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Tytuł artykułu

An Optimal Characterization of Structure by Means of several Molecular Connectivity and Complexity Indices

Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
(Received April 9th, 2001; revised manuscript November 21st, 2001) An improved numerical procedure is presented in order to enhance the possibilities of fitting polynomial equations to predict log P data within the realm of the QSAR/QSPR theory. The use of real exponents instead of restricting to integer ones for the variables in the mathematical equations gives better results and a minor number of independent variables are needed to achieve a given accuracy degree. Some possible future extensions of the method are pointed out.
Rocznik
Strony
589--600
Opis fizyczny
Bibliogr. 32 poz., rys.
Twórcy
autor
  • Facultad de Agroindustrias, Universidad Nacional del Nordeste, R. S. Pena, Chaco, Argentina
autor
  • Facultad de Agroindustrias, Universidad Nacional del Nordeste, R. S. Pena, Chaco, Argentina
autor
  • Facultad de Agroindustrias, Universidad Nacional del Nordeste, R. S. Pena, Chaco, Argentina
autor
  • CEQUlNOR, Departamento de Química, Facultad de Ciencias Exactas, Universidad Nacional de La Plata, C. C. 962, La Plata 1900, Argentina
Bibliografia
  • 1. Carrupt P., Testa B. and Gaillard P., in Reviews in Computational Chemistry, Lipkowitz K.B. and Boyd D. B. (Eds), Wiley-VCH, NY, 1997,11, 241-345.
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  • 3. Hansch C. and Leo A., Exploring QSAR Fundamentals and Applications in Chemistry and Biology, American Chemical Society, Washington, DC, 1995.
  • 4. Martin Y.C., Quantitative Drug Design: A Critical Introduction, Marcel Dekker, NY, 1978.
  • 5. Hansch C., Maloney P.P., Fujita T. and Mui R, Nature, 194, 178 (1963).
  • 6. Hansch C., Acc. Chem. Res., 2,232 (1969).
  • 7. Leo A., Hansch C. and Elkins D., Chem. Rev., 71, 525 (1971).
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  • 11. Nagy P. I., Dunn III, W.J. and Nicholas J.B., J. Chem. Phys., 91, 3707 (1989).
  • 12. Reynolds C.H., J. Chem. Inf. Comput. Sci., 35, 738 (1995).
  • 13. Dunn III, W.J. and Nagy P.I., J. Comput. Chem., 13, 468 (1992).
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  • 15. Reynolds C.H. and Best S.A., Chemtec, November 1998, 28.
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  • 18. Domine D., Devillers J. and Karcher W., Chapter 2, in Neural Networks in QSAR and Drug Desing, Devillers J., Ed., Academic Press, London, 1996, pp. 47-63 and references therein.
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  • 20. SAS/STAT User Guide, release 6.03 edition, SAS Institute Inc., Cary, NC (1988), p. 1028.
  • 21. Ou X.-C., Ouyang Y. and Lien E.J., J. Mol. Sci. (China), 4, 89 (1986).
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  • 23. Cook R .D., Technometrics, 19, 15 (1977).
  • 24. Duchowicz R and Castro E.A., J. Korean Chem. Soc., 44(6), 501 (2000).
  • 25. Romanelli G.R, Cafferata L.F.R. and Castro E.A., Khim. Fizika, 19(9), 105 (2000).
  • 26. Duchowicz R and Castro E.A., Acta Chem. Slov., 47, 281 (2000).
  • 27. Firpo M., Gavemet L. and Castro E.A., Rom. J. Phys., 44(1,2), 181 (1999).
  • 28. Castro E. A., Comp. Chem., 21(5), 305 (1997).
  • 29. Livingstone D., Data Analysis for Chemists, Oxford Science Publications, Oxford University Press, 1995, Oxford, pp. 127-129.
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  • 32. Wolfram S., The MATHEMATICA® Book, Fourth Edition, Mathematica Version 4, Wolfram Media, Cambridge University Press, NY, 1999, p.106.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BUJ1-0020-0063
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