QSRR-based estimation of the retention time of opiate and sedative drugs by comprehensive two-dimensional gas chromatography

Abstract

The quantitative structure-retention relationship (QSRR) of 69 opiate and sedative drugs against the comprehensive two-dimensional gas chromatography retention time (RT) was studied. The genetic algorithm (GA) was employed to select the variables that resulted in the best-fitted models. After the variables were selected, the linear multivariate regressions [e.g., the multiple linear regression (MLR), the partial least squares (PLS)] as well as the nonlinear regressions [e.g., the kernel PLS (KPLS), Levenberg–Marquardt artificial neural network (L–M ANN)] were utilized to construct the linear and nonlinear QSRR models. The correlation coefficient LGO-CV (Q2) of prediction for the GA-KPLS and L–M ANN models for training and test sets were (0.921 and 0.960) and (0.892 and 0.925), respectively, revealing the reliability of these models. The obtained results using L-M ANN were compared with those of GA-MLR, GA-PLS, and GA-KPLS, exhibiting that the L–M ANN model demonstrated a better performance than that of the other models. The resulting data indicated that L–M ANN could be used as a powerful modeling tool for the QSRR studies. This is the first research on the QSRR of the drug compounds against the RT using the GA-KPLS and L–M ANN.