Skip to main content

Optimization of the drug-likeness of chemical libraries

  • Chapter
  • 186 Accesses

Summary

A scoring scheme for the classification of molecules into drugs and non-drugs was established. It was set up by using atom type descriptors for encoding the molecular structures and by training a feed-forward neural network for classifying the molecules. The approach was parameterized by using large databases of drugs and non-drugs ? the Available Chemicals Directory (ACD) with 169 331 molecules and the World Drug Index (WDI) with 38 416 molecules. It was able to reveal features in the molecular descriptors that either qualify or disqualify a molecule for being a drug. The method classified about 80% of the ACD and the WDI correctly. It was extended to the application for crop protection compounds and can be used to prioritize compounds for synthesis, purchase, or biological testing. An enhancement allows to optimize the drug character of combinatorial libraries.

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

Buying options

Chapter
USD   29.95
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

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Warr, W.A., J. Chem. Inf. Comput. Sci., 37 (1997) 134.

    Article  CAS  Google Scholar 

  2. Gillet, V.J. and Bradshaw, J., J. Chem. Inf. Comput. Sci., 38 (1998) 165.

    Article  PubMed  CAS  Google Scholar 

  3. Ajay, Walters W.P. and Murcko, M.A., J. Med. Chem., 41 (1998) 3314.

    Article  PubMed  CAS  Google Scholar 

  4. Sadowski, J. and Kubinyi, H., J. Med. Chem., 41 (1998) 3325.

    Article  PubMed  CAS  Google Scholar 

  5. Lipinski, C.A., Lombardo, F., Dominy, B.W. and Feeney, P.J., Adv. Drug Delivery Rev., 23 (1997) 3.

    Article  CAS  Google Scholar 

  6. Ghose, A.K. and Crippen, G.M., J. Med. Chem., 28 (1985) 333.

    Article  PubMed  CAS  Google Scholar 

  7. Ghose, A.K. and Crippen, G.M., J. Comput. Chem., 7 (1986) 565.

    Article  CAS  Google Scholar 

  8. Ghose, A.K. and Crippen, G.M., J. Chem. Inf. Comput. Sci., 27 (1987) 21.

    Article  PubMed  CAS  Google Scholar 

  9. Ghose, A.K., Pritchett, A. and Crippen, G.M., J. Comput. Chem., 9 (1988) 80.

    Article  CAS  Google Scholar 

  10. Visnavadhan, V.N., Ghose, A.K., Revankar, G.R. and Robins, R.K., J. Chem. Inf. Comput. Sci., 29 (1989) 163.

    Google Scholar 

  11. Ghose, A.K., Viswanadhan, V.N. and Wendoloski, J.J., J. Phys. Chem. A, 102 (1998) 3762.

    Article  CAS  Google Scholar 

  12. SNNS: Stuttgart Neural Network Simulator; Version 4.0, University of Stuttgart, 1995.

    Google Scholar 

  13. WDI (World Drug Index), Version 2/96, Derwent Information, 1996.

    Google Scholar 

  14. ACD (Available Chemicals Directory), Version 2/96, MDL Information Systems, 1996.

    Google Scholar 

  15. Gillet, V.J., Willett, P., Bradshaw, J. and Green, D.V.S., J. Chem. Inf. Comput. Sci., 39 (1999) 169.

    Article  CAS  Google Scholar 

  16. Weber, L., Wallbaum, S., Broger, C. and Gubernator, K., Angew. Chem., 107 (1995) 2452.

    Google Scholar 

  17. Singh, J., Ator, M.A., Jaeger, E.P., Allen, M.P., Whipple, D.A., Soloweij, J.E., Chowdhary, S. and Treasurywala, A.M., J. Am. Chem. Soc., 118 (1996) 1669.

    CAS  Google Scholar 

  18. Brown, R.D. and Martin, Y.C., J. Med. Chem., 40 (1997) 2304.

    PubMed  CAS  Google Scholar 

  19. Sheridan, R.P. and Kearsley, S.K., J. Chem. Inf. Comput. Sci., 35 (1995) 310.

    Article  CAS  Google Scholar 

  20. Goldberg, D.E., Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA, 1989.

    Google Scholar 

  21. Genesis program by J.J. Grefenstette, Naval Research Laboratory, Washington, DC, 1987.

    Google Scholar 

  22. Gillet, V.J., Willett, P. and Bradshaw, J., J. Chem. Inf. Comput. Sci., 37 (1997) 731.

    Article  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2000 KluwerAcademic Publishers

About this chapter

Cite this chapter

Sadowski, J. (2000). Optimization of the drug-likeness of chemical libraries. In: Klebe, G. (eds) Virtual Screening: An Alternative or Complement to High Throughput Screening?., vol 20. Springer, Dordrecht. https://doi.org/10.1007/0-306-46883-2_2

Download citation

  • DOI: https://doi.org/10.1007/0-306-46883-2_2

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-0-7923-6633-1

  • Online ISBN: 978-0-306-46883-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics