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Replacement of steric 6–12 potential-derived interaction energies by atom-based indicator variables in CoMFA leads to models of higher consistency

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Summary

The steric descriptors commonly used in CoMFA — Lennard-Jones 6–12 potential-derived interaction energies calculated between a probe atom and the molecules under investigation — have been replaced by variables indicating the presence of an atom of a particular molecule in predefined volume elements (cubes) within the region enclosing the ensemble of superimposed molecules. The resulting ‘atom indicator vectors’ were used as steric fields in the subsequent PLS analyses, with and without inclusion of electrostatic Coulomb interaction-derived fields. Application of this method to five training sets (80 compounds each) and five test sets (60 compounds each), randomly selected from an ensemble of 256 dihydrofolate reductase inhibitors, leads to models of significantly higher consistency, as indicated by the cross-validated r2 values for the training sets and the predictive r2 values for the test sets.

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References

  1. CramerIII, R.D., Patterson, D.E. and Bunce, J.E., J. Am. Chem. Soc., 110 (1988) 5959. For applications of CoMFA in medicinal chemistry, see for instance Refs. 2–7:

    Google Scholar 

  2. Avery, M.A., Gao, F. and Chong, W.K.M., J. Med. Chem., 36 (1993) 4264.

    Google Scholar 

  3. Horwitz, J.P., Massova, I., Wiese, T.E., Besler, B.H. and Corbett, T.H., J. Med. Chem., 37 (1994) 781.

    Google Scholar 

  4. Waller, C.L., Oprea, T.I., Giolitti, A. and Marshall, G.R., J. Med. Chem., 36 (1993) 4152.

    Google Scholar 

  5. Waller, C.L. and Marshall, G.R., J. Med. Chem., 36 (1993) 2390.

    Google Scholar 

  6. DePriest, S.A., Mayer, D., Naylor, C.B. and Marshall, G.R., J. Am. Chem. Soc., 115 (1993) 5372.

    Google Scholar 

  7. Debnath, A.K., Hansch, C., Kim, K.H. and Martin, Y.C., J. Med. Chem., 36 (1993) 1007.

    Google Scholar 

  8. Wold, S., Ruhe, A., Wold, H. and DunnIII, W.J., SIAM J. Sci. Stat. Comput., 5 (1984) 735.

    Google Scholar 

  9. Wold, S., Albano, C., DunnIII, W.J., Edlund, U., Esbenson, K., Geladi, P., Hellberg, S., Johannson, E., Lindberg, W. and Sjörström, M., In Kowalski, B. (Ed.) Chemometrics: Mathematics and Statistics in Chemistry, Reidel, Dordrecht, 1984, pp. 17–95.

    Google Scholar 

  10. Stahle, L. and Wold, S., Prog. Med. Chem., 25 (1988) 292.

    Google Scholar 

  11. CramerIII, R.D., Patterson, D.E. and Bunce, J.D., Quant. Struct.-Act. Relatsh., 7 (1988) 18.

    Google Scholar 

  12. Thibaut, U., Folkers, G., Klebe, G., Kubinyi, H., Merz, A. and Rognan, D., Quant. Struct.-Act. Relatsh., 13 (1994) 1.

    Google Scholar 

  13. Snell, C., (1994) personal communication.

  14. Marsili, M., Floersheim, P. and Dreiding, A.S., Comput. Chem., 7 (1983) 175.

    Google Scholar 

  15. Doweyko, A.M., J. Med. Chem., 31 (1988) 1396.

    Google Scholar 

  16. Silipo, C. and Hansch, C., J. Am. Chem. Soc., 97 (1975) 6849.

    Google Scholar 

  17. Kim, K.H. and Martin, Y.C., J. Med. Chem., 34 (1991) 2056.

    Google Scholar 

  18. Greco, G., Novellino, E., Silipo, C. and Vittoria, A., Quant. Struct.-Act. Relatsh., 10 (1991) 289.

    Google Scholar 

  19. Klebe, G. and Abraham, U., J. Med. Chem., 36 (1993) 70.

    Google Scholar 

  20. SYBYL Molecular Modelling Package, Version 6.04, TRIPOS Associates, St. Louis, MO, 1993.

  21. Vinter, J.G., Davies, A. and Saunder, M.R., J. Comput.-Aided Mol. Design, 1 (1987) 31.

    Google Scholar 

  22. Powell, M.J.D., Math. Program., 12 (1977) 241.

    Google Scholar 

  23. Stewart, J.J.P. and Seiler, F.J., MOPAC (Version 5.00), QCPE Program No. 455, Quantum Chemistry Program Exchange, University of Indiana, Bloomington, IN, 1989.

    Google Scholar 

  24. Dewar, M.J.S. and Thiel, W., J. Am. Chem. Soc., 99 (1977) 4899.

    Google Scholar 

  25. SYBYL Molecular Modelling Software, Version 6.0 Theory Manual, Tripos Associates, St. Louis, MO, 1992, p. 2225.

  26. CramerIII, R.D., DePriest, S.A., Patterson, D.E. and Hecht, P., In Kubinyi, H. (Ed.) 3D QSAR in Drug Design: Theory, Methods and Applications, ESCOM, Leiden, 1993, p. 465.

    Google Scholar 

  27. Calder, J.A., Wyatt, J.A., Frenkel, D.A. and Casida, J.E., J. Comput.-Aided Mol. Design, 7 (1993) 45.

    Google Scholar 

  28. Rault, S., Bureau, R., Pilo, J.C. and Robba, M., J. Comput.-Aided Mol. Design, 6 (1992) 553.

    Google Scholar 

  29. Wiese, M. and Coats, E.A., Pharmacochem. Libr., 16 (1991) 343.

    Google Scholar 

  30. Floersheim, P., Nouzlak, J. and Weber, H.P., In Wermuth, C.G. (Ed.) Trends in QSAR and Molecular Modelling 92 (Proceedings of the 9th European Symposium on Structure-Activity Relationships: QSAR and Molecular Modelling), ESCOM, Leiden, 1993, p. 227.

    Google Scholar 

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Kroemer, R.T., Hecht, P. Replacement of steric 6–12 potential-derived interaction energies by atom-based indicator variables in CoMFA leads to models of higher consistency. J Computer-Aided Mol Des 9, 205–212 (1995). https://doi.org/10.1007/BF00124452

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