Skip to main content
SpringerLink
Log in
Book cover

Bioinformatics and Drug Discovery pp 375–388Cite as

Chemical Database Preparation for Compound Acquisition or Virtual Screening

  • Protocol

Part of the book series: Methods in Molecular Biology ((MIMB,volume 316))

Abstract

Virtual and high-throughput screening are time-saving techniques that have been successfully applied to identify novel chemotypes in biologically active molecules. Both methods require the ability to aptly handle large numbers of chemicals prior to an experiment or acquisition. We describe a step-by-step preparation procedure for handling large collections of existing or virtual compounds prior to virtual screening or acquisition.

This is a preview of subscription content, log in via an institution.

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Springer Nature is developing a new tool to find and evaluate Protocols. Learn more

References

  1. Horvath, D. (1997) A virtual screening approach applied to the search for trypanothione reductase inhibitors. J. Med. Chem. 40, 2412–2423.

    Article  PubMed  CAS  Google Scholar 

  2. Walters, W. P., Stahl, M. T., and Murcko, M. A. (1998) Virtual screening-an overview. Drug Discov. Today 3, 160–178.

    Article  CAS  Google Scholar 

  3. Mestres, J. (2002) Virtual screening: a real screening complement to high-throughput screening. Biochem. Soc. Trans. 30, 797–799.

    Article  PubMed  CAS  Google Scholar 

  4. Oprea, T. I. and Matter, H. (2004) Integrating virtual screening in lead discovery. Curr. Opin. Chem. Biol. 8, 349–358.

    Article  PubMed  CAS  Google Scholar 

  5. Oprea, T. I., Li, J., Muresan, S., and Mattes, K. C. (2003) High throughput and virtual screening: choosing the appropriate leads, in EuroQSAR 2002-Designing Drugs and Crop Protectants: Processes, Problems and Solutions (Ford, M., Livingstone, D., Dearden, J., and Van de Waterbeemd, H., eds.), Blackwell Publishing, New York, pp. 40–47.

    Google Scholar 

  6. Oprea, T. I., Bologa, C. G., Edwards, B. S., Prossnitz, E. A., and Sklar, L. A. (2005) Post-HTS analysis: an empirical compound prioritization scheme. J. Biomol. Screen. 10, in press.

    Google Scholar 

  7. Rishton, G. (2003) Nonleadlikeness and leadlikeness in biochemical screening. Drug Discov. Today 8, 86–96.

    Article  PubMed  CAS  Google Scholar 

  8. McGovern, S. L., Caselli, E., Grigorieff, N., and Shoichet, B. K. (2002) A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening. J. Med. Chem. 45, 1712–1722.

    Article  PubMed  CAS  Google Scholar 

  9. Roche, O., Schneider, P., Zuegge, J., et al. (2002) Development of a virtual screening method for identification of “frequent hitters” in compound libraries. J. Med. Chem. 45, 137–142

    Article  PubMed  CAS  Google Scholar 

  10. Oprea, T. I. (2002) Lead structure searching: Are we looking for the appropriate properties? J. Comput.-Aided Mol. Des. 16, 325–334.

    Article  PubMed  CAS  Google Scholar 

  11. Lipinski, C. A., Lombardo, F., Dominy, B. W., and Feeney, P. J. (1997) Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings. Adv. Drug Deliv. Rev. 23, 3–25.

    Article  CAS  Google Scholar 

  12. Oprea, T. I. (2000) Property distribution of drug-related chemical databases. J. Comput.-Aided Mol. Des. 14, 251–264.

    Article  PubMed  CAS  Google Scholar 

  13. Teague, S. J., Davis, A. M., Leeson, P. D., and Oprea, T. I. (1999) The design of leadlike combinatorial libraries. Angew. Chem. Int. Ed. 38, 3743–3748; German version: Angew. Chem. 111, 3962–3967.

    Google Scholar 

  14. Hann, M. M. and Oprea, T. I. (2004) Pursuing the leadlikeness concept in pharmaceutical research. Curr. Opin. Chem. Biol. 8, 255–263.

