Advertisement

Geometric manipulation of flexible ligands

  • Paul W. Finn
  • Dan Halperin
  • Lydia E. Kavraki
  • Jean-Claude Latombe
  • Rajeev Motwani
  • Christian Shelton
  • Suresh Venkatasubramanian
Submitted Contributions
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1148)

Abstract

In recent years an effort has been made to supplement traditional methods for drug discovery by computer-assisted “structure-based design.” The structure-based approach involves (among other issues) reasoning about the geometry of drug molecules (or ligands) and about the different spatial conformations that these molecules can attain. This is a preliminary report on a set of tools that we are devising to assist the chemist in the drug design process. We describe our work on the following three topics: (i) geometric data structures for representing and manipulating molecules; (ii) conformational analysis—searching for low-energy conformations; and (iii) pharmacophore identification—searching for common features among different ligands that exhibit similar activity.

Keywords

Molecular Surface Conformational Search Hard Sphere Model Atom Sphere Common Substructure 
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.
    T. Akutsu, M. Halldorsson, On the approximation of largest common point sets, Proc. of the International Symposium on Algorithms and Computations, Springer-Verlag Lecture Notes on Computer Science 834 (1987), pp. 405–413.Google Scholar
  2. 2.
    H. Alt, K. Melhorn K., H. Wagener and E. Welzl, Congruence, Similarity, and Symmetries of Geometric objects, Discrete Comput. Geom. 3 (1988), pp. 237–256.CrossRefGoogle Scholar
  3. 3.
    L.M. Balbes, S.W. Mascarella, D.B. Boyd, A perspective of modern methods for computer aided drug design, Reviews in Computational Chemistry 5 (1994), VCH Publishers Inc., pp. 337–379.Google Scholar
  4. 4.
    D.B. Boyd, Compendium of molecular modeling software, Reviews in Computational Chemistry 4 (1993), VCH Publishers Inc., pp. 229–257.Google Scholar
  5. 5.
    R.P. Brent, Algorithms for finding zeros and extrema of functions without calculating derivatives, Ph.D. Thesis, Stanford University, 1971.Google Scholar
  6. 6.
    Brint, A. T. and Willet P, Algorithms for the identification of three-dimensional maximal common substructures. J. Chem. Inf. Comput. Sci. 27 (1987), pp. 152–158.CrossRefGoogle Scholar
  7. 7.
    C.E. Bugg, W.M. Carson, and J.A. Montgomery, Drugs by design, Scientific American, December 1993, pp. 92–98.Google Scholar
  8. 8.
    D.E. Clark, G. Jones, P. Willet, P.W. Kenny, and R.C. Glen, Pharmacophoric pattern matching in files of three-dimensional chemical structures: Comparison of conformational searching algorithms for flexible searching, J. Chem. Inf. Comput. Sci. 34 (1994), pp. 197–206.CrossRefGoogle Scholar
  9. 9.
    M.L. Connolly, Solvent-accessible surfaces of proteins and nucleic acids, Science 221 (1983), pp. 709–713.PubMedGoogle Scholar
  10. 10.
    M.L. Connolly, Analytical molecular surface calculation, J. of Applied Crystallography 16 (1983), pp. 548–558.CrossRefGoogle Scholar
  11. 11.
    H. Edelsbrunner, M. Facello, P. Fu, and J. Liang, Measuring proteins and voids in proteins, Technical Report, HKUST-CS94-19, Department of Computer Science, Hong Kong University of Science and Technology, 1994.Google Scholar
  12. 12.
    R.C. Glen, G.R. Martin, A.P. Hill, R.M. Hyde, P.M. Woollard, J. Salmon, J. Buckingham and A. Robertson, Computer-aided design and synthesis of 5-substituted Tryptamins and their pharmacology at the 5-HT Receptor: discovery of compounds with potential anti-migraine properties, J. Med. Chem., 38 (1995), pp. 3566–3580.CrossRefPubMedGoogle Scholar
  13. 13.
    L.J. Guibas and J. Stolfi, Primitives for the manipulation of general subdivisions and the computation of Voronoi diagrams, ACM Transactions on Graphics, 4 (1985), pp. 74–123.CrossRefGoogle Scholar
