EVA/PLS versus autocorrelation/neural network estimation of partition coefficients

Abstract

The performances of a log P model designed from EVA descriptors based on theoretically derived normal coordinate frequencies and using the classical PLS analysis as statistical engine were compared to those provided by a neural network model employing various autocorrelation vectors for describing the molecules. The superiority of the latter is clearly demonstrated for simulating the lipophilicity of simple chemical structures.

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References

  1. 1.

    Berthelot, M., Ann. Chim. Phys., 26 (1872) 408.

    Google Scholar 

  2. 2.

    Overton, E., Studien über die Narkose zugleich ein Beitrag zur allgemeinen Pharmakologie, Verlag von Gustav Fischer, Jena, 1901.

    Google Scholar 

  3. 3.

    Hansch, C. and Leo, A., Exploring QSAR. Fundamentals and Applications in Chemistry and Biology, ACS Professional Reference Book, American Chemical Society, Washington, DC, 1995.

    Google Scholar 

  4. 4.

    Sangster, J., Octanol—Water Partition Coefficients: Fundamentals and Physical Chemistry, Wiley, Chichester, 1997.

    Google Scholar 

  5. 5.

    Reinhard, M. and Drefahl, A., Handbook for Estimating Physicochemical Properties of Organic Compounds, Wiley, New York, NY, 1999.

    Google Scholar 

  6. 6.

    Rekker, R.F. and Mannhold, R., Calculation of Drug Lipophilicity. The Hydrophobic Fragmental Constant Approach, VCH, Weinheim, 1992.

    Google Scholar 

  7. 7.

    Klopman, G., Li, J.-Y., Wang, S. and Dimayuga, M., J. Chem. Inf. Comput. Sci., 34 (1994) 752.

    CAS  Article  Google Scholar 

  8. 8.

    Meylan, W.M. and Howard, P.H., J. Pharm. Sci., 84 (1995) 83.

    PubMed  CAS  Google Scholar 

  9. 9.

    Klopman, G. and Iroff, L.D., J. Comput. Chem., 2 (1981) 157.

    CAS  Article  Google Scholar 

  10. 10.

    Basak, S.C., Gute, B.D. and Grunwald, G.D., J. Chem. Inf. Comput. Sci., 36 (1996) 1054.

    CAS  Article  Google Scholar 

  11. 11.

    Devillers, J., Domine, D. and Karcher, W., SAR QSAR Environ. Res., 3 (1995) 301.

    CAS  Google Scholar 

  12. 12.

    Bodor, N. and Huang, M.J., J. Pharm. Sci., 81 (1992) 272.

    PubMed  CAS  Google Scholar 

  13. 13.

    Bodor, N., Huang, M.J. and Harget, A., J. Mol. Struct. (THEOCHEM), 309 (1994) 259.

    Article  Google Scholar 

  14. 14.

    Domine, D., Devillers, J. and Karcher, W., In Devillers, J. (Ed.) Neural Networks in QSAR and Drug Design, Academic Press, London, 1996, pp. 47–63.

    Google Scholar 

  15. 15.

    Schaper, K.J. and Samitier, M.L.R., Quant. Struct.—Act. Relat., 16 (1997) 224.

    CAS  Google Scholar 

  16. 16.

    Devillers, J., Domine, D., Guillon, C., Bintein, S. and Karcher, W., SAR QSAR Environ. Res., 7 (1997) 151.

    CAS  Google Scholar 

  17. 17.

    Devillers, J. and Domine, D., SAR QSAR Environ. Res., 7 (1997) 195.

    CAS  Google Scholar 

  18. 18.

    Devillers, J., Domine, D. and Guillon, C., Eur. J. Med. Chem., 33 (1998) 659.

    CAS  Article  Google Scholar 

  19. 19.

    Devillers, J., Domine, D., Guillon, C. and Karcher, W., J. Pharm. Sci., 87 (1998) 1086.

    PubMed  CAS  Article  Google Scholar 

  20. 20.

    Devillers, J., Analusis, 27 (1999) 23.

    CAS  Article  Google Scholar 

  21. 21.

    Devillers, J., SAR QSAR Environ. Res., 10 (1999) 249.

    CAS  Google Scholar 

  22. 22.

    Ferguson, A.M., Heritage, T., Jonathon, P., Pack, S.E., Phillips, L., Rogan, J. and Snaith, P.J., J. Comput.-Aided Mol. Design, 11 (1997) 143.

    CAS  Article  Google Scholar 

  23. 23.

    Ginn, C.M.R., Turner, D.B., Willett, P., Ferguson, A.M. and Heritage, T.W., J. Chem. Inf. Comput. Sci., 37 (1997) 23.

    CAS  Article  Google Scholar 

  24. 24.

    Tuppurainen, K., SAR QSAR Environ. Res., 10 (1999) 39.

    CAS  Google Scholar 

  25. 25.

    Moreau, G. and Broto, P., Nouv. J. Chim., 4 (1980) 359.

    CAS  Google Scholar 

  26. 26.

    Moreau, G. and Broto, P., Nouv. J. Chim., 4 (1980) 757.

    CAS  Google Scholar 

  27. 27.

    Devillers, J., In Devillers, J. and Balaban, A.T. (Eds.) Topological Indices and Related Descriptors in QSAR and QSPR, Gordon and Breach, The Netherlands, 1999, pp. 595–612.

    Google Scholar 

  28. 28.

    Devillers, J., Domine, D. and Chastrette, M., In Proceedings of QSAR92, July 19-23, 1992, Duluth, MN, U.S.A., p. 12.

  29. 29.

    Hansen, L.K. and Salamon, P., IEEE Trans. Pattern Anal.Machine Intell., 12 (1990) 993.

    Article  Google Scholar 

  30. 30.

    The model has been implemented in AUTOLOGPTM (v. 4.0), a user-friendly software program running on IBMTM and compatible PC underWindowsTM 3.1 andWindowsTM 95.

  31. 31.

    Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C., Multivariate Data Analysis with Readings, Macmillan, New York, NY, 1992.

    Google Scholar 

  32. 32.

    Hansch, C., Leo, A. and Hoekman, D., Exploring QSAR. Hydrophobic, Electronic, and Steric Constants, ACS Professional Reference Book, American Chemical Society, Washington, DC, 1995.

    Google Scholar 

  33. 33.

    Hansch, C., In Devillers, J. (Ed.) Comparative QSAR, Taylor and Francis, Philadelphia, PA, 1998, pp. 285–368.

    Google Scholar 

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Devillers, J. EVA/PLS versus autocorrelation/neural network estimation of partition coefficients. Perspectives in Drug Discovery and Design 19, 117–131 (2000). https://doi.org/10.1023/A:1008771606841

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  • autocorrelation method
  • EVA descriptors
  • neural network
  • PLS