Life and its extraction fuels climate change. We performed studies upon an extended series of petroleum hydrocarbons, with octanol-water partition coefficients (log Kow), by using the quantitative structure-activity relationship (QSAR) methods that imply analysis of correlations and representation of models. A suitable set of molecular descriptors was calculated and the genetic algorithm (GA) was employed to select those descriptors, resulting in the best-fit models. The partial least squares PLS (PLS) was utilized to construct the linear QSAR model. The best GA-PLS model contains 27 selected descriptors in 10 latent variables space. The R2 and RMSE for training and test sets were (0.827, 0.088) and (0.716, 0.185), respectively. Inspection of the results reveals a higher R2 and lowers the RMSE value parameter for the data set GA-PLS. The GA-PLS linear model has good statistical quality with low prediction error. This is the first research on the QSAR which uses GA-PLS for the presiction octanol-water partition coefficients of some of the environmental toxic of the petroleum substances.
KEYWORDS
Petroleum substances Genetic algorithm QSAR
Graphical AbstractQuantitative structure activity relationship (QSAR) study… 117
A quantitative structure-retention relationship (QSRR), was developed by using the genetic algorithm-partial least square (GA-PLS), Kernel partial least square (GA-KPLS) and LevenbergMarquardt artificial neural network (L-M ANN) approach for the prediction of the retention time (RT) of the doping agents in urine. The values of the retention time were obtained by using ultra-high-pressure liquid chromatography-quadrupole time-offlight mass spectrometry (UHPLC-QTOF-MS). A suitable set of the molecular descriptors was calculated and the important descriptors were selected by the aid of the GA-PLS and GA-KPLS. By comparing the results, GA-KPLS descriptors are selected for L-M ANN. Finally a model with a low prediction error and a good correlation coefficient was obtained by L-M ANN. This model was used to predict the RT values of some of doping agents which were not used in the modeling procedure. This is the first research on the QSRR of doping agents against the RT using the GA-PLS, GA-KPLS and L-M ANN model.
KEYWORDS
Graphical AbstractApproach to Chemometrics Models … P a g e | 106
A B S T R A C TA quantitative structure-retention relation (QSRR) study was conducted on the retention times of 160 pesticides and 25 environmental organic pollutants in wine and grape. The genetic algorithm was used as descriptor selection and model development method. Modeling of the relationship between the selected molecular descriptors and retention time was achieved by linear (partial least square; PLS) and nonlinear (kernel PLS: KPLS and Levenberg-Marquardt artificial neural network; L-M ANN) methods. The QSRR models were validated by cross-validation as well as application of the models to predict the retention of external set compounds, which did not have contribution in model development steps. Linear and nonlinear methods resulted in accurate prediction whereas more accurate results were obtained by L-M ANN model. The best model obtained from L-M ANN showed a good R 2 value (determination coefficient between observed and predicted values) for all compounds, which was superior to those of other statistical models. This is the first research on the QSRR of the compounds in wine and grape against the retention time using the GA-KPLS and L-M ANN.
A quantitative structure-retention relation (QSRR) study was conducted on the range-scaling transformation (Xa) of the nanoparticle compounds which obtained by comprehensive two-dimensional gas chromatography (GC×GC) stationary phases consisting of thin films of the gold-centered monolayer protected nanoparticles (MPNs) system. The genetic algorithm was used as descriptor selection and model development method. Modeling of the relationship between the selected molecular descriptors and the retention time was achieved by linear (partial least square; PLS) and nonlinear (Levenberg-Marquardt artificial neural network; L-M ANN) methods. Linear and nonlinear methods resulted in an accurate prediction whereas more accurate results were obtained by L-M ANN model.
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