    Article  PubMed  CAS  Google Scholar 

  15. Weininger, D. (1988) SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28, 31–36.

    CAS  Google Scholar 

  16. Daylight Toolkit v4.81, Daylight Chemical Information Systems, http://www.daylight.com/.

  17. OEChem Toolkit v1.3, Openeye Scientific Software, http://www.eyesopen.com/.

  18. Open Babel, http://openbabel.sourceforge.net/

  19. Smi2fp_ascii, Daylight Chemical Information Systems, http://www.daylight.com/.

  20. UNITY® 4.4.2, Tripos, http://www.tripos.com/.

  21. MACCSKeys320Generator, Mesa Analytics and Computing LLC, http://www.mesaac.com/.

  22. Barnard, J. M. and Downs, G. M. (1997) Chemical fragment generation and clustering software. J. Chem. Inf. Comput. Sci. 37, 141, 142.

    CAS  Google Scholar 

  23. Durant, J. L., Leland, B. A., Henry, D. R., and Nourse, J. G. (2002) Reoptimization of MDL keys for use in drug discovery. J. Chem. Inf. Comput. Sci. 42, 1273–1280.

    PubMed  CAS  Google Scholar 

  24. MOE: The Molecular Operating Environment from Chemical Computing Group Inc., http://www.chemcomp.com/.

  25. Leo, A. (1993) Calculating log Poct from structures. Chem. Rev. 93, 1281–1306.

    Article  CAS  Google Scholar 

  26. CLogP 4.0, BioByte Corporation, http://www.biobyte.com/.

  27. EPI Suite v3.11, U.S. Environmental Protection Agency, http://www.epa.gov/.

  28. Tetko, I. V. and Tanchuk, V. Y. (2002) Application of associative neural networks for prediction of lipophilicity in ALogPS 2.1 program. J. Chem. Inf. Comput. Sci. 42, 1136–1145.

    PubMed  CAS  Google Scholar 

  29. Barnard clustering, Virtual Computational Chemistry Laboratory, http://www.bci.gb.com/cluster analysis.html.

  30. Cluster Package, Daylight Chemical Information Systems, http://www.daylight.com/.

  31. Measures, Mesa Analytics and Computing LLC, http://www.mesaac.com/.

  32. ChemoMine Consultancy, http://www.chemomine.co.uk/.

  33. iResearchtm Library, ChemNavigator, Inc., http://www.chemnavigator.com/cnc/products/IRL.asp.

  34. Andrews, K. M. and Cramer, R. D. (2000) Toward general methods of targeted library design: topomer shape similarity searching with diverse structures as queries. J. Med. Chem. 43, 1723–1740.

    Article  PubMed  CAS  Google Scholar 

  35. FILTER 2.1, OpenEye Scientific Software, http://www.eyesopen.com/products/applications/filter.html.

  36. Olah, M., Mracec, M., Ostopovici, L., et al. (2004) WOMBAT: World of Molecular Bioactivity, in Cheminformatics in Drug Discovery (Oprea, T. I., ed.), Wiley-VCH, New York, pp. 223–239.

    Google Scholar 

  37. Coats, E. A. (1998) The CoMFA steroids as a benchmark dataset for development of 3DQSAR methods, in 3D QSAR in Drug Design. Volume 3. Recent Advances (Kubinyi, H., Folkers, G., and Martin, Y. C., eds.), Kluwer/ESCOM, Dordrecht, The Netherlands, pp. 199–213.

    Google Scholar 

  38. Oprea, T. I., Olah, M., Ostopovici, L., Rad, R., and Mracec, M. (2003) On the propagation of errors in the QSAR literature, in EuroQSAR 2002-Designing Drugs and Crop Protectants: Processes, Problems and Solutions (Ford, M., Livingstone, D., Dearden, J., and Van de Waterbeemd, H., eds.), Blackwell Publishing, New York, pp. 314–315.

    Google Scholar 

  39. Chem DB, Chemical database management software, TimTec Inc., http://software.timtec. net/ched.htm.

  40. Kenny, P. W. and Sadowski, J. (2004) Structure modification in chemical databases, in Cheminformatics in Drug Discovery (Oprea, T. I., ed.), Wiley-VCH, New York, in press.

    Google Scholar 

  41. FRED, OpenEye Scientific Software, http://www.eyesopen.com/products/applications/fred.html.

  42. Sadowski, J. and Gasteiger, J. (1993) From atoms and bonds to three-dimensional atomic coordinates: automatic model builders. Chem. Rev. 93, 2567–2581.

