreover, plots of 'variable importance in the projection' (VIP) for the best models highlight the importance of the bond connecting the phenolic oxygen to the aromatic ring. This suggests the diagnostic nature of QTMS descriptors in identifying the reaction center in acidic dissociation of phenols. Copyright © 2008 John Wiley & Sons, Ltd.