Partition-based selection

Summary

This article concentrates on diversity-related methods in which the analysis involves a partitioning into discrete cells of compounds or data derived from compounds. A detailed discussion is given of two major approaches. In the first approach, each compound is assigned to only one cell, and two methods that use partitioning by molecular/atomic properties are described (DPD; BCUT/DiverseSolutions). In the second approach, each compound may exhibit multiple values of the property being partitioned, particularly when conformational flexibility is taken into account, and therefore may belong to many partitions; two methods that use potential three-dimensional pharmacophores as a molecular similarity/diversity measure are described (PDQ and ChemDiverse). The use of these methods for database analysis, subset selection and combinatorial library design is discussed.

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References

  1. 1.a.

    Mason, J.S., Lewis, R.A., McLay, I.M., Menard, P.R. and Pickett, S.D., In Random and Rational Drug Discovery via Rational Design and Combinatorial Chemistry, Strategic Research Institute, New York, NY, 1995.

    Google Scholar 

  2. 1.b.

    Lewis, R.A., Mason, J.S. and Menard, P.M., in preparation.

  3. 2.

    Hanscn, C. and Leo, A., In Exploring QSAR, Vol. 1: Fundamentals and Applications in Chemistry and Biology, American Chemical Society, Washington, DC, 1995, pp. 97–168.

    Google Scholar 

  4. 3.a.

    Pearlman, R.S., Novel software tools for addressing molecular diversity, accessible through URL http://www.awod.com/netsci/Science/Combichem/feature08.ntml.

  5. 3.b.

    Pearlman, R.S. and Smith, K.M., DiverseSolutions user’s manual, Laboratory for Molecular Graphics and Theoretical Modeling, University of Texas at Austin, Austin, TX, U.S.A., distributed by Tripos Inc. (see Ref. 31).

  6. 4.a.

    Pearlman, R.S. and Smith, K.M., BCUT, in preparation.

  7. 4.b.

    Pearlman, R.S., In Lead Generation and Optimization, Strategic Research Institute, New York, NY, 1996.

    Google Scholar 

  8. 5.

    Lewis, R.A., Mason, J.S. and McLay, I.M., J. Chem. Inf. Comput. Sci., 37 (1997) 599.

    CAS  Article  PubMed  Google Scholar 

  9. 6.a.

    Mason, J.S., McLay, I.M. and Lewis, R.A., In Dean, P.M., Jolies, G. and Newton, C.G. (Eds.) New Perspectives in Drug Design, Academic Press, London, U.K., 1995, pp. 225–253.

    Google Scholar 

  10. 6.b.

    Lewis, R.A., McLay, I.M. and Mason, J.S., Chem. Design Autom. News, 10 (1995) 37.

    Google Scholar 

  11. 7.

    Ashton, M.J., Jaye, M.C. and Mason, J.S., Drug Discov. Today, 2 (1996) 71.

    Article  Google Scholar 

  12. 8.

    Hansch, C. and Leo, A., In Substituent Constants for Correlation Analysis in Chemistry and Biology, Wiley/Interscience, New York, NY, 1979, pp. 18–43.

    Google Scholar 

  13. 9.a.

    Hall, L.H. and Kier, L.B., In Boyd, D.B. and Lipkowitz, K.B. (Eds.) Reviews in Computational Chemistry, Vol. 2, VCH, New York, NY, 1991, pp. 367–422.

    Google Scholar 

  14. 9.b.

    Hall, L.H. and Kier, L.B., molconn-x: A Program for Molecular Topology, Hall Associates Computing, Quincy, MA, U.S.A.

  15. 10.

    Hall, L.H., Mohney, B.K and Kier, L.B., J. Chem. Inf. Comput. Sci., 31 (1991) 76.

    CAS  Article  Google Scholar 

  16. 11.

    Kier, L.B. and Hall, L.H., In Molecular Connectivity in Structure-Activity Analysis, Research Studies Press, 1986.

    Google Scholar 

  17. 12.

    Daylight Software Manual: Theory, Daylight Chemical Information Systems, Mission Viejo, CA, and Santa Fe, NM, U.S.A. (Daylight daymodels/MedChem software).

  18. 13.

    Weininger, D., J. Chem. Inf. Comput. Sci., 28 (1988) 31.

    CAS  Article  Google Scholar 

  19. 14.

    Weininger, D., Weininger, A. and Weininger, J.L., J. Chem. Inf. Comput. Sci., 29 (1989) 97.

    CAS  Article  Google Scholar 

  20. 15.

    Burden, F.R., J. Chem. Inf. Comput. Sci., 29 (1989) 225.

    CAS  Article  Google Scholar 

  21. 16.

    MDDR: Maccs-II Drug Data Report, Molecular Design Ltd., San Leandro, CA, U.S.A.; an electronic database of biologically active compounds from the Drug Data Report (Prous Science Publishers).

  22. 17.

    Available Chemicals Directory, Molecular Design Ltd., San Leandro, CA, U.S.A.; an electronic database of bulk and speciality chemicals from commercial sources.

  23. 18.

    Martin, E.J., Blaney, J.M., Siani, M.A., Spellmeyer, D.C., Wong, A.K. and Moos, W.H., J. Med. Chem., 38 (1995) 1431.

    CAS  Article  PubMed  Google Scholar 

  24. 19.

    Cummins, D.J., Andrews, C.W., Bentley, J.A. and Cory, M., J. Chem. Inf. Comput. Sci., 36 (1996) 750.

    CAS  Article  PubMed  Google Scholar 

  25. 20.

    Kearsley, S.K., Sallamack, S., Fluder, E.M., Andose, J.D., Mosley, R.T. and Sheridan, R.P., J. Chem. Inf. Comput. Sci., 36 (1996) 118.

