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PRO_LIGAND: An approach to de novo molecular design. 3. A genetic algorithm for structure refinement

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Summary

Recently, the development of computer programs which permit the de novo design of molecular structures satisfying a set of steric and chemical constraints has become a burgeoning area of research and many operational systems have been reported in the literature. Experience with PRO_LIGAND—the de novo design methodology embodied in our in-house molecular design and simulation system PRO-METHEUS—has suggested that the addition of a genetic algorithm (GA) structure refinement procedure can ‘add value’ to an already useful tool. Starting with the set of designed molecules as an initial population, the GA can combine features from both high- and low-scoring structures and, over a number of generations, produce individuals of better score than any of the starting structures. This paper describes how we have implemented such a procedure and demonstrates its efficacy in improving two sets of molecules generated by different de novo design projects.

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References

  1. Martin, Y.C., Methods Enzymol., 203 (1991) 587.

    Google Scholar 

  2. Dixon, J.S., Trends Biotechnol., 10 (1992) 357.

    Google Scholar 

  3. Moon, J.B. and Howe, W.J., Protein Struct. Funct. Genet., 11 (1991) 314.

    Google Scholar 

  4. Moon, J.B. and Howe, W.J., 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, pp. 11–19.

    Google Scholar 

  5. Miranker, A. and Karplus, M., Protein Struct. Funct. Genet., 11 (1991) 29.

    Google Scholar 

  6. Caflisch, A., Miranker, A. and Karplus, M., J. Med. Chem., 36 (1993) 2142.

    Google Scholar 

  7. Nishibata, Y. and Itai, A., Tetrahedron, 47 (1991) 8985.

    Google Scholar 

  8. Nishibata, Y. and Itai, A., J. Med. Chem., 36 (1993) 2921.

    Google Scholar 

  9. Böhm, H.-J., J. Comput.-Aided Mol. Design, 6 (1992) 61.

    Google Scholar 

  10. Böhm, H.-J., J. Comput.-Aided Mol. Design, 6 (1992) 593.

    Google Scholar 

  11. Böhm, H.-J., In Kubinyi, H. (Ed.) 3D QSAR in Drug Design: Theory, Methods and Applications, ESCOM, Leiden, 1993, pp. 386–405.

    Google Scholar 

  12. Lewis, R.A., Roe, D.C., Huang, C., Ferrin, T.E., Langridge, R. and Kuntz, I.D., J. Mol. Graph., 10 (1992) 66.

    Google Scholar 

  13. Rotstein, S.H. and Murcko, M.A., J. Comput.-Aided Mol. Design, 7 (1993) 23.

    Google Scholar 

  14. Rotstein, S.H. and Murcko, M.A., J. Med. Chem., 36 (1993) 1700.

    Google Scholar 

  15. Gillet, V.J., Johnson, A.P., Mata, P., Sike, S. and Williams, P., J. Comput.-Aided Mol. Design, 7 (1993) 127.

    Google Scholar 

  16. Gillet, V.J., Newell, W., Mata, P., Myatt, G., Sike, S., Zsoldos, Z. and Johnson, A.P., J. Chem. Inf. Comput. Sci., 34 (1994) 207.

    Google Scholar 

  17. Pearlman, D.A. and Murcko, M.A., J. Comput. Chem., 14 (1993) 1184.

    Google Scholar 

  18. Tschinke, V. and Cohen, N.C., J. Med. Chem., 36 (1993) 3863.

    Google Scholar 

  19. Ho, C.W.M. and Marshall, G.R., J. Comput.-Aided Mol. Design, 7 (1993) 623.

    Google Scholar 

  20. Leach, A.R. and Lewis, R.A., J. Comput. Chem., 15 (1994) 233.

    Google Scholar 

  21. Leach, A.R. and Kilvington, S.R., J. Comput.-Aided Mol. Design, 8 (1994) 283.

    Google Scholar 

  22. Eisen, M.B., Wiley, D.C., Karplus, M. and Hubbard, R.E., Protein Struct. Funct. Genet., 19 (1994) 199.

    Google Scholar 

  23. Clark, D.E., Frenkel, D., Levy, S.A., Li, J., Murray, C.W., Robson, B., Waszkowycz, B. and Westhead, D.R., J. Comput.-Aided Mol. Design, 9 (1995) 13.

    Google Scholar 

  24. Waszkowycz, B., Clark, D.E., Frenkel, D., Li, J., Murray, C.W., Robson, B. and Westhead, D.R., J. Med. Chem., 37 (1994) 3994.

