Prediction of some thermodynamic properties of sulfonamide drugs using genetic algorithm-multiple linear regressions

A quantitative structure-property relationship (QSPR) has been widely developed to derive a correlation between chemical structures of molecules and their known properties. In the present study, the applicability of various molecular descriptors is tested for the QSPR study on 60 sulfonamide drugs (sulfa drugs). A training set of 50 sulfa drugs is used to construct QSPR models for the prediction of thermodynamic properties such as enthalpy of formation (Delta H-f/kJ mol(-1)) and Gibbs free energy (Delta G(f)/kJ mol(-1)). These properties are analyzed at the B3LYP/6-31G* level using Gaussian 98 software. The QSPR models were optimized using multiple linear regression (MLR) analysis. The Genetic algorithm and backward method were used to reduce the number of descriptors derived from the Dragon software. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the variance inflation factor (VIF), Pearson correlation coefficient (PCC), and the Durbin-Watson (DW) statistics. The predictive powers of the models are discussed using leave-one-out cross-validation (LOOCV) and external validation methods. The statistical coefficients of the models were found to be satisfactory. Thus, QSPR models derived from this study may be helpful for designing some new sulfa drugs and for predicting their properties.