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Journal of Molecular Modeling

, Volume 12, Issue 2, pp 168–181 | Cite as

Linear and nonlinear modeling of antifungal activity of some heterocyclic ring derivatives using multiple linear regression and Bayesian-regularized neural networks

  • Julio Caballero
  • Michael FernándezEmail author
Original paper

Abstract

Antifungal activity was modeled for a set of 96 heterocyclic ring derivatives (2,5,6-trisubstituted benzoxazoles, 2,5-disubstituted benzimidazoles, 2-substituted benzothiazoles and 2-substituted oxazolo(4,5-b)pyridines) using multiple linear regression (MLR) and Bayesian-regularized artificial neural network (BRANN) techniques. Inhibitory activity against Candida albicans (log(1/C)) was correlated with 3D descriptors encoding the chemical structures of the heterocyclic compounds. Training and test sets were chosen by means of k-Means Clustering. The most appropriate variables for linear and nonlinear modeling were selected using a genetic algorithm (GA) approach. In addition to the MLR equation (MLR–GA), two nonlinear models were built, model BRANN employing the linear variable subset and an optimum model BRANN–GA obtained by a hybrid method that combined BRANN and GA approaches (BRANN–GA). The linear model fit the training set (n=80) with r 2=0.746, while BRANN and BRANN–GA gave higher values of r 2=0.889 and r 2=0.937, respectively. Beyond the improvement of training set fitting, the BRANN-GA model was superior to the others by being able to describe 87% of test set (n=16) variance in comparison with 78 and 81% the MLR–GA and BRANN models, respectively. Our quantitative structure–activity relationship study suggests that the distributions of atomic mass, volume and polarizability have relevant relationships with the antifungal potency of the compounds studied. Furthermore, the ability of the six variables selected nonlinearly to differentiate the data was demonstrated when the total data set was well distributed in a Kohonen self-organizing neural network (KNN).

Figure

General structure of heterocyclic ring derivatives

Keywords

QSAR analysis Neural network Bayesian regularization Heterocyclic ring derivatives Antifungal activity 

Notes

Acknowledgements

Authors would like to acknowledge the anonymous referee for his useful comments that helped to improve the quality of the manuscript.

