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Journal of Computer-Aided Molecular Design

, Volume 15, Issue 8, pp 741–752 | Cite as

Simultaneous prediction of aqueous solubility and octanol/water partition coefficient based on descriptors derived from molecular structure

  • David J. Livingstone
  • Martyn G. Ford
  • Jarmo J. Huuskonen
  • David W. Salt
Article

Abstract

It has been shown that water solubility and octanol/water partition coefficient for a large diverse set of compounds can be predicted simultaneously using molecular descriptors derived solely from a two dimensional representation of molecular structure. These properties have been modelled using multiple linear regression, artificial neural networks and a statistical method known as canonical correlation analysis. The neural networks give slightly better models both in terms of fitting and prediction presumably due to the fact that they include non-linear terms. The statistical methods, on the other hand, provide information concerning the explanation of variance and allow easy interrogation of the models. Models were fitted using a training set of 552 compounds, a validation set and test set each containing 68 molecules and two separate literature test sets for solubility and partition.

canonical correlation electrotopological descriptors log P log S neural networks regression analysis 

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References

  1. 1.
    Livingstone, D.J., J.Chem. Inf. Comput. Sci., 40 (2000) 195.Google Scholar
  2. 2.
    Nirmalakhandan, N.N. and Speece, R.E., Environ. Sci. Technol., 22 (1988) 328.Google Scholar
  3. 3.
    Bodor, N. and Huang, M-J., J. Pharm. Sci., 81 (1992) 954.Google Scholar
  4. 4.
    Patil, G.S., J. Hazard. Mater., 36 (1994) 35.Google Scholar
  5. 5.
    Sutter, J.M. and Jurs, P.C., J. Chem. Inf. Comput. Sci., 36 (1996) 100.Google Scholar
  6. 6.
    Huibers, P.D.T. and Katritzky, A.R., J.Chem.Inf.Comput.Sci., 38 (1998) 283.Google Scholar
  7. 7.
    Mitchell, B.E. and Jurs, P.C., J. Chem. Inf. Comput. Sci., 38 (1998) 489.Google Scholar
  8. 8.
    Rekker, R. E., Hydrophobic Fragment Constant; Elsevier: New York, 1977.Google Scholar
  9. 9.
    Hansch, C. and Leo, A., Substituent Constants for Correlation Analysis in Chemistry and Biology; Wiley: New York, 1979.Google Scholar
  10. 10.
    Klopman, G. and Iroff, L., J. Comput. Chem., 2 (1981) 157.Google Scholar
  11. 11.
    Bodor, N. and Huang, M-J., J. Pharm. Sci., 81 (1992) 272.Google Scholar
  12. 12.
    Leo, A., Chem. Rev., 93 (1993) 1281.Google Scholar
  13. 13.
    Klopman, G.; Li, J-Y.; Wang, S. and Dimayuga, M., J. Chem. Inf. Comput. Sci., 34 (1994) 752.Google Scholar
  14. 14.
    Meylan, W. M. and Howard, P. H., J. Pharm. Sci., 84 (1995) 83.Google Scholar
  15. 15.
    Wang, R.; Fu, Y. and Lai, L., J. Chem. Inf. Comput. Sci. 37 (1997) 615.Google Scholar
  16. 16.
    Haeberlin, M. and Brinck, T., J. Chem. Soc. Perkin Trans. 2, (1997) 289.Google Scholar
  17. 17.
    Bodor, N. and Buchwald, P., J. Phys. Chem., 101 (1997) 3404.Google Scholar
  18. 18.
    Buchwald, P. and Bodor, N., Current. Med. Chem., 5 (1998) 353.Google Scholar
  19. 19.
    Bodor, N. and Huang, M-J., J.Am.Chem.Soc., 113 (1991) 9480.Google Scholar
  20. 20.
    Breindl, A.; Beck, N.; Clark, T. and Glen, R. C., J. Mol. Model., 3 (1997) 142.Google Scholar
  21. 21.
