QSAR models to predict physico-chemical Properties of some barbiturate derivatives using molecular descriptors and genetic algorithm- multiple linear regressions

Abstract

In this study the relationship between choosing appropriate descriptors by genetic algorithm to the Polarizability (POL), Molar Refractivity (MR) and Octanol/water Partition Coefficient (LogP) of barbiturates is studied. The chemical structures of the molecules were optimized using ab initio 6-31G basis set method and Polak-Ribiere algorithm with conjugated gradient within HyperChem 8.0 environment. Three structural parameters were calculated using a quantum-mechanical method and Polak-Ribiere geometric optimization followed ab initio 6-31G method. The multiple linear regressions (MLR) and Backward methods (with significant at the 0.05 level) were employed to give the QSAR models. After MLR analysis, we studied the validation of linearity between the molecular descriptors in the best models for use properties. The predictive powers of the models were discussed by using the method of cross-validation. The results have shown that descriptor (MPC08, SIC2, TIC0), (ZM1V, IC2, GNar, UNIP, X3) and (S1K, Mi, SMTIV) could be used for modeling and predicting the MR, LogP and POL of the corresponding barbiturates respectively.