Advertisement

Journal of Mathematical Chemistry

, Volume 39, Issue 1, pp 107–118 | Cite as

Putting molecular similarity into context: asymmetric indices for field-based similarity measures

  • Jordi MestresEmail author
  • Gerald M. Maggiora
Article

Abstract

Some of the most widely used indices in molecular similarity searching are intrinsically symmetric in nature, meaning that each molecule under comparison contributes equally to the similarity index. For example, the Carbó and the Hodgkin–Richards similarity indices are respectively, related to the geometric and arithmetic averages of the molecular self-similarities. This work introduces the asymmetric forms of an entire family of field-based molecular similarity indices. By incorporating a weighted contribution of each molecule into the similarity index, the newly obtained asymmetric forms allow for measuring and modulating the similarity of one molecule in the context of another and thus have the potential of alleviating the size dependency often observed in chemical similarity searching

Keywords

asymmetric similarity molecular fields similarity searching virtual screening 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Tversky A. (1977). Features of similarity. Psychol. Rev. 84:327–352CrossRefGoogle Scholar
  2. 2.
    Holman E.W. (1979). Monotonic models for asymmetric proximities. J. Math. Psychol. 20:1–15CrossRefGoogle Scholar
  3. 3.
    Nosofsky R.M. (1991). Stimulus bias, asymmetric similarity, and classification. Cogn. Psychol. 23:91–140CrossRefGoogle Scholar
  4. 4.
    Johannesson M. (2000). Modelling asymmetric similarity with prominence, Br. J. Math. Stat. Psychol. 53:121–139CrossRefGoogle Scholar
  5. 5.
    G.M. Maggiora and V. Shanmugasundaram, Molecular Similarity Measures, in Chemoinformatics: Concepts, Methods, and Tools for Drug Discovery, ed. J. Bajorath, Methods Mol Biol., vol. 275, pp. 1–50.Google Scholar
  6. 6.
    Willett P. (1998). Chemical similarity searching. J. Chem. Inf. Comput. Sci. 38:983–996Google Scholar
  7. 7.
    J. Bradshaw, Introduction to Tversky similarity measure. Presented at the 11th Annual Daylight MUG Meeting, Laguna Beach (CA), February 1997. http://www.daylight.com/meetings/mug97/Bradshaw/MUG97/tv_tversky.html.Google Scholar
  8. 8.
    G.M. Maggiora, J. Mestres, T.R. Hagadone and M.S. Lajiness, Asymmetric similarity and molecular diversity, Presented at the 213th National Meeting of the American Chemical Society, San Francisco (CA), April 1997.Google Scholar
  9. 9.
    Holliday J.D., Salim N., Whittle M., Willett P. (2003). Analysis and display of the size dependence of chemical similarity coefficients. J. Chem. Inf. Comput. Sci. 43:819–828CrossRefGoogle Scholar
  10. 10.
    Cramer R.D. III., Patterson D.E., Bunce J.D. (1988). Comparative molecular field analysis (CoMFA) .1. Effect of shape on binding of steroids to carrier proteins. J. Am. Chem. Soc. 110:5959–5967CrossRefGoogle Scholar
  11. 11.
    Klebe G., Abraham U., Mietzner T. (1994). Molecular similarity indices in a comparative analysis (CoMSIA) of drug molecules to correlate and predict their biological activity. J. Med. Chem. 37:4130–4146CrossRefGoogle Scholar
  12. 12.
    Lemmen C., Lengauer T. (2000). Computational methods for the structural alignment of molecules. J. Comput. Aided Mol. Design 14:215–232CrossRefGoogle Scholar
  13. 13.
    Carbó R., Besalú E., Amat L., Fradera X. (1996). On quantum molecular similarity measures (QMSM) and indices (QMSI). J. Math. Chem. 19:47–56CrossRefGoogle Scholar
  14. 14.
    Maggiora G.M., Petke J.D., Mestres J. (2002). A general analysis of field-based molecular similarity indices. J. Math. Chem. 31:251–270CrossRefGoogle Scholar
  15. 15.
    Carbó R., Leyda L., Arnau M. (1980). How similar is a molecule to another? An electron density measure of similarity between two molecular structures. Intl. J. Quantum Chem. 17:1185–1189CrossRefGoogle Scholar
  16. 16.
    Hodgkin E.E., Richards W.G. (1987). Molecular similarity based on electrostatic potential and electric field. Intl. J. Quantum Chem. Quantum Biol. Symp. 14:105–110CrossRefGoogle Scholar
  17. 17.
    Petke J.D. (1993). Cumulative and discrete similarity analysis of electrostatic potentials and fields. J. Comput. Chem. 14: 928–933CrossRefGoogle Scholar
  18. 18.
    Klir G.J., Yuan B. (1995). Fuzzy Sets and Fuzzy Logic – Theory and Applications. Prentice-Hall, Upper Saddle River, NJ, Chapter 3Google Scholar

Copyright information

© Springer Science+Business Media Inc 2005

Authors and Affiliations

  1. 1.Chemogenomics Laboratory, Research Unit on Biomedical InformaticsInstitut Municipal d’Investigació Mèdica and Universitat Pompeu FabraBarcelona (Catalonia)Spain
  2. 2.Department of Pharmacology and ToxicologyUniversity of Arizona College of PharmacyTucsonUSA

Personalised recommendations