Journal of Computer-Aided Molecular Design

, Volume 21, Issue 4, pp 145–153

3D-QSAR study of hallucinogenic phenylalkylamines by using CoMFA approach

  • Zhuoyong Zhang
  • Liying An
  • Wenxiang Hu
  • Yuhong Xiang
Original Paper

Abstract

The three-dimensional quantitative structure–activity relationship (3D-QSAR) has been studied on 90 hallucinogenic phenylalkylamines by the comparative molecular field analysis (CoMFA). Two conformations were compared during the modeling. Conformation I referred to the amino group close to ring position 6 and conformation II related to the amino group trans to the phenyl ring. Satisfactory results were obtained by using both conformations. There were still differences between the two models. The model based on conformation I got better statistical results than the one about conformation II. And this may suggest that conformation I be preponderant when the hallucinogenic phenylalkylamines interact with the receptor. To further confirm the predictive capability of the CoMFA model, 18 compounds with conformation I were randomly selected as a test set and the remaining ones as training set. The best CoMFA model based on the training set had a cross-validation coefficient q2 of 0.549 at five components and non cross-validation coefficient R2 of 0.835, the standard error of estimation was 0.219. The model showed good predictive ability in the external test with a coefficient Rpre2 of 0.611. The CoMFA coefficient contour maps suggested that both steric and electrostatic interactions play an important role. The contributions from the steric and electrostatic fields were 0.450 and 0.550, respectively.

Keywords

3D-QSAR Amphetamines CoMFA Hallucinogen Phenylethylamines 

References

  1. 1.
    Nichols DE (2004) Pharmacol Ther 101:131CrossRefGoogle Scholar
  2. 2.
    Krebs-Thomson K, Paulus MP, Geyer MA (1998) Neuropsychopharmacology 18:339CrossRefGoogle Scholar
  3. 3.
    Nichols DE (1981) J Pharm Sci 70:839CrossRefGoogle Scholar
  4. 4.
    Gupta SP, Singh P, Bindal MC (1983) Chem Rev 83:633CrossRefGoogle Scholar
  5. 5.
    Clare BW (1990) J Med Chem 33:687CrossRefGoogle Scholar
  6. 6.
    Clare BW (2002) Computer-Aided Mol Des 16:611CrossRefGoogle Scholar
  7. 7.
    Schulze-Alexandru M, Kovar K-A (1999) Quant Struct-Act Relat 18:548CrossRefGoogle Scholar
  8. 8.
    Cramer RD, Patterson DE, Bunce JD (1988) J Am Chem Soc 110:5959CrossRefGoogle Scholar
  9. 9.
    Klebe G, Abraham U, Mietzner T (1994) J Med Chem 37:4130CrossRefGoogle Scholar
  10. 10.
    Song MH, Breneman CM, Sukumar N (2004) Bioorg Med Chem 12:489CrossRefGoogle Scholar
  11. 11.
    Kennard O, Giacovazzo C, Horn AS, Mongiorgi R, Riva di Sanseverino L (1974) J Chem Soc Perkin Trans II 10:1160CrossRefGoogle Scholar
  12. 12.
    Hu WX, Yun LH (1992) Chin Chem Lett 3:271Google Scholar
  13. 13.
    Pauling P, Data N (1980) J Pro Natl Acad Sci USA 77:708CrossRefGoogle Scholar
  14. 14.
    Tripos Inc. (2005) Sybyl 7.1. Manual, St. Louis, MOGoogle Scholar
  15. 15.
    Chambers JJ, Nichols DE (2002) J Comput-Aided Mol Des 16:511CrossRefGoogle Scholar
  16. 16.
    McLean TH, Chambers JJ, Parrish JC, Braden MR, Marona-Lewicka D, Kurrasch-Orbaugh D, Nichols DE (2006) J Med Chem 49:4269CrossRefGoogle Scholar
  17. 17.
    Glennon RA, Young R, Benington F, Morint RD (1982) J Med Chem 25:1163CrossRefGoogle Scholar
  18. 18.
    Domelsmith LN, Eaton TA, Houk KN, Anderson GM, Glennon RA, Shulgin AT, Castagnoli N, Kollman PA (1981) J Med Chem 24:1414CrossRefGoogle Scholar
  19. 19.
    Braun U, Braun G, Jacob P III, Nichols DE, Shulgin AT (1978) NIDA Res Monogr 22:35Google Scholar
  20. 20.
    Parker MA, Lewicka DM, Lucaites VL, Nelson DL, Nichols DE (1998) J Med Chem 41:5148CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2006

Authors and Affiliations

  • Zhuoyong Zhang
    • 1
  • Liying An
    • 1
  • Wenxiang Hu
    • 1
  • Yuhong Xiang
    • 1
  1. 1.Department of ChemistryCapital Normal UniversityBeijingP.R. China

Personalised recommendations