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Benchmarking of computational methods for creation of retention models in quantitative structure-retention relationships studies


Amos, RIJ and Tyteca, E and Talebi, M and Haddad, PR and Szucs, R and Dolan, JW and Pohl, CA, Benchmarking of computational methods for creation of retention models in quantitative structure-retention relationships studies, Journal of Chemical Information and Modeling, 57, (11) pp. 2754-2762. ISSN 1549-9596 (2017) [Refereed Article]

Copyright Statement

Copyright 2017 American Chemical Society

DOI: doi:10.1021/acs.jcim.7b00346


Quantitative structure-retention relationship (QSRR) models are powerful techniques for the prediction of retention times of analytes, where chromatographic retention parameters are correlated with molecular descriptors encoding chemical structures of analytes. Many QSRR models contain geometrical descriptors derived from the three-dimensional (3D) spatial coordinates of computationally predicted structures for the analytes. Therefore, it is sensible to calculate these structures correctly, as any error is likely to carry over to the resulting QSRR models. This study compares molecular modeling, semiempirical, and density functional methods (both B3LYP and M06) for structure optimization. Each of the calculations was performed in a vacuum, then repeated with solvent corrections for both acetonitrile and water. We also compared Natural Bond Orbital analysis with the Mulliken charge calculation method. The comparison of the examined computational methods for structure calculation shows that, possibly due to the error inherent in descriptor creation methods, a quick and inexpensive molecular modeling method of structure determination gives similar results to experiments where structures are optimized using an expensive and time-consuming level of computational theory. Also, for structures with low flexibility, vacuum or gas phase calculations are found to be as effective as those calculations with solvent corrections added.

Item Details

Item Type:Refereed Article
Keywords:QSRR modelling, molecular modelling, semi-empirical, DFT, benchmarking, 3D structures
Research Division:Chemical Sciences
Research Group:Analytical chemistry
Research Field:Separation science
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the chemical sciences
UTAS Author:Amos, RIJ (Dr Ruth Amos)
UTAS Author:Tyteca, E (Dr Eva Tyteca)
UTAS Author:Talebi, M (Dr Mohammad Talebi)
UTAS Author:Haddad, PR (Professor Paul Haddad)
ID Code:123580
Year Published:2017
Funding Support:Australian Research Council (LP120200700)
Web of Science® Times Cited:8
Deposited By:Austn Centre for Research in Separation Science
Deposited On:2018-01-11
Last Modified:2022-08-22

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