A quantitative structure–retention relationship study for prediction of chromatographic relative retention time of chlorinated monoterpenes

Abstract

A novel quantitative structure-retention relationship model has been developed for the gas chromatographic relative retention times (t R ) of 67 polychlorinated monoterpene congeners in a non-polar column. Modeling of the relative retention time of these compounds as a function of the theoretically derived descriptors was established by principal component and partial least squares regressions. The choice of optimal training sets is efficiently performed by Kohonen self-organizing map. The genetic algorithm was used for the selection of the variables resulted in the bestfitted models. Appropriate models with low standard errors and high correlation coefficients were obtained. Wiener index, Balaban index, and ideal gas thermal capacity are examples of the descriptors affected by the retention times of polychlorinated monoterpenes.

Topological Indexes and QSRR Methodology in Pharmaceutical and Chemical Analysis

“…Ghasemi et al (120) developed a novel QSRR model for GC-RRTs of 67 polychlorinated monoterpene congeners in a nonpolar column. Wiener and Balaban indexes and ideal gas thermal capacity were affected by the t R values of polychlorinated monoterpenes (120).…”

“…Wiener and Balaban indexes and ideal gas thermal capacity were affected by the t R values of polychlorinated monoterpenes (120).…”

Application of Wavelet and Genetic Algorithms for QSAR Study on 5-Lipoxygenase Inhibitors and Design New Compounds

“…This rule or function is then utilized to predict the same bioactivities of compounds which are not involved in the training set from their structural descriptors. Model development in QSAR studies comprises different critical steps as (1) descriptor generation, (2) data splitting to calibration (or training) and prediction (or validation) sets, (3) variable selection, (4) finding appropriate model between selected variables and activity and (5) model validation [5]. Since a large number of molecular descriptors are available for QSAR analysis, the most relevant descriptors should be selected.…”