References

  1. 1.
    Georgopapadakou NH (1998) Curr Opin Microbiol 1:547–557CrossRefGoogle Scholar
  2. 2.
    St-Georgiev V (2000) Curr Drug Targets 1:261–284CrossRefGoogle Scholar
  3. 3.
    Rex JH, Walsh TJ, Sobel JD, Filler SG, Pappas PG, Dismukes WE, Edwards JE (2000) Clin Infect Dis 30:662–678CrossRefGoogle Scholar
  4. 4.
    Meyers FH, Jawetz E, Goldfien A (1976) Review of medical pharmacology. Lange Medical Pub, CaliforniaGoogle Scholar
  5. 5.
    Tafi A, Costi R, Botta M, Di Santo R, Corelli F, Massa S, Ciacci A, Manetti F, Artico M (2002) J Med Chem 45:2720–2732CrossRefGoogle Scholar
  6. 6.
    Chan JH., Hong, JS, Kuyper LF, Baccanari DP, Joyner SS, Tansik RL, Boytos CM, Rudolph SK (1995) J Med Chem 38:3608–3616CrossRefGoogle Scholar
  7. 7.
    Elnima EI, Zubair MU, Al-Badr AA (1981) Antimicrob Agents Chemother 19:29–32Google Scholar
  8. 8.
    Göker H, Kus C, Boykin DW, Yildizc S, Altanlarc N (2002) Bioorg Med Chem 10:2589–2596CrossRefGoogle Scholar
  9. 9.
    Yildiz-Oren I, Yalcin I, Aki-Sener E, Ucarturk N (2004) Eur J Med Chem 39:291–298CrossRefGoogle Scholar
  10. 10.
    Yalcin I, Sener E, Ozden T, Ozden S, Akin A (1990) Eur J Med Chem 25:705–708CrossRefGoogle Scholar
  11. 11.
    Hansch C, Leo A (1995) Exploring QSAR. Fundamentals and applications in chemistry and biology, ACS professional reference book. American chemical society, Washington DCGoogle Scholar
  12. 12.
    Yalcin I, Oren I, Temiz O, Sener EA (2000) Acta Biochim Pol 47:481–486Google Scholar
  13. 13.
    García-Domenech R, Ríos-Santamarina I, Catalá A, Calabuig C, del Castillo L, Gálvez J (2003) J Mol Struct (THEOCHEM) 624:97–107CrossRefGoogle Scholar
  14. 14.
    Hasegawa K, Deushi T, Yaegashi O, Miyashita Y, Sasaki S (1995) Eur J Med Chem 30:569–574CrossRefGoogle Scholar
  15. 15.
    Mghazli S, Jaouad A, Mansour M, Villemin D, Cherqaoui D (2001) Chemosphere 43:385–390CrossRefGoogle Scholar
  16. 16.
    Mackay DJC (1992) Neural Comput 4:415–447CrossRefGoogle Scholar
  17. 17.
    Stewart JJP (1989) J Comp Chem 10:210–220Google Scholar
  18. 18.
    MOPAC 6.0 (1993) Frank J Seiler Research Laboratory, US Air Force academy, Colorado Springs, COGoogle Scholar
  19. 19.
    Todeschini R, Consonni V, Pavan M (2002) Dragon software version 2.1Google Scholar
  20. 20.
    Todeschini R, Consonni V (2000) Handbook of molecular descriptors. Wiley-VCH, WeinheimGoogle Scholar
  21. 21.
    Kruszewski J, Krygowski TM (1972) Tetrahedron Lett 36:3839–3842CrossRefGoogle Scholar
  22. 22.
    Jug K (1983) J Org Chem 48:1344–1348CrossRefGoogle Scholar
  23. 23.
    Randic M (1995) J Chem Inf Comput Sci 35:372–382Google Scholar
  24. 24.
    Hemmer MC, Steinhauer V, Gasteiger J (1999) Vibrat Spect 19:151–154CrossRefGoogle Scholar
  25. 25.
    Schuur J, Selzer P, Gasteiger J (1996) J Chem Inf Comput Sci 36:334–344CrossRefGoogle Scholar
  26. 26.
    Todeschini R, Lansagni M, Marengo E (1994) J Chemom 8:263–272CrossRefGoogle Scholar
  27. 27.
    Consonni V, Todeschini R, Pavan M (2002) J Chem Inf Comput Sci 42:682–692CrossRefGoogle Scholar
  28. 28.
    Mc Farland JW, Gans DJ (1995) Cluster significance analysis. In: Manhnhold R, Krogsgaard-Larsen P, Timmerman H (eds) Method and principles in medicinal chemistry, vol 2. Chemometric methods in molecular design. van Waterbeemd H (ed) VCH Weinheim, pp 295–307Google Scholar
  29. 29.
    Gao H, Lajiness MS, Van Drie J (2002) J Mol Graph Model 20:259–268CrossRefGoogle Scholar
  30. 30.
    So SS, Karplus M (1996) J Med Chem 39:1521–1530CrossRefGoogle Scholar
  31. 31.
    Matlab 7.0 (2004) The Math Works IncGoogle Scholar
  32. 32.
    The MathWorks Inc (2004) Genetic algorithm and direct search toolbox user’s guide for use with MATLAB. The Mathworks Inc, MassachusettsGoogle Scholar
  33. 33.
    Hemmateenejad B, Safarpour MA, Miri R, Nesari N (2005) J Chem Inf Model 45:190–199CrossRefGoogle Scholar
  34. 34.
    Zupan J, Gasteiger J (1991) Anal Chim Acta 248:1–30CrossRefGoogle Scholar
  35. 35.
    Burden FR, Winkler D (2000) Chem Res Toxicol 13:436–440CrossRefGoogle Scholar
  36. 36.
    Kohonen T (1987) Self-organization and associative memory, 2nd edn. Springer-Verlag, BerlinGoogle Scholar
  37. 37.
    Wold S (1991) Quant Struct–Act Relat 10:191–193CrossRefGoogle Scholar
  38. 38.
    Moreau G, Broto P (1980) Nouv J Chim 4:757–764Google Scholar
  39. 39.
    Foresee FD, Hagan MT (1997) Gauss-Newton approximation to Bayesian regularization. Proceedings of the 1997 International joint conference on neural networks 1930–1935Google Scholar
  40. 40.
    Bazoui H, Zahouily M, Sebti S, Boulajaaj S, Zakarya D (2002) J Mol Model 8:1–7CrossRefGoogle Scholar
  41. 41.
    Fernández M, Caballero J, Helguera AM, Castro EA, González MP (2005) Bioorg Med Chem 13:3269–3277CrossRefGoogle Scholar
  42. 42.
    Golbraikh A, Tropsha A (2002) J Comp Aided Mol Design 16:357–369CrossRefGoogle Scholar
  43. 43.
    González MP, Helguera AM (2003) J Comp Aided Mol Design 17:665–672CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2005

Authors and Affiliations

  1. 1.Molecular Modeling Group, Probiotic Group, Center for Biotechnological StudiesUniversity of MatanzasMatanzasCuba

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