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Molecular Test Systems for Computational Selectivity Studies and Systematic Analysis of Compound Selectivity Profiles

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Chemoinformatics and Computational Chemical Biology

Part of the book series: Methods in Molecular Biology ((MIMB,volume 672))

Abstract

For chemical genetics and chemical biology, an important task is the identification of small molecules that are selective against individual targets and can be used as molecular probes for specific biological functions. To aid in the development of computational methods for selectivity analysis, molecular benchmark systems have been developed that capture compound selectivity data for pairs of targets. These molecular test systems are utilized for “selectivity searching” and the analysis of structure–selectivity relationships. Going beyond binary selectivity sets focusing on target pairs, a methodological framework, Molecular Formal Concept Analysis (MolFCA), is described for the definition and systematic mining of compound selectivity profiles.

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References

  1. Jacoby, E. (2006) Chemogenomics: drug discovery’s panacea? Mol. Biosyst. 2, 218–220.

    Article  PubMed  CAS  Google Scholar 

  2. Spring, D. R. (2005) Chemical genetics to chemical genomics: small molecules offer big insights. Chem. Soc. Rev. 34, 472–482.

    Article  PubMed  CAS  Google Scholar 

  3. Bajorath, J. (2008) Computational approaches in chemogenomics and chemical biology: current and future impact on drug discovery. Expert. Opin. Drug Discov. 3, 1371–1376.

    Article  CAS  Google Scholar 

  4. Tan, D. S. (2005) Diversity-oriented synthesis: exploring the intersections between chemistry and biology. Nat. Chem. Biol 1, 74–84.

    Article  PubMed  CAS  Google Scholar 

  5. Stockwell, B. R. (2004) Exploring biology with small organic molecules. Nature 432, 846–854.

    Article  PubMed  CAS  Google Scholar 

  6. Bredel, M., and Jacoby, E. (2004) Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat. Rev. Genet. 5, 262–275.

    Article  PubMed  CAS  Google Scholar 

  7. Paolini, G. V., Shapland, R. H. B., van Hoorn, W. P., Mason, J. S., and Hopkins, A. L. (2006) Global mapping of pharmacological space. Nat. Biotechnol. 24, 805–815.

    Article  PubMed  CAS  Google Scholar 

  8. Yildirim, M. A., Goh, K., Cusick, M. E., Barabási, A., and Vidal, M. (2007) Drug-target network. Nat. Biotechnol. 25, 1119–1126.

    Article  PubMed  CAS  Google Scholar 

  9. Cheng, Y., and Prusoff, W. H. (1973) Relationship between the inhibition constant (K1) and the concentration of inhibitor which causes 50 per cent inhibition (I50) of an enzymatic reaction. Biochem. Pharmacol. 22, 3099–3108.

    Article  PubMed  CAS  Google Scholar 

  10. Stumpfe, D., Ahmed, H. E. A., Vogt, I., and Bajorath, J. (2007) Methods for computer-aided chemical biology. Part 1: design of a benchmark system for the evaluation of compound selectivity. Chem. Biol. Drug Des. 70, 182–194.

    Article  PubMed  CAS  Google Scholar 

  11. Stumpfe, D., Geppert, H., and Bajorath, J. (2008) Methods for computer-aided chemical biology. Part 3: analysis of structure-selectivity relationships through single- or dual-step selectivity searching and Bayesian classification. Chem. Biol. Drug Des. 71, 518–528.

    Article  PubMed  CAS  Google Scholar 

  12. Johnson, M. A., and Maggiora, G. M. (1990) Concepts and applications of molecular similarity. Wiley, New York.

    Google Scholar 

  13. Kubinyi, H. (1998) Similarity and dissimilarity – a medicinal chemist’s view. Perspect. Drug Discov. Des. 11, 225–252.

    Article  Google Scholar 

  14. Peltason, L., and Bajorath, J. (2007) Molecular similarity analysis uncovers heterogeneous structure-activity relationships and variable activity landscapes. Chem. Biol. 14, 489–497.

    Article  PubMed  CAS  Google Scholar 

  15. Vogt, I., Stumpfe, D., Ahmed, H. E. A., and Bajorath, J. (2007) Methods for computer-aided chemical biology. Part 2: evaluation of compound selectivity using 2D molecular fingerprints. Chem. Biol. Drug Des. 70, 195–205.

    Article  PubMed  CAS  Google Scholar 

  16. Wassermann, A. M., Geppert, H., and Bajorath, J. (2009) Searching for target-selective compounds using different combinations of multiclass support vector machine ranking methods, kernel functions, and fingerprint descriptors. J. Chem. Inf. Model. 49, 582–592.

    Article  PubMed  CAS  Google Scholar 

  17. Vogt, I., Ahmed, H. E. A., Auer, J., and Bajorath, J. (2008) Exploring structure-selectivity relationships of biogenic amine GPCR antagonists using similarity searching and dynamic compound mapping. Mol. Divers. 12, 25–40.

    Article  PubMed  CAS  Google Scholar 

  18. Priss, U. (2006) Formal concept analysis in information science. Annu. Rev. Inform. Sci. Technol. 40, 521–543.

    Article  Google Scholar 

  19. Lounkine, E., Auer, J., and Bajorath, J. (2008) Formal concept analysis for the identification of molecular fragment combinations specific for active and highly potent compounds. J. Med. Chem. 51, 5342–5348.

    Article  PubMed  CAS  Google Scholar 

  20. Lounkine, E., Stumpfe, D., and Bajorath, J. (2009) Molecular Formal Concept Analysis for compound selectivity profiling in biologically annotated databases. J. Chem. Inf. Model. 49, 1359–1368.

    Article  PubMed  CAS  Google Scholar 

  21. Duvic, M., Talpur, R., Ni, X., Zhang, C., Hazarika, P., Kelly, C., Chiao, J. H., Reilly, J. F., Ricker, J. L., Richon, V. M., and Frankel, S. R. (2007) Phase 2 trial of oral vorinostat (suberoylanilide hydroxamic acid, SAHA) for refractory cutaneous T-cell lymphoma (CTCL). Blood 109, 31–39.

    Article  PubMed  CAS  Google Scholar 

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© 2011 Humana Press

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Stumpfe, D., Lounkine, E., Bajorath, J. (2011). Molecular Test Systems for Computational Selectivity Studies and Systematic Analysis of Compound Selectivity Profiles. In: Bajorath, J. (eds) Chemoinformatics and Computational Chemical Biology. Methods in Molecular Biology, vol 672. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-60761-839-3_20

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  • DOI: https://doi.org/10.1007/978-1-60761-839-3_20

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  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-60761-838-6

  • Online ISBN: 978-1-60761-839-3

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