Genetic algorithm and partial least square (GA-PLS) and kernel PLS (GA-KPLS) techniques were used to investigate the correlation between retention indices (RI) and descriptors for 117 diverse compounds in essential oils from 5 Pimpinella species gathered from central Turkey which were obtained by gas chromatography and gas chromatography-mass spectrometry. The square correlation coefficient leave-group-out cross validation (LGO-CV) (Q 2 ) between experimental and predicted RI for training set by GA-PLS and GA-KPLS was 0.940 and 0.963, respectively. This indicates that GA-KPLS can be used as an alternative modeling tool for quantitative structure-retention relationship (QSRR) studies.
Genetic algorithm (GA) and partial least squares (PLS) and kernel PLS (KPLS) techniques were used to investigate the correlation between immobilized liposome chromatography partitioning (log Ks) and descriptors for 65 drug compounds. The models were validated using leave-group-out cross validation LGO-CV. The results indicate that GA-KPLS can be used as an alternative modelling tool for quantitative structure-property relationship (QSPR) studies.
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 bestfitted 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), LevenbergMarquardt artificial neural network (L-M ANN)] were utilized to construct the linear and nonlinear QSRR models. The correlation coefficient LGO-CV (Q 2 ) 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.
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