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Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines

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Summary

The accurate nonlinear model for predicting the tissue/blood partition coefficients (PC) of organic compounds in different tissues was firstly developed based on least-squares support vector machines (LS-SVM), as a novel machine learning technique, by using the compounds’ molecular descriptors calculated from the structure alone and the composition features of tissues. The heuristic method (HM) was used to select the appropriate molecular descriptors and build the linear model. The prediction result of the LS-SVM model is much better than that obtained by HM method and the prediction values of tissue/blood partition coefficients based on the LS-SVM model are in good agreement with the experimental values, which proved that nonlinear model can simulate the relationship between the structural descriptors, the tissue composition and the tissue/blood partition coefficients more accurately as well as LS-SVM was a powerful and promising tool in the prediction of the tissue/blood partition behaviour of compounds. Furthermore, this paper provided a new and effective method for predicting the tissue/blood partition behaviour of the compounds in the different tissues from their structures and gave some insight into structural features related to the partition process of the organic compounds in different tissues.

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References

  1. Boobis A., Gundert-Remy U., Kremers P., Macheras P., Pelkonen O., (2002). Eur. J. Pharm. Sci., 17 183

    Article  CAS  Google Scholar 

  2. Baláž Š., Luka¶cÏova V., (1999) Quant. Struct. Act. Relat., 18 361

    Article  Google Scholar 

  3. Zhang H.B., (2004) J. Pharm. Sci., 93 1595

    Article  CAS  Google Scholar 

  4. Zhang H.B., (2005) J. Chem. Inf. Comput. Sci., 45 121

    CAS  Google Scholar 

  5. Ekins S., Obach R.S., (2000) J. Pharmacol. Exp. Ther., 295 463

    CAS  Google Scholar 

  6. Cronin M.T.D., (2000) Curr. Opin. Drug Discovery Dev., 3 292

    CAS  Google Scholar 

  7. Yoshida F., Topliss J.G., (2000) J. Med. Chem., 43 2575

    Article  CAS  Google Scholar 

  8. Belousov A.I., Verzakov S.A., Von Frese J., (2002). Chemometr. Intell. Lab. Syst., 64 15

    Article  CAS  Google Scholar 

  9. Morris Colin W., Autret A., Boddy L., (2001) Ecol. Model., 146 57

    Article  Google Scholar 

  10. Burbidge R., Trotter M., Buxton B., Holden S., (2001) Comput. Chem.. 26 5

    Article  CAS  Google Scholar 

  11. Liu H.X., Zhang R.S., Yao X.J., Liu M.C., Hu Z.D., Fan B.T., (2003) J. Chem. InfComput. Sci., 43 1288

    Article  CAS  Google Scholar 

  12. Liu H.X., Zhang R.S.,Yao X.J., Liu M.C., Hu Z.D., Fan B.T., (2004) J. Chem. Inf. Comput. Sci., 44 161

    Article  CAS  Google Scholar 

  13. Xue C.X., Zhang R.S., Liu H.X., Yao X.J., Liu M.C., Hu Z.D., Fan B.T., (2004) J. Chem. Inf. Comput. Sci., 44 669

    Article  CAS  Google Scholar 

  14. Liu H.X., Zhang R.S., Yao X.J., Liu M.C., Hu Z.D., Fan B.T., (2004) J. Comput. Aid. Mol. Des. 18 389

    Article  CAS  Google Scholar 

  15. Yao X.J., Panaye A., Doucet J.P., Zhang R.S., Chen H.F., Fan B.T., Liu M.C., Hu Z.D., (2004) J. Chem. Inf. Comput. Sci.., 44 1257

    Article  CAS  Google Scholar 

  16. Liu H.X., Xue C.X., Zhang R.S., Yao X.J., Liu M.C., Hu Z.D., Fan B.T., (2004) J. Chem. Inf. Comput. Sci., 44 1979

    Article  CAS  Google Scholar 

  17. Burges C.J.C., (1998) Data Min. Know. Disc. 2 1

    Google Scholar 

  18. Vapnik V., 1982 Estimation of Dependences Based on Empirical Data, Springer, Berlin

    Google Scholar 

  19. Suykens J.A.K., Vandewalle J., (1999) Neural Process. Lett. 9 293

    Article  Google Scholar 

  20. Katritzky, A.R., Lobanov, V.S. and Karelson, M., Comprehensive Descriptors for Structural and Statistical Analysis, Reference Manual. Version 2.0, 1994

  21. Katritzky A.R., Lobanov V.S., Karelson M., (1995) Chem. Soc. Rev.. 24 279

    Article  CAS  Google Scholar 

  22. Oblak M., Randic M., Solmajer T., (2000) ?J. Chem. Inf. Comput. Sci.. 40 994

    Article  CAS  Google Scholar 

  23. Katritzky A.R., Tatham D.B., (2001) J. Chem. Inf. Comput. Sci., 41 1162

    Article  CAS  Google Scholar 

  24. HyperChem. 4.0, Hypercube, 1994

  25. Stewart J.P.P., MOPAC 6.0, 1989 Quantum Chemistry Program Exchange; QCPE, No. 455, Indiana University, Bloomington, IN

    Google Scholar 

  26. Katritzky A.R., Petrukhin R., Jain R., Karelson M., (2001) J. Chem. Inf. Comput. Sci.. 41 1521

    Article  CAS  Google Scholar 

  27. Cortes C., Vapnik V., (1995) Machine Learn., 20 273

    Google Scholar 

  28. Vapnik V., 1998 Statistical Learning Theory, Wiley: New York

    Google Scholar 

  29. Schölkopf B., Burges C., Smola A., 1999 Advances in Kernel Methods – Support Vector Learning, MIT Press: Cambridge, MA

    Google Scholar 

  30. Cristianini N., Shawe-Taylor J., 2000 An Introduction to Support Vector Machines, Cambridge University Press: Cambridge, UK

    Google Scholar 

  31. URL: http://www.kernel-machines.org/,Dec, 2004

  32. Pelckmans, K., Suykens, J.A.K., Van Gestel, T., De Brabanter, D., Lukas, L., Hamers, B., De Moor, B. and Vandewalle, J., LS-SVMlab: a Matlab/C Toolbox for Least Squares Support Vector Machines. Internal Report 02-44, ESATSISTA; K.U. Leuven, Leuven, 2002

  33. Stanton D.T., Egolf L.M., Jurs P.C.J., (1992) Chem. Inf. Comput. Sci.. 32 306

    Article  CAS  Google Scholar 

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Authors and Affiliations

  1. Department of Chemistry, Lanzhou University, 730000, Lanzhou, China

    H.X. Liu, X.J. Yao, M.C. Liu & Z.D. Hu

  2. Department of Computer Science, Lanzhou University, 730000, Lanzhou, China

    R.S. Zhang

  3. Université Paris 7-Denis Diderot, ITODYS 1 rue Guy de la Brosse, 75005, Paris, France

    B.T. Fan

Authors
  1. H.X. Liu
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  2. X.J. Yao
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  3. R.S. Zhang
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  4. M.C. Liu
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  5. Z.D. Hu
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  6. B.T. Fan
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Corresponding author

Correspondence to Z.D. Hu.

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Liu, H., Yao, X., Zhang, R. et al. Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines. J Comput Aided Mol Des 19, 499–508 (2005). https://doi.org/10.1007/s10822-005-9003-5

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  • DOI: https://doi.org/10.1007/s10822-005-9003-5

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