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Using particle swarms for the development of QSAR models based on K-nearest neighbor and kernel regression

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Abstract

We describe the application of particle swarms for the development of quantitative structure-activity relationship (QSAR) models based on k-nearest neighbor and kernel regression. Particle swarms is a population-based stochastic search method based on the principles of social interaction. Each individual explores the feature space guided by its previous success and that of its neighbors. Success is measured using leave-one-out (LOO) cross validation on the resulting model as determined by k-nearest neighbor kernel regression. The technique is shown to compare favorably to simulated annealing using three classical data sets from the QSAR literature.

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References

  1. Neural Networks in QSAR and drug design, Academic Press: New York, 1996.

  2. Breiman, L., Friedman, J.H., Olshen, R.A., Stone, C.J., Classification and Regression Trees. Wadsworth Intl. Group: Belmont, CA, 1984.

    Google Scholar 

  3. Parzen, E., Ann. Math. Stat. 33 (1962) 1065-1076.

    Google Scholar 

  4. van de Waterbeemd, H., Ed. Chemometric Methods in Drug Design, VCH: Weinheim, 1995, Vol. 2.

    Google Scholar 

  5. Hansch, L., Leo, C., In American Chemical Society: Washington, DC, 1995.

    Google Scholar 

  6. Manallack, D.T., Ellis, D.D., Livingston, D.J.J., Med. Chem., 37 (1994) 3758-3767.

    Google Scholar 

  7. Topliss, J.G., Edwards, R.P.J., Med. Chem., 22 (1979) 1238-1244.

    Google Scholar 

  8. John, G., Kohavi, R., Pfleger, J. In Machine Learning: Proceedings of the 11-th International Conference, Morgan-Kaufmann, 1994, pp. 121-129.

  9. Selwood, D.L., Livingston, D.J., Comley, J.C.W., O'Dowd, A.B., Hudson, A.T., Jackson, P., Jandu, K.S., Rose, V.S., Stables, J.N.J., Med. Chem., 33 (1990) 136-142.

    Google Scholar 

  10. Sutter, J.M., Dixon, S.L., Jurs, P.C.J., Chem. Inf. Comput. Sci., 35 (1995) 77-84.

    Google Scholar 

  11. Luke, B.T.J., Chem. Inf. Comput. Sci., 34 (1994) 1279-1287.

    Google Scholar 

  12. Rogers, D.R., Hopfinger, A.J.J., Chem. Inf. Comput. Sci., 34 (1994) 854-866.

    Google Scholar 

  13. So, S.-S., Karplus, M.J., Med. Chem., 38 (1996) 5246-5256.

    Google Scholar 

  14. Yasri, A., Hartsough, D.J., Chem. Inf. Comput. Sci., 41 (2001) 1218-1227.

    Google Scholar 

  15. Hasegawa, K., Miyashita, Y., Funatsu, K.J., Chem. Inf. Comput. Sci., 37 (1997) 306-310.

    Google Scholar 

  16. Izrailev, S., Agrafiotis, D.K., SAR and QSAR in Environ. Res., 2001.

  17. Agrafiotis, D.K., Cedeno, W.J., Med. Chem., 45 (2002) 1098-1107.

    Google Scholar 

  18. Wettschereck, D., Aha, D.W., Mohri, T., Artif. Intell. Review 11 (1997) 273-314.

    Google Scholar 

  19. Aha, D.W., In Liu, H., Motoda, H., Eds. Feature extraction, construction and selection: a data mining perspective, Kluwer: Norwell, MA, 1998.

    Google Scholar 

  20. Kohavi, R., Langley, P., Yun, Y. In European Conference on Machine Learning (ECML97), 1997.

  21. Reymer, M.L., Punch, W.F., Goodman, E.D., Kuhn, L.A., Jain, A.K., IEEE Trans. Evol. Comput., 4 (2000) 164-171.

    Google Scholar 

  22. Komosinski, M., Krawiec, K., Artif. Intel. Med., 19, 25-38.

  23. Kennedy, J., Eberhart, R. In IEEE International Conference on Neural Networks, IEEE Service Center: Perth, Australia, 1995, pp. 1942-1948.

    Google Scholar 

  24. Nadaraya, E.A. Theory of Probability and its Applications, 9 (1964) 141-142.

    Google Scholar 

  25. Watson, G.S. Sankhya - Ind. J. Stat. A, 26, 359-372.

  26. Agrafiotis, D.K.J., Chem. Inf. Comput. Sci., 39 (1999) 51-58.

    Google Scholar 

  27. Shi, Y.H., Eberhart, R. In IEEE International Conference on Evolutionary Computation: Anchorage, AL, 1998.

  28. Shi, Y.H., Eberhart, R. In 7th. Annual Conference on Evolutionary Programming: San Diego, CA, 1998.

  29. Kirkpatrik, S., Gelatt, C.D.J., Vecchi, M.P., Science, 220 (1983) 671.

    Google Scholar 

  30. Agrafiotis, D.K.J., Chem. Inf. Comput. Sci., 37 (1997) 841-851.

    Google Scholar 

  31. Wikel, J.H., Dow, E.R., Bioorg. Med. Chem. Lett., 3 (1993) 645-651.

    Google Scholar 

  32. Maddalena, D.J., Johnson, G.A.R.J., Med. Chem. 1995, 38.

  33. Hirst, J.D., King, R.D., Sternberg, M.J.E.J., Comput.-Aided Mol. Design, 8 (1994) 405-420.

    Google Scholar 

  34. Agrafiotis, D.K., Bone, R.F., Salemme, F.R., Soll, R.M. United States, 1995.

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  1. 3-Dimensional Pharmaceuticals, Inc., 665 Stockton Drive, Exton, PA, 19341, USA

    Walter Cedeño & Dimitris K. Agrafiotis

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  1. Walter Cedeño
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  2. Dimitris K. Agrafiotis
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Cedeño, W., Agrafiotis, D.K. Using particle swarms for the development of QSAR models based on K-nearest neighbor and kernel regression. J Comput Aided Mol Des 17, 255–263 (2003). https://doi.org/10.1023/A:1025338411016

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