Advertisement

Perspectives in Drug Discovery and Design

, Volume 12, Issue 0, pp 41–56 | Cite as

Improving the predictive quality of CoMFA models

  • Romano T. Kroemer
  • Peter Hecht
  • Stefan Guessregen
  • Klaus R. Liedl
Article

Keywords

Polymer CoMFA Model Predictive Quality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Cramer III, R. D., Patterson, D. E. and Bunce, J. D., Comparative molecular field analysis (CoMFA):1. Effect of shape on binding of steroids to carrier proteins, J. Am. Chem. Soc., 110 (1988) 5959-5967.Google Scholar
  2. 2.
    Wold, S., Albano, C., Dunn, W. J., Edlund, U., Esbenson, K., Geladi, P., Hellberg, S., Lindberg, W. and Sjöström, M., Multivariate data analysis in chemistry, In Kowalski, B. (Ed.) Chemometrics: Mathematics and statistics in chemistry, Reidel, Dordrecht, The Netherlands, 1984, p. 17-95.Google Scholar
  3. 3.
    Dunn, W. J., III, Wold, S., Edlund, U., Hellberg, S. and Gasteiger, J., Multivariate structure-activity relationship between data from a battery of biological tests and an ensemble of structure descriptors: The PLS method, Quant. Struct.-Act. Relat., 3 (1984) 131-137.Google Scholar
  4. 4.
    Geladi, P., Notes on the history and nature of partial least squares (PLS) modeling, J. Chemometrics, 2 (1988) 231-246.Google Scholar
  5. 5.
    Wold, S., Cross-validatory estimation of the number of components in factor and principal component models, Technometrics, 4 (1978) 397-405.Google Scholar
  6. 6.
    Diaconis, P. and Efron, B., Computer-intensive methods for statistics, Sci. Am., 116 (1984) 96-117.Google Scholar
  7. 7.
    Cramer III, R. D., Bunce, J. D. and Patterson, D. E., Cross-validation, bootstrapping and partial least squares compared with multiple regression in conventional QSAR studies, Quant. Struct.-Act. Relat., 7 (1988) 18-25.Google Scholar
  8. 8.
    Thibaut, U., Applications of CoMFA and related 3D QSAR approaches, In Kubinyi, H. (Ed.) 3D QSAR in drug design: Theory, methods and applications, ESCOM, Leiden, The Netherlands, 1993, pp. 661-696.Google Scholar
  9. 9.
    Clark, M. and Cramer III, R. D., The probability of chance correlation using partial least-squares (PLS), Quant. Struct.-Act. Relat., 12 (1993) 137-145.Google Scholar
  10. 10.
    Kroemer, R. T., Ettmayer, P. and Hecht, P., 3D-quantitative structure-activity relationships of human immunodeficiency virus type-1 proteinase inhibitors: comparative molecular field analysis of 2-heterosubstituted statine derivatives - implications for the design of novel inhibitors, J. Med. Chem., 38 (1995) 4917-4928.Google Scholar
  11. 11.
    Kellog, G. E., Semus, F. E. and Abraham, D. J., HINT: A new method of empirical hydrophobic field calculation for CoMFA, J. Comput.-Aided Mol. Design, 5 (1991) 545-552.Google Scholar
  12. 12.
    Kim, K. H. and Martin, Y. C., Direct prediction of dissociation-constants (PKAS) of clonidin-like imidazolines, 2-substituted imidazoles, and 1-methy-2-substituted-imidazoles from 3D structures using a comparative molecular-field analysis (CoMFA) approach, J. Med. Chem., 34 (1991) 2056-2060.Google Scholar
  13. 13.
    Greco, G., Novellino, E., Silipo, C. and Vittoria, A., Comparative molecular-field analysis on a set of muscarinic agonists, Quant. Struct.-Act. Relat., 10 (1991) 289-299.Google Scholar
  14. 14.
    Klebe, G. and Abraham, U., On the prediction of binding-properties of drug molecules by comparative molecular-field analysis, J. Med. Chem., 36 (1993) 70-80.PubMedGoogle Scholar
  15. 15.
    Floersheim, P., Nouzlak, J. and Weber, H. P., Experience with comparative molecular-field analysis, In Wermuth, C. G. (Ed.) Trends in QSAR and molecular modeling 92, ESCOM, Leiden, The Netherlands, 1993, pp. 227-232.Google Scholar
  16. 16.
    Marsili, M., Floersheim, P. and Dreiding, A. S., Generation and comparison of space-filling molecularmodels, Comput. Chem., 7 (1983) 175-181.Google Scholar
