Article (Scientific journals)
Towards a chromatographic similarity index to establish localized quantitative structure-retention models for retention prediction: Use of retention factor ratio
Tyteca, Eva; Talebi, M.; Amos, R. et al.
2017In Journal of Chromatography. A, 1486, p. 50-58
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Keywords :
Automation; Quantitative Structure-Retention Relationships; Similarity searching; Chemical compounds; Chromatographic analysis; Computational chemistry; Errors; Forecasting; Molecular graphics; Statistical tests; Structure (composition); Average prediction error; Chromatographic methods; Chromatographic systems; Quantitative structure-activity relationships; Quantitative structure-retention relationship; Quantitative structures; Structural similarity indices; Chromatography
Abstract :
[en] Quantitative Structure-Retention Relationships (QSRR) have the potential to speed up the screening phase of chromatographic method development as the initial exploratory experiments are replaced by prediction of analyte retention based solely on the structure of the molecule. The present study offers further proof-of-concept of localized QSRR modelling, in which the retention of any given compound is predicted using only the most chromatographically similar compounds in the available dataset. To this end, each compound in the dataset was sequentially removed from the database and individually utilized as a test analyte. In this study, we propose the retention factor k as the most relevant chromatographic similarity measure and compare it with the Tanimoto index, the most popular similarity measure based on chemical structure. Prediction error was reduced by up to 8 fold when QSRR was based only on chromatographically similar compounds rather than using the entire dataset. The study therefore shows that the design of a practically useful structural similarity index should select the same compounds in the dataset as does the k-similarity filter in order to establish accurate predictive localized QSRR models. While low average prediction errors (Mean Absolute Error (MAE) < 0.5 min) and slopes of the regression lines through the origin close to 1.00 were obtained using k-similarity searching, the use of the structural Tanimoto similarity index, considered as the gold standard in Quantitative Structure-Activity Relationships (QSAR) studies, generally resulted in much higher prediction errors (MAE > 1 min) and significant deviations from the reference slope of 1.0. The Tanomoto similarity index therefore appears to have limited general utility in QSRR studies. Future studies therefore aim at designing a more appropriate chromatographic similarity index that can then be applied for unknown compounds (that is, compounds which have not been tested previously on the chromatographic system used, but for which the chemical structures are known). © 2016
Disciplines :
Chemistry
Author, co-author :
Tyteca, Eva ;  Université de Liège > Agronomie, Bio-ingénierie et Chimie (AgroBioChem) > Analyse, qual. et risques - Labo. de Chimie analytique
Talebi, M.;  Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, Australia
Amos, R.;  Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, Australia
Park, S. H.;  Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, Australia
Taraji, M.;  Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, Australia
Wen, Y.;  Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, Australia
Szucs, R.;  Pfizer Global Research and Development, Sandwich, United Kingdom
Pohl, C. A.;  Thermo Fisher Scientific, Sunnyvale, CA, United States
Dolan, J. W.;  LC Resources Inc., 1795 NW Wallace Rd, McMinnville, OR, United States
Haddad, P. R.;  Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, Australia
Title :
Towards a chromatographic similarity index to establish localized quantitative structure-retention models for retention prediction: Use of retention factor ratio
Publication date :
2017
Journal title :
Journal of Chromatography. A
ISSN :
0021-9673
eISSN :
1873-3778
Publisher :
Elsevier B.V.
Volume :
1486
Pages :
50-58
Peer reviewed :
Peer Reviewed verified by ORBi
Name of the research project :
LP120200700
Funders :
ARC - Australian Research Council [AU]
FWO - Fonds Wetenschappelijk Onderzoek Vlaanderen [BE]
Available on ORBi :
since 30 March 2017

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