  14. 14.
    D. Gusfield and R. W. Irving, The stable marriage problem: structure and algorithms. MIT Press, Cambridge, 1989.Google Scholar
  15. 15.
    D. Halperin, J.-C. Latombe and R. Motwani, Dynamic maintenance of kinematic structures, Manuscript, 1996.Google Scholar
  16. 16.
    D. Halperin and M.H. Overmars, Spheres, molecules, and hidden surface removal, Proc. 10th ACM Symposium on Computational Geometry, Stony Brook, 1994, pp. 113–122.Google Scholar
  17. 17.
    L.E. Kavraki, Random networks in configuration space for fast path planning, Ph.D. Thesis, Stanford, 1995.Google Scholar
  18. 18.
    L. Kaufman and P.J. Rousseeuw, Finding groups in data an introduction to cluster analysis, Wiley, NY, 1990.Google Scholar
  19. 19.
    S. Khanna, R. Motwani, and Frances F. Yao, Approximation algorithms for the largest common subtree problem, Report No. STAN-CS-95-1545, Department of Computer Science, Stanford University (1995).Google Scholar
  20. 20.
    G. Klebe and T. Mietzner, A fast and efficient method to generate biologically relevant conformations, J. of Computer Aided Molecular Design 8 (1994), pp. 583–606.CrossRefGoogle Scholar
  21. 21.
    A.R. Leach, A survey of methods for searching the conformational space of small and medium size molecules, Reviews in Computational Chemistry 2 (1991), VCH Publishers Inc., pp. 1–55.Google Scholar
  22. 22.
    T. Lengauer, Algorithmic research problems in molecular bioinformatics, IEEE Proc. of the 2nd Israeli Symposium on the Theory of Computing and Systems, 1993, pp. 177–192.Google Scholar
  23. 23.
    B. Lee and F.M. Richards, The interpretation of protein structure: Estimation of static accessibility, J. of Molecular Biology 55 (1971), pp. 379–400.Google Scholar
  24. 24.
    Y.C. Martin, M.G. Bures, E.A. Danaher, J. DeLazzer, I. Lico, and P.A. Pavlik, A fast new approach to pharmacophore mapping and its application to dopaminergic and benzodiazepine agonists, J. of Computer-Aided Molecular Design 7 (1993), pp. 83–102.CrossRefGoogle Scholar
  25. 25.
    P.G. Mezey, Molecular surfaces, in Reviews in Computational Chemistry, Vol. I, K.B. Lipkowitz and D.B. Boyd, Eds., VCH Publishers, 1990, pp. 265–294.Google Scholar
  26. 26.
    K. Mulmuley, Computational Geometry: An Introduction Through Randomized Algorithms, Prentice Hall, New York, 1993.Google Scholar
  27. 27.
    R. Norel, D. Fischer, H.J. Wolfson, and R. Nussinov, Molecular surface recognition by a computer vision-based technique, Protein Engineering 7 (1994), pp. 39–46.PubMedGoogle Scholar
  28. 28.
    F.M. Richards, Areas, volumes, packing, and protein structure, in Annual Reviews of Biophysics and Bioengineering 6 (1977), pp. 151–176.CrossRefGoogle Scholar
  29. 29.
    D.A. Pierre, Optimization theory with applications, Dover, NY, 1986.Google Scholar
  30. 30.
    A. Smellie, S.D. Kahn, and S.L. Tieg, Analysis of conformational coverage: 1. Validation and estimation of coverage, J. Chem. Inf. Comput. Sci, 35(1995), pp. 285–294.CrossRefGoogle Scholar
  31. 31.
    Y. Takahashi, Y. Satoh and S. Sasaki, Recognition of largest common structural fragment among a variety of chemical structures, Analytical Sciences 3 (1987), pp. 23–28.Google Scholar
  32. 32.
    Tripos Associates Inc., Sybyl Manual, St. Louis, MO.Google Scholar
  33. 33.
    H. J. Wolfson, Model-based object recognition by geometric hashing, Proc. of the 1st European Conference on Computer Vision (1990), pp. 526–536.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1996

Authors and Affiliations

  • Paul W. Finn
    • 1
  • Dan Halperin
    • 2
  • Lydia E. Kavraki
    • 2
  • Jean-Claude Latombe
    • 2
  • Rajeev Motwani
    • 2
  • Christian Shelton
    • 2
  • Suresh Venkatasubramanian
    • 2
  1. 1.Pfizer Central Research, SandwichKentUK
  2. 2.Department of Computer ScienceStanford UniversityStanfordUSA

Personalised recommendations