Molecular Diversity

, Volume 10, Issue 3, pp 377–388 | Cite as

Leadlikeness and structural diversity of synthetic screening libraries

Full–length Paper

Summary

High program failure rates in the pharmaceutical industry have prompted the development of predictive software that can profile compound libraries as being ‘druglike’ (resembling existing drugs) and/or ‘leadlike’ (possessing the structural and physicochemical profile of a quality lead). In recent years, these two notions prompted pharmaceutical companies to clean up their corporate libraries of screening compounds. In order to maintain and expand the size and diversity of these corporate libraries, pharmaceutical companies still continue to add compounds to these, mainly by the acquisition of screening libraries. In this paper, we have analyzed 45 commercially available libraries, offered by suppliers of screening chemistry, for leadlikeness and diversity of the offered structures. To this end we have used a set of structural and physicochemical filters for leadlikeness that was developed in-house. These 45 supplier libraries contained a total of 5.3 million structures, of which 49% (2,592,778 structures) turned out to be unique, and only 12% (677,328 structures) were found to be both unique and leadlike. A diversity analysis revealed that big differences exist between the various offered libraries.

Key words

diversity druglikeness drug development library filtering high throughput screening lead identification leadlikeness physicochemical properties property profile screening library 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Milne, G.M.,Pharmaceutical productivity — The Imperative for New Paradigms, Ann. Rep. Med. Chem., 38 (2002) 383–396.CrossRefGoogle Scholar
  2. 2.
    Lipinski, C.A., Lombardo, F., Dominy, B.W. and Feeny, P.J.,Experimental and computational approaches to estimate solubility and permeability in drug discovery and development settings, Adv. Drug Delivery Rev., 23 (1997) 3–25.CrossRefGoogle Scholar
  3. 3.
    Proudfoot, J.R.,Drugs, leads, and drug-likeness: An analysis of some recently launched drugs, Bioorg. Med. Chem. Lett., 12 (2002) 1647–1650.PubMedCrossRefGoogle Scholar
  4. 4.
    Teague, S.J., Davis, A.M., Leeson, P.D. and Oprea, T.I.,The design of leadlike combinatorial libraries, Angew. Chem. Int. Ed., 38 (1999) 3743–3748.CrossRefGoogle Scholar
  5. 5.
    Hann, M.M., Leach, A.R. and Harper, G.,Molecular complexity and its impact on the probability of finding leads for drug discovery, J. Chem. Inf. Comput. Sci., 41 (2001) 856–864.PubMedCrossRefGoogle Scholar
  6. 6.
    Oprea, T.I., Davis, A.M., Teague, S.J. and Leeson, P.D.,Is there a difference between leads and drugs? A historical perspective, J. Chem. Inf. Comput. Sci., 41 (2001) 1308–1315.PubMedCrossRefGoogle Scholar
  7. 7.
    Oprea, T.I.,Current trends in lead discovery: Are we looking for the appropriate properties?, J. Comp. Aid. Mol. Des., 16 (2002) 325–334.CrossRefGoogle Scholar
  8. 8.
    Lipinski, C.A.,Drug-like properties and the causes of poor solubility and poor permeability, J. Pharm. Tox. Methods, 44 (2000) 235–249.CrossRefGoogle Scholar
  9. 9.
    Blake, J.F.,Examiniation of the computed molecular properties of compounds selected for clinical development, Biotechniques, 34 (2003) S16–S20.Google Scholar
  10. 10.
    Rishton, G.M.,Reactive compounds and in vitro false positives in HTS, Drug Discovery Today, 2 (1997) 382–384.CrossRefGoogle Scholar
  11. 11.
    Huth, J.R., Mendoza, R., Olejniczak, E.T., Johnson, R.W., Cothron, D.A., Liu, Y., Lerner, C.G., Chen, J. and Hajduk, P.J.,ALARM NMR: A rapid and robust experimental method to detect reactive false positives in biochemical screens, J. Am. Chem. Soc., 127 (2005) 217–224.PubMedCrossRefGoogle Scholar
  12. 12.
    (a) McGovern, S.L., Caselli, E., Grigorieff, N. and Shoichet, B.K.,A common mechanism underlying promiscuous inhibitors from virtual and high-throughput screening, J. Med. Chem., 45 (2002) 1712–1722. (b) Seidler, J., McGovern, S.L., Doman, T.N. and Shoichet, B.K.,Identification and prediction of promiscuous aggregating inhibitors among known drugs, J. Med. Chem., 46 (2003) 4477–4486.PubMedCrossRefGoogle Scholar
  13. 13.
    Roche, O., Schneider, P., Zuegge, J., Guba, W., Kansy, M., Alanine, A., Bleicher, K., Danel, F., Gutknecht, E-M., Rogers-Evans, M., Neidhart, W., Stalder, H., Dillon, M., Sjögren, E., Fotouhi, N., Gillespie, P., Goodnow, R., Harris, W., Jones, P., Taniguchi, M., Tsujii, S., von der Saal, W., Zimmerman, G. and Schneider, G.,Development of a virtual screening method for identification of ‘frequent hitters’ in compound libraries, J. Med. Chem., 45 (2002) 137–142.PubMedCrossRefGoogle Scholar
  14. 14.
