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The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine

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Abstract

Support vector machine (SVM), as a novel machine learning technique, was used for the prediction of the human oral absorption for a large and diverse data set using the five descriptors calculated from the molecular structure alone. The molecular descriptors were selected by heuristic method (HM) implemented in CODESSA. At the same time, in order to show the influence of different molecular descriptors on absorption and to well understand the absorption mechanism, HM was used to build several multivariable linear models using different numbers of molecular descriptors. Both the linear and non-linear model can give satisfactory prediction results: the square of correlation coefficient R2 was 0.78 and 0.86 for the training set, and 0.70 and 0.73 for the test set respectively. In addition, this paper provides a new and effective method for predicting the absorption of the drugs from their structures and gives some insight into structural features related to the absorption of the drugs.

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References

  1. C.Y. Yang A.H. Dantzig C. Pidgeon (1999) Pharm. Res. 16 1331 Occurrence Handle1:CAS:528:DyaK1MXmtVKktLs%3D Occurrence Handle10496647

    CAS  PubMed  Google Scholar 

  2. Y.H. Zhao J.A.M.H. Le A. Hersey P.J. Eddershaw C.N. Luscombe D. Boutina G. Beck B. Sherborne I.J. Cooper A.J. Platts (2001) Pharm. Sci. 90 749 Occurrence Handle10.1002/jps.1031 Occurrence Handle1:CAS:528:DC%2BD3MXksFequ7g%3D

    Article  CAS  Google Scholar 

  3. X.J. Yao M.C. Liu X.Y. Zhang Z.D. Hu B.T. Fan (2002) Anal. Chim. Acta 462 101 Occurrence Handle10.1016/S0003-2670(02)00273-8 Occurrence Handle1:CAS:528:DC%2BD38XktlKjsL0%3D

    Article  CAS  Google Scholar 

  4. O.A. Raevsky V.I. Fetisov E.P. Trepalina J.W. McFarland K.J. Schaper (2000) Quant. Struct.-Act. Relat. 19 366 Occurrence Handle10.1002/1521-3838(200010)19:4<366::AID-QSAR366>3.0.CO;2-E Occurrence Handle1:CAS:528:DC%2BD3cXnslGrtb8%3D

    Article  CAS  Google Scholar 

  5. D.E. Clark (1999) J. Pharm. Sci. 88 807 Occurrence Handle10.1021/js9804011 Occurrence Handle1:CAS:528:DyaK1MXjvVCqtbw%3D Occurrence Handle10430547

    Article  CAS  PubMed  Google Scholar 

  6. M.D. Wessel P.C. Jurs J.W. Tolan S.M. Muskal (1998) J. Chem. Inf.Comput. Sci. 38 726 Occurrence Handle10.1021/ci980029a Occurrence Handle1:CAS:528:DyaK1cXktFKrtLk%3D Occurrence Handle9691477

    Article  CAS  PubMed  Google Scholar 

  7. Y.H. Zhao M.H. Abraham J. Le A. Hersey C.N. Luscombe G. Beck B. Sherborne I. Cooper (2002) Pharm. Res. 19 1446 Occurrence Handle10.1023/A:1020444330011 Occurrence Handle1:CAS:528:DC%2BD38XnsFSgsb8%3D Occurrence Handle12425461

    Article  CAS  PubMed  Google Scholar 

  8. S. Tavelin J. Taipalensuu L. Söderberg R. Morrison S. Chong P. Artursson (2003) Pharm. Res. 20 397 Occurrence Handle10.1023/A:1022699920043 Occurrence Handle1:CAS:528:DC%2BD3sXhslGrsbc%3D Occurrence Handle12669959

    Article  CAS  PubMed  Google Scholar 

  9. J.K. Wegner H. Fröhlich A. Zell (2004) J. Chem. Inf.Comput. Sci. 44 931 Occurrence Handle10.1021/ci034233w Occurrence Handle1:CAS:528:DC%2BD2cXht1Whsr4%3D Occurrence Handle15154759

    Article  CAS  PubMed  Google Scholar 

  10. W.J. Egan K.M. Merz J.J. Baldwin (2000) J. Med. Chem. 43 3867 Occurrence Handle10.1021/jm000292e Occurrence Handle1:CAS:528:DC%2BD3cXmsFeitLk%3D Occurrence Handle11052792

