Towards a chromatographic similarity index to establish localised Quantitative Structure-Retention Relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography

Park, S. H.; Talebi, M.; Amos, R. I. J.; Tyteca, Eva; Haddad, P. R.; Szucs, R.; Pohl, C. A.; Dolan, J. W.

doi:10.1016/j.chroma.2017.02.054

Article (Scientific journals)

Towards a chromatographic similarity index to establish localised Quantitative Structure-Retention Relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography

Park, S. H.; Talebi, M.; Amos, R. I. J. et al.

2017 • In Journal of Chromatography. A

Peer Reviewed verified by ORBi

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https://hdl.handle.net/2268/208807

DOI
10.1016/j.chroma.2017.02.054

PubMed
28291517

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Keywords :

Genetic algorithm (GA); Ion chromatography; Linear solvent strength (LSS) model; Partial least squares (PLS); QSRR; Tanimoto similarity; Chemical compounds; Forecasting; Genetic algorithms; Ions; Least squares approximations; Genetic algorithm-partial least squares; Linear solvent strengths; Molecular descriptors; Partial least square (PLS); Quantitative structure-retention relationship; Retention time prediction; Chromatography

Abstract :

[en] Quantitative Structure-Retention Relationships (QSRR) are used to predict retention times of compounds based only on their chemical structures encoded by molecular descriptors. The main concern in QSRR modelling is to build models with high predictive power, allowing reliable retention prediction for the unknown compounds across the chromatographic space. With the aim of enhancing the prediction power of the models, in this work, our previously proposed QSRR modelling approach called "federation of local models" is extended in ion chromatography to predict retention times of unknown ions, where a local model for each target ion (unknown) is created using only structurally similar ions from the dataset. A Tanimoto similarity (TS) score was utilised as a measure of structural similarity and training sets were developed by including ions that were similar to the target ion, as defined by a threshold value. The prediction of retention parameters (a- and b-values) in the linear solvent strength (LSS) model in ion chromatography, log k = a - blog[eluent], allows the prediction of retention times under all eluent concentrations. The QSRR models for a- and b-values were developed by a genetic algorithm-partial least squares method using the retention data of inorganic and small organic anions and larger organic cations (molecular mass up to 507) on four Thermo Fisher Scientific columns (AS20, AS19, AS11HC and CS17). The corresponding predicted retention times were calculated by fitting the predicted a- and b-values of the models into the LSS model equation. The predicted retention times were also plotted against the experimental values to evaluate the goodness of fit and the predictive power of the models. The application of a TS threshold of 0.6 was found to successfully produce predictive and reliable QSRR models (Qext(F2) 2 >. 0.8 and Mean Absolute Error. <. 0.1), and hence accurate retention time predictions with an average Mean Absolute Error of 0.2. min. © 2017.

Disciplines :

Chemistry

Author, co-author :

Park, S. H.; Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, 7001, Australia

Talebi, M.; Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, 7001, Australia

Amos, R. I. J.; Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, 7001, Australia

Tyteca, Eva ; Université de Liège > Agronomie, Bio-ingénierie et Chimie (AgroBioChem) > Analyse, qual. et risques - Labo. de Chimie analytique

Haddad, P. R.; Australian Centre for Research on Separation Science (ACROSS), School of Physical Sciences-Chemistry, University of Tasmania, Private Bag 75, Hobart, 7001, Australia

Szucs, R.; Pfizer Global Research and Development, Sandwich, UK

Pohl, C. A.; Thermo Fisher Scientific, Sunnyvale, CA, USA

Dolan, J. W.; LC Resources Inc., 1795 NW Wallace Rd., McMinnville, OR, 97128, USA

Title :

Towards a chromatographic similarity index to establish localised Quantitative Structure-Retention Relationships for retention prediction. II Use of Tanimoto similarity index in ion chromatography

Publication date :

2017

Journal title :

Journal of Chromatography. A

ISSN :

0021-9673

eISSN :

1873-3778

Publisher :

Elsevier B.V.

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 30 March 2017

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