Quantitative structure-retention relationships (QSRRs) are used to correlate paper chromatographic retention factors of disperse dyes with theoretical molecular descriptors. A data set of 23 compounds with known RF values was used. The genetic algorithm-multiple linear regression analysis (GA-MLR) with three selected theoretical descriptors was obtained. The stability and predictability of the ...

predictive quantitative structure–activity relationship was performed on the novel 4-oxo-1,4-dihydroquinoline and 4-oxo-4h-pyrido[1,2-a]pyrimidine derivatives to explore relationship between the structure of synthesized compounds and their anti-hiv-1 activities. in this way, the suitable set of the molecular descriptors was calculated and the important descriptors using the variable selections ...

in this work, quantitative structure-property relationship (qspr) approaches were used to predict the redox potential of 42 phenolic antioxidants. the structures of all compounds optimized by the am1 semi-empirical method and then a large number of molecular descriptors were calculated for each compound in the data set. subsequently, stepwise multilinear regression was applied to select the mos...

in this work some quantitative structure activity relationship models were developed for prediction of three bioenvironmental parameters of 28 volatile organic compounds, which are used in assessing the behavior of pollutants in soil. these parameters are; half-life, non dimensional effective degradation rate constant and effective péclet number in two type of soil. the most effective descripto...

A successful interpretation of the complex manner by which the GC retention indexes of methylalkanes produced by insects are related to chemical structure was achieved using the quantitative structure-property relationship (QSPR) method. A general QSPR model including mainly topological descriptors was obtained for 178 data points. The error of the model is similar to the experimental error. Th...

A quantitative structure-property relationship (QSPR) study is suggested for the prediction of retention times of volatile organic compounds. Various kinds of molecular descriptors were calculated to represent the molecular structure of compounds. Modeling of retention times of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (...

In this paper, a novel approach is described for the a priori prediction of protein retention in ion exchange systems. Quantitative structure retention relationship (QSRR) models based on a genetic algorithm/partial least squares approach were developed using experimental chromatographic data in concert with molecular descriptors computed using protein crystal structures. The resulting QSRR mod...

Gene Expression Programming (GEP) is a novel genetic algorithm, a highly effective, stable random searching method. We take GEP to make models of Quantitative Structure-Retention Relationship (QSRR) for a series of oxygen-containing organic compounds of GC retention index, and compare the predictive results with Artificial Neural Network (ANN) and Multiple Linear Regression (MLR). The correlati...

This commentary describes two simple procedures using commercially available software packages that greatly facilitate the creation of and replication of data sets intended for quantitative structure activity relationship (QSAR) and quantitative structure property relationship (QSPR) studies. Used properly, the procedures allow the capture of individual chemical structures from the Chemical Abs...

Genetic algorithm and partial least square (GA-PLS) technique was used to investigate the correlation between linear retention index (LRI) for Teucrium chamaedrys volatiles which were obtained by direct thermal desorption (DTD) technique with comprehensive two-dimensional (2D) gas chromatography-time-of-flight mass spectrometry (GC×GC-TOF/MS). The applied internal (leave-group-out cross validat...