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Ligand-Based Approaches to In Silico Pharmacology

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Chemoinformatics and Computational Chemical Biology

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

Abstract

The development of computational methods that can estimate the various pharmacodynamic and pharmacokinetic parameters that characterise the interaction of drugs with biological systems has been a highly pursued objective over the last 50 years. Among all, methods based on ligand information have emerged as simple, yet highly efficient, approaches to in silico pharmacology. With the recent impact on the identification of new targets for known drugs, they are again the focus of attention in chemical biology and drug discovery.

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References

  1. Ekins, S., Mestres, J., and Testa, B. (2008) In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br. J. Pharmacol. 152, 9–20.

    Article  Google Scholar 

  2. Ekins, S., Mestres, J., and Testa, B. (2008) In silico pharmacology for drug discovery: applications to targets and beyond. Br. J. Pharmacol. 152, 21–37.

    Article  Google Scholar 

  3. Hansch, C. and Fujita, T. (1964) Rho-sigma-pi analysis: a method for the correlation of biological activity and chemical structure. J. Am. Chem. Soc. 86, 1616–1626.

    Article  CAS  Google Scholar 

  4. Hansch, C., Hoekman, D., Leo, A., Weininger, D., and Selassie, C. D. (2002) Chem-bioinformatics: comparative QSAR at the interface between chemistry and biology. Chem. Rev. 102, 783–812.

    Article  PubMed  CAS  Google Scholar 

  5. Kurup, A. (2003) C-QSAR: a database of 18,000 QSARs and associated biological and physical data. J. Comput. Aided Mol. Des. 17, 187–196.

    Article  PubMed  CAS  Google Scholar 

  6. Lahana, R. (1999) How many leads from HTS? Drug Discov. Today 4, 447–448.

    Article  PubMed  Google Scholar 

  7. Bajorath, J. (2002) Integration of virtual and high-throughput screening. Nat. Rev. Drug Discov. 1, 882–894.

    Article  PubMed  CAS  Google Scholar 

  8. Willett, P. (2003) Similarity-based approaches to virtual screening. Biochem. Soc. Trans. 31, 603–606.

    Article  PubMed  CAS  Google Scholar 

  9. Lengauer, T., Lemmen, C., Rarey, M., and Zimmermann, M. (2004) Novel technologies for virtual screening. Drug Discov. Today 9, 27–34.

    Article  PubMed  CAS  Google Scholar 

  10. Bleicher, K. H., Böhm, H. -J., Müller, K., and Alanine, A. I. (2003) Hit and lead generation: beyond high-throughput screening. Nat. Rev. Drug Discov. 2, 369–378.

    Article  PubMed  CAS  Google Scholar 

  11. Shoichet, B. K. (2004) Virtual screening of chemical libraries. Nature 432, 862–865.

    Article  PubMed  CAS  Google Scholar 

  12. Mestres, J. (2004) Computational chemogenomic approaches to systematic knowledge-based drug discovery. Curr. Top. Drug Discov. Dev. 7, 304–313.

    CAS  Google Scholar 

  13. Savchuk, N. P., Balakin, K. V., and Tkachenko, S. E. (2004) Exploring the chemogenomic knowledge space with annotated chemical libraries. Curr. Opin. Chem. Biol. 8, 412–417.

    Article  PubMed  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  15. Bajorath, J. (2008) Computational analysis of ligand relationships within target families. Curr. Opin. Chem. Biol. 12, 352–358.

    Article  PubMed  CAS  Google Scholar 

  16. Karelson, M. (2000) Molecular descriptors in QSAR/QSPR. Wiley-VCH: New York.

    Google Scholar 

  17. Todeschini, R. and Consonni, V. (2000) Handbook of molecular descriptors. Wiley-VCH: New York.

    Book  Google Scholar 

  18. Walters, W. P. and Goldman, B. B. (2005) Feature selection in quantitative structure-activity relationships. Curr. Opin. Drug Discov. Devel. 8, 329–333.

    PubMed  CAS  Google Scholar 

  19. Willett, P. (2006) Similarity-based virtual screening using 2D fingerprints. Drug Discov. Today 11, 1046–1053.

    Article  PubMed  CAS  Google Scholar 

  20. Mestres, J., Gregori-Puigjané, E., Valverde, S., and Solé, R. V. (2009) The topology of drug-target interaction networks: implicit dependence on drug properties and protein families. Mol. Biosyst. 5, 1051–1057.

    Article  PubMed  CAS  Google Scholar 

  21. Gregori-Puigjané, E. and Mestres, J. (2006) SHED: Shannon entropy descriptors from topological feature distributions. J. Chem. Inf. Model. 46, 1615–1622.

    Article  PubMed  Google Scholar 

  22. Gregori-Puigjané, E. and Mestres, J. (2008) A ligand-based approach to mining the chemogenomic space of drugs. Comb. Chem. High Throughput Screen. 11, 669–676.

    Article  PubMed  Google Scholar 

  23. Keiser, M. J., Roth, B. L., Armbruster, B. N., Ernsberger, P., Irwin, J. J., and Shoichet, B. K. (2007) Relating protein pharmacology by ligand chemistry. Nat. Biotechnol. 25, 197–206.

    Article  PubMed  CAS  Google Scholar 

  24. Keiser, M. J., Setola, V., Irwin, J. J., Laggner, C., Abbas, A. I., Hufeisen, S. J., Jensen, N. H., Kuijer, M. B., Matos, R. C., Tran, T. B., Whaley, R., Glennon, R. A., Hert, J., Thomas, K. L. H., Edwards, D. D., Shoichet, B. K., and Roth, B. L. (2009) Predicting new molecular targets for known drugs. Nature 462, 175–182.

    Article  PubMed  CAS  Google Scholar 

  25. Campillos, M., Kuhn, M., Gavin, A. -C., Jensen, L. J., and Bork, P. (2008) Drug target identification using side-effect similarity. Science 321, 263–266.

    Article  PubMed  CAS  Google Scholar 

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Acknowledgments

Funding for this research was received from the Instituto de Salud Carlos III and the Spanish Ministerio de Ciencia e Innovación (project BIO2008-02329). GRIB is a node of the Instituto Nacional de Bioinformática (INB) and a member of the RETIC COMBIOMED network.

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© 2011 Humana Press

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Vidal, D., Garcia-Serna, R., Mestres, J. (2011). Ligand-Based Approaches to In Silico Pharmacology. 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_19

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  • DOI: https://doi.org/10.1007/978-1-60761-839-3_19

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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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