Molecular Similarity

  • W. Graham Richards
  • Daniel D. Robinson
Part of the The IMA Volumes in Mathematics and its Applications book series (IMA, volume 108)

Abstract

Molecular similarity aims to give a quantitative answer to the question of how similar two given molecules are. Such indices are of use in drug design as aids to the creation of molecular mimics and in structure-activity studies or measures of molecular diversity. Similarity is most often computed in terms of molecular shape or electrostatic potential.

The advent of combinatorial techniques and the use of high throughput synthesis have created a need for ever faster methods of computation. Numerical calculation has been superceded by analytical evaluation of integrals, but even faster methods are urgently needed. This is especially so if we can ever hope to take thousands of molecules and calculate the similarity between all pairs.

A promising technique is to use two-dimensional molecular representations and to utilise methodologies perfected in optical character recognition.

Keywords

Expense Macromolecule Triazine 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    R. Carbo, L. Leyda and M. Arnau. An electron density measure of the similarity between two compounds. Int. J. Quantum Chem. 17, 1185–1189 (1980).CrossRefGoogle Scholar
  2. [2]
    E.E. Hodgkin and W.G. Richards. Molecular similarity based on electrostatic potential and electric field. Int. J. Quantum Chem. Quantum Biol. Symp. 14, 105–110 (1987).CrossRefGoogle Scholar
  3. [3]
    A.C. Good, E.E. Hodgkin and W.G. Richards. The utilization of Gaussian functions for the rapid evaluation of molecular similarity. J. Chem. Inf. Comput. Sci. 33, 188–191 (1992).Google Scholar
  4. [4]
    A. Szabo and N.S. Ostlund. Modern Quantum Chemistry. Macmillan, Basingstoke. 1982. 410–412.Google Scholar
  5. [5]
    A.M. Meyer and W.G. Richards. Similarity of molecular shape. J. Comput. Aided. Mole.Des. 5, 426–439 (1991).Google Scholar
  6. [6]
    J. A. Pople et al. Gaussian94. Gaussian Inc. Pitsburgh, PA.Google Scholar
  7. [7]
    P.E. Gill and W. Murray. Algorithms for the solution of non-linear least squares problems. J. Numer. Anal. 15, 977–982 (1978).MathSciNetMATHCrossRefGoogle Scholar
  8. [8]
    A.C. Good and W.G. Richards. Rapid evaluation of shape similarity using Gaussian functions. J. Chem. Inf. Comput. Sci. 33, 112–116 (1993).CrossRefGoogle Scholar
  9. [9]
    A.J. Hopfinger. A QSAR investigation of DHFR inhibition by Bakers triazines based upon molecular shape analysis. J. Am. Chem. Soc. 102, 7196–7206. (1980).CrossRefGoogle Scholar
  10. [10]
    A.J. Hopfinger. Theory and analysis of molecular potential energy fields in molecular shape analysis: a QSAR study of 2,4-diamino 5-benzylpyrimidines as DHFR inhibitors. J. Med. Chem. 26, 990–996. (1983).CrossRefGoogle Scholar
  11. [11]
    R.D. Cramer, D. E. Patterson and J. D. Bunce. Comparative molecular field analysis (CoMFA) Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 110, 5959–5967 (1988).CrossRefGoogle Scholar
  12. [12]
    J. W. McFarland. Comparative molecular field analysis of anticoccidial triazines. J. Med. Chem. 35, 2543–2550. (1992).CrossRefGoogle Scholar
  13. [13]
    A.C. Good, Sung-Sau So and W.G. Richards. Structure activity relationships from molecular similarity matrices. J. Med. Chem. 36, 433–438 (1993).CrossRefGoogle Scholar
  14. [14]
    A.C. Good, S. J. Peterson and W. G. Richards. QSARs from similarity matrices. Technique validation and application in the comparison of different similarity evaluation methods. J. Med. Chem. 36, 2929–2937 (1993).CrossRefGoogle Scholar
  15. [15]
    T.W. Barlow and W. G. Richards. A novel representation of protein structure. J. Molec. Graph. 13, 373–376 (1996).CrossRefGoogle Scholar
  16. [16]
    M. K. Hu. Visual pattern recognition by moment invariants. IRE Transactions on Information Theory. 1962 179–187.Google Scholar
  17. [17]
    A. Papoulis. Probability, Random variables and Stochastic processes. McGraw-Hill. New York, 1965.MATHGoogle Scholar
  18. [18]
    J. W. Cooley and J. W. Tukey. An algorithm for machine calculation of complex Fourier series. Math. Computation. 19, 97–301 (1956).MathSciNetGoogle Scholar
  19. [19]
    D. D. Robinson, T. W. Barlow and W. G. Richards. The utilization of reduced dimensional representations of molecular structure for rapid molecular similarity calculations. J. Chem. Inf. Comput. Sci. in press.Google Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • W. Graham Richards
    • 1
  • Daniel D. Robinson
    • 2
  1. 1.New Chemistry LaboratoryOxford UniversityOxfordUK
  2. 2.Department of ChemistryOxford UniversityOxfordUK

Personalised recommendations