The variable connectivity index 1chi(f) versus the traditional molecular descriptors: a comparative study of 1chi(f) against descriptors of CODESSA.

In this study we compared the prediction abilities of the variable connectivity index 1chi(f) (not included in CODESSA) with topological indices available from CODESSA. We selected the boiling points of n = 100 alcohols as the property and examined the pool of 56 topological indices. Prediction capabilities of the developed models were evaluated by classical training/test set approach. RMS errors calculated from the prediction set for the MLR models obtained from CODESSA software with 1, 2, 3, 4, and 5 parameters were 9.06, 5.69, 5.40, 4.9, and 3.37 degrees C, respectively. Using the variable connectivity index with weights x = 0.10 and y = -0.92 for carbon and oxygen atom respectively, we obtain regression BP = 38.12 1chi(f) - 37.56 with the correlation coefficient r = 0.9915, RMS error 4.21 degrees C calculated from the test set, and Fisher ratio F = 5691. Prediction capability of the variable connectivity index was better than for MLR regression model with up to four parameters.     

Multiple linear regression and artificial neural network retention prediction models for ginsenosides on a polyamine-bonded stationary phase in hydrophilic interaction chromatography

The development of retention prediction models for the seven ginsenosides Rf, Rg1, Rd, Re, Rc, Rb2, and Rb1 on a polyamine-bonded stationary phase in hydrophilic interaction chromatography (HILIC) is presented. The models were derived using multiple linear regression (MLR) and artificial neural network (ANN) using the logarithm of the retention factor (log k) as the dependent variable for four temperature conditions (0, 10, 25, and 40 degrees C). Using stepwise MLR, the retention of the analytes in all the temperature conditions was satisfactorily described by a two-predictor model wherein the predictors were the percentage of ACN (%ACN) in the mobile phase and local dipole index (LDI) of the compounds. These predictors account for the contribution of the solute-related variable (LDI) and the influence of the mobile phase composition (%ACN) on the retention behavior of the ginsenosides. A comparison of the models derived from both MLR and ANN revealed that the trained ANNs showed better predictive abilities than the MLR models in all temperature conditions as demonstrated by their higher R(2) values for both training and test sets and lower average percentage deviation of the predicted log k from the observed log k of the test compounds. The ANN models also showed excellent performance when applied to the prediction of the seven ginsenosides in different sample matrices.     

A quantitative structure–mobility relationship of organic acids using solvation parameters

A quantitative structure–mobility relationship (QSMR) is proposed to estimate the electrophoretic mobility of diverse sets of analyses in capillary zone electrophoresis using Abraham solvation parameters of analyses, such as the excess molar refraction, polarizability, hydrogen bond acidity, basicity, and molar volume. QSMR was developed for prediction the electrophoretic mobility of 231 organic acids using the solvation parameters calculated by Abraham. Multiple linear regression (MLR) as a linear model and artificial neural network (ANN) methods were used to evaluate the nonlinear behavior of the involved parameters. The prediction results are obtained by nonlinear model, ANN, seem to be superior over MLR and were in good agreement with experimental data. In the proposed ANN–QSMR model, the overall mean percentage deviation values were 5.6, 5.4, and 5.3% and the coefficients of determinations (R²) were 0.84, 0.84, and 0.84 for training, test, and verification set, respectively. To investigate the robustness of the model, cross-validation methods have been established, i.e., leave-one-out and leave-N-out (N?=?5 and 10) and model is showed good predictive ability against data variation in cross-validation process. This model is not only able to accurately predict the migration order of a diverse set of organic acids but also model finds that solvation parameters are responsible in separation mechanism.