Quantitative Structure‐Retention Relationships (QSRR) of Some CNS Agents Studied on DB‐5 and DB‐17 Phases in Gas Chromatography

Abstract

A QSRR study was performed to develop a predictive model correlating molecular structures with their RIs in gas chromatography. CNS agents included some benzodiazepines, barbiturates and phenytoin, which had been studied, previously, on two DB stationary phases with different phenyl percentages. At first, a high quality model (R 0.988, F 253.89 for DB-5 and R 0.983, F 164.721 for DB-17 column) was generated for two columns separately, using only calculated descriptors and multiple linear regression techniques. Then a combined model, added a polarity term of stationary phase (M), was also developed for these columns, and the result was satisfactory (R 0.989, F 232.354). The results of leave-one-out cross and external validation techniques showed good predictive ability of the models (R 2 cv 0.967, R 2 Prediction set 0.954). The results of the study indicated that a sixparameter equation can be utilized for prediction of retention indices of compounds on DB-5 and DB-17 stationary phases simultaneously, for which there are no empirical RI values.

Use of Topological Indices of Organic Sulfur Compounds in Quantitative Structure‐Retention Relationship Study

“…A large number of structure-retention index correlations have been developed for various groups of compounds using different types of descriptors. Some of the model compounds are dialkyl hydrazones [4], alkylbenzenes [5], methylalkanes [6], oxo compounds [7], disulfides [8] and central nervous system agents [9]. However, among the descriptors, topological indices are particularly of interest because they can be readily calculated from the molecular structures [10 -14].…”

Quantitative structure-retention relationship for polychlorinated dibenzofurans based on molecular interaction field analysis

“…Gas chromatographic retention parameters, which are typically used to qualitatively identify PCDFs congeners [7] , are determined by electronic, chemical and structural properties of compounds [8] . Therefore, descriptors encoding significant structural information such as topology, geometry, and electronic environment can be used to model the reten tion behavior of compounds [8,9] .…”