    Article  CAS  PubMed  Google Scholar 

  11. P. Stenberg U. Norinder L. Kristina P. Artursson (2001) J. Med. Chem. 44 1927 Occurrence Handle10.1021/jm001101a Occurrence Handle1:CAS:528:DC%2BD3MXjsVOltLg%3D Occurrence Handle11384238

    Article  CAS  PubMed  Google Scholar 

  12. C.A.S. Bergström M. Strafford L. Lazorova A. Avdeef Kristina Luthman Per. Artursson (2003) J. Med. Chem. 46 558 Occurrence Handle10.1021/jm020986i Occurrence Handle12570377

    Article  PubMed  Google Scholar 

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

  14. A.R. Katritzky V.S. Lobanov M. Karelson (1995) Chem. Soc. Rev. 24 279 Occurrence Handle10.1039/cs9952400279 Occurrence Handle1:CAS:528:DyaK2MXosFOgurc%3D

    Article  CAS  Google Scholar 

  15. M. Oblak M. Randic T. Solmajer (2000) J. Chem. Inf.Comput. Sci. 40 994 Occurrence Handle10.1021/ci000001a Occurrence Handle1:CAS:528:DC%2BD3cXivFeqsbk%3D Occurrence Handle10955529

    Article  CAS  PubMed  Google Scholar 

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

    Google Scholar 

  17. R. Burbidge M. Trotter B. Buxton S. Holden (2001) Comput. Chem. 26 14 Occurrence Handle10.1016/S0097-8485(01)00094-8

    Article  Google Scholar 

  18. D.T. Manallack D.J. Livingstone (1999) Eur. J. Med. Chem. 34 95 Occurrence Handle10.1016/S0223-5234(99)80052-X

    Article  Google Scholar 

  19. D. Goldberg (1989) Genetic Algorithms in Search, Optimization and Machine Learning Addison-Wesley Reading, MA

    Google Scholar 

  20. L. Bao Z.R. Sun (2002) FEBS Lett. 521 109 Occurrence Handle10.1016/S0014-5793(02)02835-1 Occurrence Handle1:CAS:528:DC%2BD38XksVahsr8%3D Occurrence Handle12067737

    Article  CAS  PubMed  Google Scholar 

  21. A.I. Belousov S.A. Verzakov J. Von Frese (2002) Chemometr. Intell. Lab. Syst. 64 15 Occurrence Handle10.1016/S0169-7439(02)00046-1 Occurrence Handle1:CAS:528:DC%2BD38XntVamsbk%3D

    Article  CAS  Google Scholar 

  22. Y.D. Cai X.J. Liu X.B. Xu K.C. Chou (2002) Comb. Chem. 26 293 Occurrence Handle10.1016/S0097-8485(01)00113-9 Occurrence Handle1:CAS:528:DC%2BD38XmtlWrsA%3D%3D

    Article  CAS  Google Scholar 

  23. C.W. Morris A. Autret L. Boddy (2001) Ecol. Model. 146 57 Occurrence Handle10.1016/S0304-3800(01)00296-4

    Article  Google Scholar 

  24. S.C.M. Minghu N. BrenemanJ.B Sukumar P.B. K C. Steven T. Nihal (2002) J. Chem. Inf. Comput. Sci. 42 1347 Occurrence Handle10.1021/ci025580t Occurrence Handle12444731

    Article  PubMed  Google Scholar 

  25. H.X. Liu R.S. Zhang F. Luan X.J. Yao M.C. Liu Z.D. Hu B.T. Fan (2003) J. Chem. Inf. Comput. Sci. 43 900 Occurrence Handle10.1021/ci0256438 Occurrence Handle1:CAS:528:DC%2BD3sXjtFKisLY%3D Occurrence Handle12767148

    Article  CAS  PubMed  Google Scholar 

  26. H.X. Liu R.S. Zhang X.J. Yao M.C. Liu Z.D. Hu B.T. Fan (2003) J. Chem. Inf. Comput. Sci. 43 1288 Occurrence Handle10.1021/ci0340355 Occurrence Handle1:CAS:528:DC%2BD3sXjvVGmsr4%3D Occurrence Handle12870922

    Article  CAS  PubMed  Google Scholar 

  27. H.X. Liu R.S. Zhang X.J. Yao M.C. Liu Z.D. Hu B.T. Fan (2004) J. Chem. Inf. Comput. Sci. 44 161 Occurrence Handle10.1021/ci034173u Occurrence Handle1:CAS:528:DC%2BD3sXptlOksbk%3D Occurrence Handle14741023