  17. 17.
    Kroemer, R. T. and Hecht, P., Replacement of steric 6-12 potential-derived interaction energies by atombased indicator variables in CoMFA leads to models of higher consistency, J. Comput.-Aided Mol. Design., 9 (1995) 205-212.Google Scholar
  18. 18.
    Cramer III, R. D., Patterson, D. E. and Bunce, J. D., Cross-validation, bootstrapping, and partial leastsquares compared with multiple-regression in conventional QSAR Studies, Quant. Struct.-Act. Relat., 7 (1988) 18-25.Google Scholar
  19. 19.
    Cramer III, R. D., DePriest, S. A., Patterson, D. E. and Hecht, P., The developing practice of comparative molecular-field analysis, In Kubinyi, H., (Ed.) 3D QSAR in drug design, ESCOM, Leiden, The Netherlands, 1993, pp. 465-485.Google Scholar
  20. 20.
    Calder, J. A., Wyatt, J. A., Frenkel, D. A. and Casida, J. E., CoMFA validation of the superposition of 6 classes of compounds which block GABA receptors noncompetitively, J. Comput.-Aided Mol. Design, 7 (1993) 45-60.Google Scholar
  21. 21.
    Rault, S., Bureau, R., Pilo, J. C. and Robba, M., Comparative molecular-field analysis of CCK-A antagonists using field-fit as an alignment technique - a convenient guide to design new CCK-A ligands, J. Comput.-Aided Mol. Design, 6 (1992) 553-568.Google Scholar
  22. 22.
    Allen, M. S., Tan, Y.-C., Trudell, M. L., Narayanan, K., Schindler, L. R., Martin, M. J., Schultz, C., Hagen, T. J., Koehler, K. F., Codding, P. W., Skolnick, P. and Cook, J. M., Synthetic and computer-assisted analyses of the pharmacophore for the benzodiazepine receptor inverse agonist site, J. Med. Chem., 33 (1990) 2343-2357.Google Scholar
  23. 23.
    Allen, M. S., LaLoggia, A. J., Dorn, L. J., Martin, M. J., Costatino, G., Hagen, T. J., Koehler, K. F., Skolnick, P. and Cook, J. M., Predictive Binding of beta-carboline inverse agonists and antagonists via the CoMFA GOLPE approach, J. Med. Chem., 35 (1992) 4001-4010.Google Scholar
  24. 24.
    Kroemer, R. T., Liedl, K. R. and Hecht, P., Different electrostatic descriptors in comparative molecular field analysis (CoMFA): A comparison of molecular electrostatic and coulomb potentials, J. Comput. Chem., 17 (1996) 1296-1308.Google Scholar
  25. 25.
    Gasteiger, J. and Marsilli, M., Iterative partial equalization of orbital electronegativity - a rapid access to atomic charges, Tetrahedron, 36 (1980) 3219-3228.Google Scholar
  26. 26.
    Dewar, M. J. S. and Thiel, W., Ground states of molecules: 38. The MNDO method - approximations and parameters, J. Am. Chem. Soc., 99 (1977) 4899-4907.Google Scholar
  27. 27.
    Dewar, M. J. S., Zoebisch, E. G., Healy, E. F. and Stewart, J. J. P., AM1: A new general purpose quantum chemical mechanical molecular model, J. Am. Chem. Soc., 107 (1985) 3902-3909.Google Scholar
  28. 28.
    Stewart, J. J. P., Optimization of parameters for semiempirical methods: 1. Method, J. Comp. Chem., 10 (1989) 209-220.Google Scholar
  29. 29.
    Mulliken, R. S., Electronic population analysis on LCAO-MO molecular wave functions, I., J. Chem. Phys., 23 (1955) 1833-1840.Google Scholar
  30. 30.
    Singh, U. C. and Kollman, P. A., An approach to computing electrostatic charges for molecules, J. Comp. Chem., 5 (1984) 129-145.Google Scholar
  31. 31.
    Besler, B. H., Merz, K. M., Jr. and Kollman, P. A., Atomic charges derived from semiempirical methods, J. Comp. Chem., 11 (1990) 431-439.Google Scholar
  32. 32.
    Chirlian, L. E. and Francl, M. M., Atomic charges derived from electrostatic potentials - a detailed study, J. Comp. Chem., 8 (1987) 894-905.Google Scholar
  33. 33.
    Breneman, C. M. and Wiberg, K. B., Determining atom-centred monopoles from molecular electrostatic potentials - the need for high sampling density in formamide conformational analysis, J. Comp. Chem., 11 (1990) 361-373.Google Scholar
  34. 34.
