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Molecular modeling and prediction accuracy in Quantitative Structure-Retention Relationship calculations for chromatography

Citation

Amos, RIJ and Haddad, PR and Szucs, R and Dolan, JW and Pohl, CA, Molecular modeling and prediction accuracy in Quantitative Structure-Retention Relationship calculations for chromatography, Trends in Analytical Chemistry, 105 pp. 352-359. ISSN 0165-9936 (2018) [Refereed Article]

Copyright Statement

Copyright 2018 Elsevier B.V.

DOI: doi:10.1016/j.trac.2018.05.019

Abstract

Quantitative Structure-Retention Relationship (QSRR) methodology is a useful tool in chromatography of all kinds, allowing the prediction of analyte retention time and providing insight into the mechanisms of separation. The prediction of retention is useful in reducing method development time and identifying analytes in Non-Targeted Analysis. The varying methods used for geometry optimization, descriptor calculation, feature selection, and model generation in many different QSRR settings are investigated and compared. It is found that the method of geometry optimization and descriptor selection is of less importance than the chromatographic similarity of compounds in the training sets used for model building in order to reduce the error of the model.

Item Details

Item Type:Refereed Article
Keywords:QSRR, geometry optimization, descriptor generation, chiral descriptors, feature selection, retention prediction, similarity, Non-Targeted Analysis, chromatography, uantitative structure-retention relationships
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:Haddad, PR (Professor Paul Haddad)
ID Code:130491
Year Published:2018
Funding Support:Australian Research Council (LP120200700)
Web of Science® Times Cited:2
Deposited By:Chemistry
Deposited On:2019-01-29
Last Modified:2019-03-07
Downloads:0

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