    Article  CAS  PubMed  Google Scholar 

  28. C.X. Xue R.S. Zhang H.X. Liu X.J. Yao M.C. Liu Z.D. Hu B.T. Fan (2004) J. Chem. Inf. Comput. Sci. 44 669 Occurrence Handle10.1021/ci034248u Occurrence Handle1:CAS:528:DC%2BD2cXosl2jsw%3D%3D Occurrence Handle15032549

    Article  CAS  PubMed  Google Scholar 

  29. A.R. Katritzky R. Petrukhin R. Jain M. Karelson (2001) J. Chem. Inf. Comput. Sci. 41 1521 Occurrence Handle10.1021/ci010043e Occurrence Handle1:CAS:528:DC%2BD3MXntVCgsbo%3D Occurrence Handle11749578

    Article  CAS  PubMed  Google Scholar 

  30. C. Cortes V. Vapnik (1995) Machine Learning 20 273

    Google Scholar 

  31. L. Zhang W.D. Zhou L.C. Jiao (2002) J. Infrared Millimeter Waves 21 119

    Google Scholar 

  32. C.H.Q. Ding I. Dubchak (2001) Bioinformatics 17 349 Occurrence Handle10.1093/bioinformatics/17.4.349 Occurrence Handle1:CAS:528:DC%2BD3MXjvVegurg%3D Occurrence Handle11301304

    Article  CAS  PubMed  Google Scholar 

  33. R. Karchin K. Karplus D. Haussler (2002) Bioinformatics 18 147 Occurrence Handle10.1093/bioinformatics/18.1.147 Occurrence Handle1:CAS:528:DC%2BD38Xhs1elu7w%3D Occurrence Handle11836223

    Article  CAS  PubMed  Google Scholar 

  34. Y.D. Cai X.J. Liu X.B. Xu K.C. Chou (2002) J. Comput. Chem. 23 267 Occurrence Handle10.1002/jcc.10017 Occurrence Handle1:CAS:528:DC%2BD38XksFShuw%3D%3D Occurrence Handle11924738

    Article  CAS  PubMed  Google Scholar 

  35. W.J. Wang Z.B. Xu W.Z. Lu X.Y. Zhang (2003) Neurocomputing 55 643 Occurrence Handle10.1016/S0925-2312(02)00632-X

    Article  Google Scholar 

  36. V. Vapnik (1998) Statistical Learning Theory Wiley New York

    Google Scholar 

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

    Google Scholar 

  38. N. Cristianini J. Shawe-Taylor (2000) An Introduction to Support Vector Machines Cambridge University Press Cambridge, UK

    Google Scholar 

  39. W.K. Sietsema (1989) Int. J. Clin. Pharmacol. Ther. Toxicol. 27 179 Occurrence Handle1:CAS:528:DyaL1MXktVSjtr8%3D Occurrence Handle2654032

    CAS  PubMed  Google Scholar 

  40. R. Bosque J. Sales (2003) J. Chem. Inf.Comput. Sci. 43 637 Occurrence Handle10.1021/ci025632e Occurrence Handle1:CAS:528:DC%2BD3sXlvV2guw%3D%3D Occurrence Handle12653532

    Article  CAS  PubMed  Google Scholar 

  41. A.R. Katritzky A.A. Oliferenko P.V. Oliferenko R. Petrukhin D.B. Tatham (2003) J. Chem. Inf.Comput. Sci. 43 1794 Occurrence Handle10.1021/ci034120c Occurrence Handle1:CAS:528:DC%2BD3sXns1Oht7g%3D Occurrence Handle14632425

    Article  CAS  PubMed  Google Scholar 

  42. B. Balon B.U. Riebesehl B.W. Müller (1999) Pharm. Res. 16 890 Occurrence Handle10.1023/A:1018882221008

    Article  Google Scholar 

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Authors and Affiliations

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

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

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

    R. S. Zhang

  3. Universitè Paris 7-Denis Diderot, ITODYS 1, Rue Guy de la Brosse, F-75005, Paris, France

    X. J. Yao & B. T. Fan

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  1. H. X. Liu
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Correspondence to Z. D. Hu.

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Liu, H.X., Hu, R.J., Zhang, R.S. et al. The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine. J Comput Aided Mol Des 19, 33–46 (2005). https://doi.org/10.1007/s10822-005-0095-8

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  • DOI: https://doi.org/10.1007/s10822-005-0095-8

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