    Debnath, A. K., Jiang, S., Strick, N., Lin, K., Haberfield, P. and Neurath, A. R., Three-dimensional structure-activity analysis of a series of porphyrin derivatives with anti-HIV-1 activity targeted on the V3 loop of the gp120 envelope glycoprotein of the human immunodeficiency virus type 1, J. Med. Chem., 37 (1994) 1099-1108.Google Scholar
  35. 35.
    Avery, M. A., Gao, F., Chong W. K. M., Mehrotra, S. and Milhous, W. K., Structure-activity relationships of the antimalarial agent artemisinin: 1. Synthesis and comparative molecular field analysis of C-9 analogs of artemisinin and 10-deoxoartemisinin, J. Med. Chem., 36 (1993) 4264-4275.Google Scholar
  36. 36.
    Carroll, F. I., Mascarella, S. W., Kuzemko, M. A., Gao, Y., Abraham, P., Lewin, A. H., Boja, J. W. and Kuhar, M. J., Synthesis, ligand binding, and QSAR (CoMFA and classical study of β-3′-substituted phenyl)-, β-(4′-substituted phenyl)-, and β-(3′,4′-disubstituted phenyl) tropane-β-carboxylic acid methyl esters, J. Med. Chem. 37 (1994) 2865-2873.PubMedGoogle Scholar
  37. 37.
    Tong, W., Collantes, E. R., Chen, Y. and Welsch, W. J., A comparative molecular-field analysis study of N-benzylpiperidines as acetylcholesterinesterase inhibitors, J. Med. Chem., 39 (1996) 380-387.Google Scholar
  38. 38.
    Kroemer, R. T., Koutsilieri, E., Hecht, P., Liedl, K. R., Riederer, P. and Kornhuber, J., Quantitative analysis of the structural requirements for blockade of the NMDA receptor at the PCP binding site, J. Med. Chem., (in press).Google Scholar
  39. 39.
    Martin, Y. C., Bures, M. G., Dahaner, E. A., DeLazzer, J., Lico, I. and Pavlik, P., A fast approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists, J. Comput.-Aided Mol. Des., 7 (1993) 83-102.Google Scholar
  40. 40.
    Gamper, A. M., Winger, R. H., Liedl, K. R., Sotriffer, C. A., Varga, J. M., Kroemer, R. T. and Rode, B. M., Comparative molecular field analysis (CoMFA) of haptens docked to the multispecific antibody IgE(Lb4), J. Med. Chem., 39 (1996) 3882-3888.Google Scholar
  41. 41.
    Goodsell, D. S. and Olson A. J., Automated docking of substrates to proteins by simulated annealing, Proteins: Struct. Funct. Genet., 8 (1990) 195-202.Google Scholar
  42. 42.
    Marshall, G. R., Barry, C. D., Bosshard, H. E., Dammkoehler, R. A. and Dunn, D. A., The conformational parameters in drug design, In Olson, E. C. and Christoffersen, R. E. (Eds.) Computer-assisted drug design, ACS Symp. Series, Vol. 112, American Chemical Society, Washington, DC, 1979, pp. 205-226.Google Scholar
  43. 43.
    Thibaut, U., Folkers, G., Klebe, G., Kubinyi, H., Merz, A. and Rognan, D., Recommendations for CoMFA studies and 3D QSAR publications, Quant. Struct.-Act. Relat., 13 (1994) 1-3.Google Scholar
  44. 44.
    Kroemer, R. T. and Hecht, P., A new procedure for improving the predictiveness of CoMFA-models and its application to a set of dihydrofolate reductase inhibitors, J. Comput.-Aided Mol. Des., 9 (1995) 396-406.Google Scholar
  45. 45.
    Silipo, C. and Hansch, C., Correlation analysis: Its application to the structure-activity relationship of triazines inhibiting dihydrofolate reductase, J. Am. Chem. Soc. (1975) 6849-6861.Google Scholar
  46. 46.
    Baroni, M., Constantino, G., Cruciani, G., Riganelli, D., Valigi, R. and Clementi, S., Generating optimal linear PLS estimations (GOLPE): An advanced chemometric tool for handling 3D-QSAR problems, Quant. Struct.-Act. Relat., 12 (1993) 9-20.Google Scholar

Copyright information

© Kluwer Academic Publishers 1998

Authors and Affiliations

  • Romano T. Kroemer
    • 1
  • Peter Hecht
    • 2
  • Stefan Guessregen
    • 2
  • Klaus R. Liedl
    • 3
  1. 1.Physical and Theoretical Chemistry LaboratoryUniversity of OxfordOxfordU.K.
  2. 2.Tripos GmbHMunichGermany
  3. 3.Department of General, Inorganic and Theoretical ChemistryUniversity of InnsbruckInnsbruckAustria

Personalised recommendations