Summary
A database of probe molecules and their reported ion-exchange chromatographic data was collected from the literature, after which an extensive set of both traditional and novel molecular property descriptors were computed for each probe molecule. A genetic algorithm/partial least squares (GA/PLS) approach was then used on the data to create a predictive Quantitative Structure-Retention Relationship (QSRR) model of retention where a subset of the original data was used for training and the remainder of the data as a test set. The utility of this model was demonstrated by using it to predict the chromatographic behavior of compounds not included in the training set. The results presented in this paper demonstrate the utility of modern QSRR modeling to predict chromatographic behavior in ion-exchange systems.
Similar content being viewed by others
References
Hansch, C.; Leo, A.Exploring QSAR, American Chemical Society,1995.
Hopfinger, A.J.J. Amer. Chem. Soc.,1980,102, 7196–7206.
Horwell, D.C.; Howson, W.; Higginbottom, M.; Naylor, D.; Ratcliffe, G.S.; Williams, S.Journal of Medicinal Chemistry,1995,38, 4454–4462.
Mazerska, Z.; Augustin, E.; Dziegielewski, J.; Cholody, M.W.; Konopa, J.Anti-Cancer Drug Design,1996,11, 73–88.
Skvortsova, M.I.; Baskin, I.I.; Slovokhotova, O.L.; Palyulin, V.A.; Zefirov, N.S.Journal of Chemical Information and Computer Sciences,1993,33, 630–634.
Seydel, J.K.; Trettin, D.; Cordes, H.P.Journal of Medicinal Chemistry,1980,23, 607–613.
Karcher, W.; Karabunarliev, S.Journal of Chemical Information and Computer Sciences,1996,36, 672–677.
Kamlet, M.J.; Abboud, J.-L.; Taft, R.W.Prog. Phys. Org. Chem.,1981,13, 485–630.
Kamlet, M.J.; Abboud, J.-L.; Abraham, M.H.; Taft, R.W.J. Org. Chem.,1983,48, 2877–87.
Hickey, J.P.; Passino-Reader, D.R.Environ. Sci. Technol. 1991,25, 1753–1760.
Carr, P.W.Microchem. Journal. 1993,48, 4–28.
Forgács, E.; Csartháti, T.Molecular basis of chromatographic separations, CRC Press, Boca Raton,1997.
Kaliszan, R.CRC Critical Reviews in Analytical Chemistry,16,4, 323–383.
Rahman, A.; Hoffman, N.E.J. Chrom. Sci. 1990,28, 157–61.
Lee, H.K.; Hoffman, N.E.J. Chrom. Sci. 1992,30, 98–105.
Lee, H.K.; Hoffman, N.E.J. Chrom. Sci. 1992,30, 415–21.
Law, B.; Weir, S.J. Chrom. A,1993,657, 17–24.
Law, B.J. Chrom. 1987,407, 1–18.
Shukla, A.A.; Bae, S.S.; Moore, J.A.; Cramer, S.M.J. Chrom. A 1998,827, 295–310.
Breneman, C.M.; Thompson, T.R.; Rhem, M.; Dunn, M.Computers Chem.,1995,19, 3, 161–179.
Bader, R.F.W.; Carroll, M.T.; Cheeseman, J.R.; Chang, C.J. Am. Chem. Soc. 1987,109, 7968–79.
Breneman, C.M.; Rhem, M.J. of Comp. Chem. 1997,18, 182–197.
Wold, S.; Ruhe, A.; Wold, H.; Dunn III, W.J.SIAM Journal of Scientific Statistical Computing 1984,5, 3, 735–743.
Haykin, S.Neural Networks—A Comprehensive Foundation, 2nd Edition, Prentice Hall, Upper Saddle River, NJ,1999.
Livingstone, D.Data analysis for chemists, Oxford Science Publications, Oxford1995.
Wold, S.Technometrics 1978,20, 397–405.
Breneman, C.M.; Rhem, M.; Thompson, T.; Dung, M.H.ACS Symp. Ser. 1994,569, 152–174.
Mazza, C.B.; Rege, K.; Breneman, C.M.; Dordick, J.S.; Cramer, S.M.Biotechnology and Bioengineering. in press.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Mazza, C.B., Whitehead, C.E., Breneman, C.M. et al. Predictive quantitative structure retention relationship models for ion-exchange chromatography. Chromatographia 56, 147–152 (2002). https://doi.org/10.1007/BF02493203
Received:
Revised:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF02493203