A Comparative QSRR Study on Enantioseparation of Ethanol Ester Enantiomers in HPLC Using Multivariate Image Analysis, Quantum Mechanical and Structural Descriptors

Abstract

Quantitative structure–retention relationship study was carried out for predicting the retention times of twenty‐six 1‐(2‐naphtyl)‐1 ethanol ester enantiomers in high‐pressure liquid chromatography by using original molecular, quantum mechanical, and multivariate image analysis (MIA) descriptors. Multiple linear regressions, partial least squares (PLS), and partial component regression (PCR) models combined with genetic algorithm (GA) were constructed as variable selection methods by using molecular descriptors to investigate the potential relationship between the selected descriptors and the retention times of the enantiomers. The molecular descriptors were generated from the molecular structure of enantiomers and calculated by the DRAGON software. Besides 504 DRAGON descriptors, additional 38 quantum mechanical descriptors were obtained using density functional theory/B3LYP/6‐31G method. Then, the results of the obtained models were compared with MIA descriptors that are pixels of two‐dimensional (2D) chemical structures. MIA descriptors were analyzed by correlation ranking‐PCR and PLS methods. The predictive ability of the models was evaluated using cross‐validation and an external test set. The results showed that GA is a good method for variable selection using structural descriptors combined with the PLS model. Since MIA descriptors are 2D pixels of the chemical structures, they can differentiate between the (R) and (S) isomers of enantiomers better than the quantum mechanical and structural descriptors. Comparison between the different models indicated that the GA‐PLS and MIA‐PLS models were the best methods.

Modelling approaches for chiral chromatography on polysaccharide-based and macrocyclic antibiotic chiral selectors: A review

“…Therefore variable selection is often combined with the modelling approach, e.g. genetic algorithms (GA), stepwise regression, … [61,75].…”

“…Barfeii et al [75] made a QSRR approach to predict the retention times of 26 ethanol ester enantiomers on a CDMPC CSP in HPLC by using different types of descriptors: structural descriptors, electronic and quantum mechanical descriptors and multivariate image analysis (MIA) descriptors. MIA descriptors are calculated by transforming two-dimensional images of a molecular structure into pixels.…”

“…The previous studies [54,75,80] were carried out for structurally related compounds. As a consequence information was obtained about the functional groups responsible for the enantioseparation, considering certain compounds and its derivatives [78].…”

Intelligent Recommendation Systems Powered by Consensus Neural Networks: The Ultimate Solution for Finding Suitable Chiral Chromatographic Systems?

“…Alternatively, quantitative structure-enantioselective retention relationships (QSERR) have emerged as a useful sustainable strategy to select a suitable CSP/MP combination in chiral HPLC optimization processes . For polysaccharide-based CSPs, QSERR have been constructed using chemometric tools, which allow the prediction of enantioselective parameters. − …”

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