    Google Scholar 

  25. Klebe, G., J. Mol. Biol., 237 (1994) 212.

    Google Scholar 

  26. Holland, J.H., Adaptation in Natural and Artificial Systems, University of Michigan Press, Ann Arbor, MI, 1975.

    Google Scholar 

  27. Holland, J.H., Sci. Am., 267 (1992) 44.

    Google Scholar 

  28. Goldberg, D.E., Genetic Algorithms in Search, Optimization and Machine Learning, Addison-Wesley, Reading, MA, 1989.

    Google Scholar 

  29. Davis, L. (Ed.) Handbook of Genetic Algorithms, Van Nostrand Reinhold, New York, NY, 1991.

    Google Scholar 

  30. Lucasius, C.B. and Kateman, G., Chemometrics Intelligent Lab. Syst., 19 (1993) 1.

    Google Scholar 

  31. South, M.C., Wetherill, G.B. and Tham, M.T., J. Appl. Stats., 20 (1993) 153.

    Google Scholar 

  32. Judson, R.S., J. Phys. Chem., 96 (1992) 10102.

    Google Scholar 

  33. Dandekar, T. and Argos, P., Protein Eng., 5 (1992) 637.

    Google Scholar 

  34. Dandekar, T. and Argos, P., J. Mol. Biol., 236 (1994) 844.

    Google Scholar 

  35. Sun, S., Protein Sci., 2 (1993) 762.

    Google Scholar 

  36. Unger, R. and Moult, J., J. Mol. Biol., 231 (1993) 75.

    Google Scholar 

  37. Jones, D.T., Protein Sci., 3 (1994) 567.

    Google Scholar 

  38. Judson, R.S., Jaeger, E.P. and Treasurywala, A.M., J. Mol. Struct. (THEOCHEM), 308 (1994) 191.

    Google Scholar 

  39. McGarrah, D.B. and Judson, R.S., J. Comput. Chem., 14 (1993) 1385.

    Google Scholar 

  40. Judson, R.S., Jaeger, E.P., Treasurywala, A.M. and Peterson, M.L., J. Comput. Chem., 14 (1993) 1407.

    Google Scholar 

  41. Clark, D.E., Jones, G., Willett, P., Kenny, P.W. and Glen, R.C., J. Chem. Inf. Comput. Sci., 34 (1994) 197.

    Google Scholar 

  42. Blommers, M.J.J., Lucasius, C.B., Kateman, G. and Kaptein, R., Biopolymers, 32 (1992) 45.

    Google Scholar 

  43. Judson, R.S., Colvin, M.E., Meza, J.C., Huffer, A. and Gutierrez, D., Int. J. Quantum Chem., 44 (1992) 277.

    Google Scholar 

  44. Tufféry, P., Etchebest, C., Hazout, S. and Lavery, R., J. Comput. Chem., 14 (1993) 790.

    Google Scholar 

  45. Sanderson, P.N., Glen, R.C., Payne, A.W.R., Hudson, B.D., Heide, C., Tranter, G.E., Doyle, P.D. and Harris, C.J., Int. J. Pept. Protein Res., 43 (1994) 588.

    Google Scholar 

  46. Le Grand, S.M. and MerzJr., K.M., J. Global Opt., 3 (1993) 49.

    Google Scholar 

  47. Brodmeier, T. and Pretsch, E., J. Comput. Chem., 15 (1994) 588.

    Google Scholar 

  48. May, A.C.W. and Johnson, M.S., Protein Eng., 7 (1994) 475.

    Google Scholar 

  49. Payne, A.W.R. and Glen, R.C., J. Mol. Graph., 11 (1993) 74.

    Google Scholar 

  50. Rogers, D. and Hopfinger, A.J., J. Chem. Inf. Comput. Sci., 34 (1994) 854.

    Google Scholar 

  51. Leardi, R., J. Chemometrics, 8 (1994) 65.

    Google Scholar 

  52. Walters, D.E. and Hinds, R.M., J. Med. Chem., 37 (1994) 2527.

    Google Scholar 

  53. Blaney, J.M., Dixon, J.S. and Weininger, D., Paper presented at the Molecular Graphics Society Meeting on Binding Sites, York, U.K., March 1993.

  54. Glen, R.C., Paper presented at the Molecular Graphics Society Meeting on Binding Sites, York, U.K., March 1993.

  55. Cramer, R.D., CDA News, 8 (1993) 32.

    Google Scholar 

  56. Radcliffe, N.J., In Männer, R. and Manderick, B. (Eds.) Parallel Problem Solving from Nature, Vol. 2, Elsevier, Amsterdam, 1992, pp. 259–268.

    Google Scholar 

  57. Herndon, W.C., In King, R.B. (Ed.) Chemical Applications of Topology and Graph Theory, Elsevier, Amsterdam, 1983, pp. 231–242.

    Google Scholar 

  58. Morgan, H.L., J. Chem. Doc., 5 (1965) 107.

    Google Scholar 

  59. Moreau, G., Nouv. J. Chim., 4 (1980) 17.

    Google Scholar 

  60. Kuyper, L.F., In Perum, T.J. and Propst, C.L. (Eds.) Computer-Aided Drug Design, Marcel Dekker, New York, NY, 1989, pp. 327–369.

    Google Scholar 

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Authors and Affiliations

  1. Proteus Molecular Design Ltd., Proteus House, Lyme Green Business Park, SK11 0JL, Macclesfield, Cheshire, U.K.

    David R. Westhead, David E. Clark, David Frenkel, Jin Li, Christopher W. Murray, Barry Robson & Bohdan Waszkowycz

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  1. David R. Westhead
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  2. David E. Clark
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  3. David Frenkel
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  4. Jin Li
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  5. Christopher W. Murray
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  6. Barry Robson
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  7. Bohdan Waszkowycz
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Westhead, D.R., Clark, D.E., Frenkel, D. et al. PRO_LIGAND: An approach to de novo molecular design. 3. A genetic algorithm for structure refinement. J Computer-Aided Mol Des 9, 139–148 (1995). https://doi.org/10.1007/BF00124404

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  • DOI: https://doi.org/10.1007/BF00124404

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