    (a) Oprea, T.I., Gottfries, J., Sherbukhin, V., Svensson, P. and Kuhler, T.C.,Chemical information management in drug discovery: Optimizing the computational and combinatorial chemistry interfaces, J. Mol. Graph. Mol., 18 (2000) 512–524. (b) Oprea, T.I., Zamora, I. and Ungell, A.,Pharmacokinetically Based Mapping Device for Chemical Space Navigation, J. Comb. Chem., 4 (2002) 258–266.CrossRefGoogle Scholar
  15. 15.
    (a) Rishton, G.M.,Nonleadlikeness and leadlikeness in biochemical screening, Drug Discovery Today, 8 (2003) 86–96. (b) Rishton, G.M.,Failure and Success in Modern Drug Discovery: Guiding Principles in the Establishment of High Probability of Success Drug Discovery Organizations, Medicinal Chemistry, 1 (2005) 519–527.PubMedCrossRefGoogle Scholar
  16. 16.
    Lipinski, C.A.,Lead- and drug-like compounds: The rule-of-five revolution, Drug Discovery Today: Technologies, 1 (2004) 337–341.CrossRefGoogle Scholar
  17. 17.
    Lipinski, C.A., Presentation ‘Combinatorial chemistry and HTS: Causes of or solutions to the innovation gap’, 4th Symposium on Drug Discovery, April 7–8, 2005, Antwerp, Belgium.Google Scholar
  18. 18.
    Hemmerle, H., Presentation ‘Platform library science and compound collection enhancement as the base for successful medicinal chemistry’, DDT Conference, August 9–11, 2005, Boston, U.S.A.Google Scholar
  19. 19.
    Blower, P.E., Cross, K.P., Fligner, M.A., Myatt, G.J., Verducci, J.S. and Yang, C.,Systematic Analysis of Large Screening Sets in Drug Discovery, Curr. Drug Disc. Technol., 1 (2004) 37.CrossRefGoogle Scholar
  20. 20.
    Voigt, J.H., Bienfait, B., Wang, S. and Nicklaus, M.C.,Comparison of the NCI open database with seven large chemical structural databases, J. Chem. Inf. Comput. Sci., 41 (2001) 702–712.PubMedCrossRefGoogle Scholar
  21. 21.
    Baurin, N., Baker, R., Richardson, C., Chen, I., Foloppe, N., Potter, A., Jordan, A., Roughley, S., Parratt, M., Greany, P., Morley, D. and Hubbard, R.E.,Drug-like annotation and duplicate analysis of a 23-supplier chemical database totalling 2.7\ million compounds, J. Chem. Inf. Comput. Sci., 44 (2004) 643–651.PubMedCrossRefGoogle Scholar
  22. 22.
    MDL Information Systems, Inc., 14600 Catalina Street, San Leandro, CA 94577, U.S.A. http://www.mdli.com/.
  23. 23.
    Chemical Computing Group, Inc. 1010 Sherbrooke St. West, Suite 910, Montreal, H3A 2R7 Canada. http://www.chemcomp.com/
  24. 24.
    Syracuse Research Corporation, 6225 Running Ridge Road, North Syracuse, NY 13212, U.S.A. KOWWIN and WSKOWWIN are part of the EPI suite (V3.12), available from http://www.epa.gov/oppt/-exposure/docs/episuitedl.htm
  25. 25.
    Xu, J.,A New Approach to finding natural chemical structure classes, J. Med. Chem., 45 (2002) 5311–5320.PubMedCrossRefGoogle Scholar
  26. 26.
    Trepalin, S.V. and Yarkov, A.V.,Ched—Chemical databases compilation tool, internet server and client for SQL servers, J. Chem. Inf. Comput. Sci., 41 (2001) 100–107., http://ched.ipac.ac.ru. PubMedCrossRefGoogle Scholar
  27. 27.
    Congreve, M., Carr, R., Murray, C. and Jhoti, H.,A ‘rule of three’ for fragment-based lead discovery?, Drug Discovery Today, 8 (2003) 876–877.PubMedCrossRefGoogle Scholar
  28. 28.
    Veber, D.F., Johnson, S.R., Cheng, H., Smith, B.R., Ward, K.W. and Kopple, K.D.,Molecular properties that influence the oral bioavailability of drug candidates, J. Med. Chem., 45 (2002) 2615–2623.PubMedCrossRefGoogle Scholar
  29. 29.
    Fichert, T., Yazdanian, M. and Proudfoot, J.R.,A structure-permeability study of samll drug-like molecules, Bioorg. Med. Chem. Lett., 13 (2003) 719–722.PubMedCrossRefGoogle Scholar
  30. 30.
    Clark, D.E. and Pickett, S.D.,Computational methods for the prediction of drug-likeness, Drug Discovery Today 5 (2000) 49–58.PubMedCrossRefGoogle Scholar
  31. 31.
    Kelder, J., Grootenhuis, P.J.D., Bayada, D.M., Delbressine, L.P.C. and Bloemen, J.,Polar molecular surface as a dominating determinant fororal absorption and brain penetration of drugs, Pharm. Res., 16 (1999) 1514–1519.PubMedCrossRefGoogle Scholar
  32. 32.
    Xu, J. and Stevenson, J.,Drug-like index: A new approach to measure drug-like compounds and their diversity, J. Chem. Inf. Comput. Sci., 40 (2000) 1177–1187.PubMedCrossRefGoogle Scholar
  33. 33.
    Bemis, G.W. and Murcko, M.A.,The properties of known drugs. 1. Molecular frameworks, J. Med. Chem., 39 (1996) 2887–2893.PubMedCrossRefGoogle Scholar
  34. 34.
    Maybridge PLC., Trevillett, Tintagel, Cornwall PL34 OHW, England. http://www.maybridge.com/.
  35. 35.
    Hann, M.M. and Oprea, T.I.,Pursuing the leadlikeness concept in pharmaceutical research, Curr. Opin. Chem. Biol., 8 (2004) 255–263.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Pyxis DiscoveryDelftThe Netherlands

Personalised recommendations