    Article  CAS  Google Scholar 

  43. LibraryExplorer, Optive Research Inc., http://www.optive.com/productDetailLibraryExplorer.do.

  44. Pearlman, R. S. (1996) Novel Software Tools for Addressing Chemical Diversity. Network Science, http://netsci.org/Science/Combichem/feature08.html.

  45. Pearlman, R. S. and Smith, K. M. (1998) Novel software tools for chemical diversity. Perspect. Drug Discov. 9-11, 339–353.

    Article  Google Scholar 

  46. Johnson, M. A. and Maggiora, G. M. (1990) Concepts and Applications of Molecular Similarity, Wiley-VCH, New York.

    Google Scholar 

  47. Oprea, T. I. (2002) Chemical space navigation in lead discovery. Curr. Opin. Chem. Biol. 6, 384–389.

    Article  PubMed  CAS  Google Scholar 

  48. Todeschini, R. and Consonni, V. (2002) Handbook of Molecular Descriptors, WileyVCH, Weinheim, Germany.

    Google Scholar 

  49. Tanimoto, T. T. (1961)Non-linear model for a computer assisted medical diagnostic procedure. Trans. NY Acad. Sci. Ser. 2(23), 576–580.

    Google Scholar 

  50. Tversky, A. (1977) Features of similarity. Psychol. Rev. 84, 327–352.

    Article  Google Scholar 

  51. Willett, P. (1987) Similarity and Clustering Techniques in Chemical Information Systems, Research Studies Press, Letchworth, England.

    Google Scholar 

  52. Willett, P. (2000) Chemoinformatics-similarity and diversity in chemical libraries. Curr. Opin. Biotechnol. 11, 85–88.

    Article  PubMed  CAS  Google Scholar 

  53. Lewis, R. A., Pickett, S. D., and Clark, D. E. (2000) Computer-aided molecular diversity analysis and combinatorial library design. Rev. Comput. Chem. 16, 1–51.

    Article  CAS  Google Scholar 

  54. Martin, Y. C. (2001) Diverse viewpoints on computational aspects of molecular diversity. J. Comb. Chem. 3, 231–250.

    Article  PubMed  CAS  Google Scholar 

  55. Linusson, A., Gottfries, J., Lindgren, F., and Wold, S. (2000) Statistical molecular design of building blocks for combinatorial chemistry. J. Med. Chem. 43, 1320–1328.

    Article  PubMed  CAS  Google Scholar 

  56. Eriksson, L., Johansson, E., Kettaneh-Wold, N., Wikstrom, C., and Wold, S. (2000) Design of Experiments: Principles and Applications, Umetrics Academy, Umea, Sweden.

    Google Scholar 

  57. Kubinyi, H. (1999) Chance favors the prepared mind-from serendipity to rational drug design. J. Rec. Signal Transd. Res. 19, 15–39.

    Article  CAS  Google Scholar 

  58. Bostrom, J., Norrby, P.-O., and Liljefors, T. (1998) Conformational energy penalties of protein-bound ligands. J. Comput.-Aided Mol. Des. 12, 383–396.

    Article  PubMed  CAS  Google Scholar 

Download references

Acknowledgment

These studies were supported in part by New Mexico Tobacco Settlement funds.

Author information

Authors and Affiliations

  1. Division of Biocomputing, Department of Biochemistry and Molecular Biology, Univerisity of New Mexico, Albuquerque, NM

    Cristian G. Bologa, Marius M. Olah & Tudor I. Oprea

Authors
  1. Cristian G. Bologa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marius M. Olah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tudor I. Oprea
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Pathology, University of New Mexico School of Medicine, Albuquerque, NM

    Richard S. Larson

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Humana Press Inc.

About this protocol

Cite this protocol

Bologa, C.G., Olah, M.M., Oprea, T.I. (2006). Chemical Database Preparation for Compound Acquisition or Virtual Screening. In: Larson, R.S. (eds) Bioinformatics and Drug Discovery. Methods in Molecular Biology, vol 316. Humana Press. https://doi.org/10.1385/1-59259-964-8:375

Download citation

  • DOI: https://doi.org/10.1385/1-59259-964-8:375

  • Publisher Name: Humana Press

  • Print ISBN: 978-1-58829-346-6

  • Online ISBN: 978-1-59259-964-6

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics

Search

Navigation