    Schaper, K.-J. and Samitier, M. L. R., Quant. Struct.-Act. Relat. 16 (1997) 224.Google Scholar
  22. 22.
    Devillers, J.; Domine, D. and Guillon, C., Eur. J. Med. Chem. 33 (1998) 659.Google Scholar
  23. 23.
    Huuskonen, J. J.; Salo, M. and Taskinen, J., J. Pharm. Sci. 86 (1997) 450.Google Scholar
  24. 24.
    Huuskonen, J. J.; Salo, M. and Taskinen, J., J. Chem. Inf. Comput. Sci., 38 (1998) 450.Google Scholar
  25. 25.
    Huuskonen, J.J., Rantanen, J. and Livingstone, D., Eur. J. Med. Chem., 35 (2000) 1081.Google Scholar
  26. 26.
    Huuskonen, J. J., J. Chem. Inf. Comput. Sci., 40 (2000) 773.Google Scholar
  27. 27.
    Huuskonen, J. J.; Villa, A. E. P. and Tetko, I. V., J. Pharm. Sci. 88 (1999) 229.Google Scholar
  28. 28.
    Huuskonen, J. J.; Livingstone, D.J. and Tetko, I. V., J. Chem. Inf. Comput. Sci., 40 (2000) 947.Google Scholar
  29. 29.
    Hall, L.H. and Kier, L.B., J. Chem. Inf. Comput. Sci. 35 (1995) 1039.Google Scholar
  30. 30.
    Szydlo, R.M., Ford M.G., Greenwood, R.G. and Salt, D.W., In Dearden, J.C. (ed.), Quantitative Approaches to Drug Design, Elsevier, Amsterdam, 1983, pp. 203-14.Google Scholar
  31. 31.
    Laass, W., In Seydel, J.K. (ed.), QSAR and Strategies in the Design of Bioactive Compounds, VCH, Weinheim, 1985, pp. 285-289.Google Scholar
  32. 32.
    Bordas, B., In Seydel, J.K. (ed.), QSAR and Strategies in the Design of Bioactive Compounds, VCH, Weinheim, 1985, pp. 389-392.Google Scholar
  33. 33.
    Ford, M.G. and Salt, D.W., In van de Waterbeemd, H. (ed.), Chemometric Methods in Molecular Design, VCH, Weinheim, 1995, pp. 265-282.Google Scholar
  34. 34.
    Yalkowsky, S.H. and Dannenfelser, R-M. (ed.), AQUASOL dATAbASE of Aqueous Solubility, College of Pharmacy, University of Arizona, Arizona, USA, 1990.Google Scholar
  35. 35.
    Biobyte, Corp., 201W. Fourth St., Suite #204, Claremont, CA 91711, USA.Google Scholar
  36. 36.
    Moriguchi, I., Hirono, S., Nakagome, I. And Hirono, H. Chem. Pharm. Bull., 42 (1994) 976-978.Google Scholar
  37. 37.
    Yalkowsky, S., Chemosphere 26 (1993) 1239-1261.Google Scholar
  38. 38.
    BMDP Statistical Software Manual, Dixon, W.J. (ed.) University of California Press, 1990.Google Scholar
  39. 39.
    Tetko, I.V., Villa, A.E.P. and Livingstone, D.J., J. Chem. Inf. Comput. Sci, 36 (1996) 794.Google Scholar
  40. 40.
    Wikel, J.H. and Dow, E.R., BioMed. Chem. Lett. 3 (1993) 645.Google Scholar
  41. 41.
    Kühne, R., Ebert, R.-U., Kleint, F., Scmidt, G. and Schüürmann, G. Chemosphere 30 (1995) 2061Google Scholar
  42. 42.
    Klopman, G., Wang, S., Balthasar, D.M., J.Chem.Inf.Comput.Sci. 32 (1992) 474Google Scholar
  43. 43.
    Stewart, D. K. and Love, W. A. Psychol. Bull., 70 (1968) 160.Google Scholar
  44. 44.
    Roadknight, C.M., Palmer-Brown, D. and Mills, G.E., In Liu, X., Cohen, P. and Berthold, M. (eds), Advances in Intelligent Data Analysis, Springer-Verlag, Berlin, 1997, pp. 337-346.Google Scholar

Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  • David J. Livingstone
    • 1
  • Martyn G. Ford
    • 2
  • Jarmo J. Huuskonen
    • 3
  • David W. Salt
    • 4
  1. 1.ChemQuestSandown, Isle of WightUK
  2. 2.Centre for Molecular DesignUniversity of PortsmouthPortsmouth, HantsUK
  3. 3.Division of Pharmaceutical Chemistry, Department of PharmacyUniversity of HelsinkiFIN-00014
  4. 4.School of Computer Science & MathematicsUniversity of PortsmouthPortsmouth, HantsUK

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