QSRR Analysis in Characterization of Some Benzimidazole Derivatives

Milica Karadžić,Lidija Jevrić,Strahinja Kovačević,Sanja Podunavac-Kuzmanović

doi:10.17344/acsi.2014.1134

Milica Karadžić, Lidija Jevrić + Show 2 more

Open Access

https://doi.org/10.17344/acsi.2014.1134

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Journal: Acta chimica Slovenica	Publication Date: Dec 15, 2015
Citations: 1	License type: cc-by

Affiliation: University of Novi Sad

Abstract

In this paper, quantitative structure-retention relationship study has been applied in order to correlate obtained retention parameter R(M)(0) and two groups of molecular descriptors, for eleven investigated benzimidazole derivatives. Principal component analysis (PCA), followed by hierarchical cluster analysis (HCA) and multiple linear regression (MLR), was applied in order to identify the most important molecular descriptors. Mathematical models were established and the best models were further validated by leave-on-out (LOO) technique as well as by the calculation of the statistical parameters. Statistically significant models were established.

Highlights

Benzimidazoles, as biologically active compounds, are frequently studied group of molecules
Principal component analysis (PCA) was performed on both sets of molecular descriptors in order to reveal some similarities among studied molecules
From score plot for molecular descriptors, any type of grouping of the molecules cannot be observed along the PC1 or PC2 axis

Summary

Introduction

Benzimidazoles, as biologically active compounds, are frequently studied group of molecules. Strict control of experimental conditions can be obtained and that qualifies reversed-phase thin-layer chromatography as suitable technique for estimating physicochemical parameters and biological activity of molecules.[9,10,11,12] For understanding the chromatographic processes, it is very convenient to establish mathematical models. Established QSRR models can be widely applied for identification of the most useful structural descriptors, prediction of the retention of new synthesized molecules and identification of unknown analytes.[13] In QSRR analysis, correlation between retention data (RM0 values) and structural parameters (molecular descriptors), can be examined by linear regression (LR) and multiple linear regression (MLR), principal component regression (PCR), partial least squares regression (PLS) and automated neural networks (ANN)

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