    CAS  Article  Google Scholar 

  26. 21.

    Hudson, B.D., Hyde, R.M., Rahr, E., Wood, J. and Osman, J., Quant. Struct.-Act. Relatsh., 15 (1996) 285.

    CAS  Article  Google Scholar 

  27. 22.

    Lajiness, M., In Van de Waterbeemd, H. (Ed.) Structure-Property Correlations in Drug Research, R.G. Landes Company, Austin, TX, 1996, Chapter 5.

    Google Scholar 

  28. 23.

    Holland, J.D., Ranade, S.S. and Willett, P., Quant. Struct.-Act. Relatsh., 14 (1995) 501.

    Article  Google Scholar 

  29. 24.

    Turner, D.B., Tyrell, S.M. and Willett, P., J. Chem. Inf. Comput. Sci., 37 (1997) 18.

    CAS  Article  Google Scholar 

  30. 25.

    Good, A.C. and Mason, J.S., In Lipkowitz, K.B. and Boyd, D.B. (Eds.) Reviews in Computational Chemistry, Vol. 7, VCH, New York, NY, 1996, pp. 67–117.

    Google Scholar 

  31. 26.

    Mason, J.S., In Ciaassen, V. (Ed.) Trends in Drug Research, Proceedings of the 9th Noordwijkerhout-Camerino Symposium, Noordwijkerhout, The Netherlands, May 23–28 1993, Elsevier, Amsterdam, 1993, pp. 147–156.

    Google Scholar 

  32. 27.

    Mason, J.S., In Dean, P.M. (Ed.) Molecular Similarity in Drug Design, Blackie Academic and Professional, Glasgow, 1995, pp. 138–162.

    Google Scholar 

  33. 28.a.

    Pickett, S.D., Mason, J.S. and McLay, I.M., J. Chem. Inf. Comput. Sci., 36 (1996) 1214.

    CAS  Article  Google Scholar 

  34. 28.b.

    Preliminary communications: Pickett, S.D., McLay, I.M., Lewis, R.A. and Mason, J.S., Chem. Design Autom. News, 10 (1995) 38 and in Ref. 7.

    Google Scholar 

  35. 29.a.

    Davies, E.K. and Briant, C., Combinatorial chemistry library design using pharmacophore diversity, accessible through URL http://www.awod.com/netsci/Science/Combichem/feature05.html.

  36. 29.b.

    Davies, K., In Chaiken, I.M. and Janda, K.D. (Eds.) Molecular Diversity and Combinatorial Chemistry. Libraries and Drug Discovery, American Chemical Society, Washington, DC, 1996, pp. 309–316.

    Google Scholar 

  37. 30.

    Murrall, N.W. and Davies, E.K., J. Chem. Inf. Comput. Sci., 30 (1990) 31.

    Article  Google Scholar 

  38. 31.

    Unity Chemical Information Software, Tripos Inc., St. Louis, MO, U.S.A.

  39. 32.

    ChemDBS-3D/ChemDiverse software: developed and distributed as part of the Chem-X modeling package by Chemical Design Ltd., Chipping Norton, Oxfordshire, U.K.

  40. 33.

    Greene, J., Kahn, S., Savoj, H., Sprague, P., and Teig, S., J. Chem. Inf. Comput. Sci., 34 (1994) 1297.

    CAS  Article  Google Scholar 

  41. 34.

    Lewis, R.A., Good, A.C. and Pickett, S.D., In Van de Waterbeemd, H., Testa, B. and Folkers, G. (Eds.) Computer Assisted Lead Finding and Optimization, Proceedings of the European QSAR meeting, Lausanne, 1996, Wiley-VCH, Basle, 1997, pp. 135–156.

    Google Scholar 

  42. 35.

    Brown, R.D. and Martin, Y.C., J. Chem. Inf. Comput. Sci., 36 (1996) 572.

    CAS  Article  Google Scholar 

  43. 36.

    Maccs II, Molecular Design Ltd., San Leandro, CA, U.S.A.

  44. 37.

    Brown, R.D. and Martin, Y.C., J. Chem. Inf. Comput. Sci., 37 (1997) 1.

    CAS  Article  Google Scholar 

  45. 38.

    Mason, J.S., In Lead Generation and Optimization, Strategic Research Institute, New York, NY, 1996 (March 21–22, New Orleans meeting).

    Google Scholar 

  46. 39.a.

    Mason, J.S., Charleston Conference: Advancing New Lead Discovery, 1997.

  47. 39.b.

    Mason, J.S. and Morize, I.M., Abstracts of the American Chemical Society Meeting, San Francisco, April 1997.

    Google Scholar 

  48. 40.

    DeWitt, S.H., Kiely, J.S., Stankovic, C.J., Schroeder, M.C., Cody, D.M.R. and Pavia, M.R., Proc. Natl. Acad. Sci. U.S.A., 90 (1993) 6909.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  49. 41.

    Mason, J.S., In Random and Rational Drug Discovery via Rational Design and Combinatorial Chemistry, Strategic Research Institute, New York, NY, 1996.

    Google Scholar 

  50. 42.

    Goodford, P.J., J. Med. Chem., 28 (1985) 849.

    CAS  Article  PubMed  Google Scholar 

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Correspondence to Jonathan S. Mason.

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Mason, J.S., Pickett, S.D. Partition-based selection. Perspectives in Drug Discovery and Design 7, 85–114 (1996). https://doi.org/10.1007/BF03380183

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Key words

  • cell-based
  • ChemDiverse
  • DiverseSolutions
  • diversity
  • library design
  • molecular properties
  • partitioning
  • pharmacophores
  • subset selection