Skip to main content
SpringerLink
Log in

Managing, profiling and analyzing a library of 2.6 million compounds gathered from 32 chemical providers

  • Full–length paper
  • Published:
Molecular Diversity Aims and scope Submit manuscript

Summary

The data for 3.8 million compounds from structural databases of 32 providers were gathered and stored in a single chemical database. Duplicates are removed using the IUPAC International Chemical Identifier. After this, 2.6 million compounds remain. Each database and the final one were studied in term of uniqueness, diversity, frameworks, ‘drug-like’ and ‘lead–like’ properties. This study also shows that there are more than 87 000 frameworks in the database. It contains 2.1 million ‘drug-like’ molecules among which, more than one million are ‘lead-like’. This study has been carried out using ‘ScreeningAssistant’, a software dedicated to chemical databases management and screening sets generation. Compounds are stored in a MySQL database and all the operations on this database are carried out by Java code. The druglikeness and leadlikeness are estimated with ‘in–house’ scores using functions to estimate convenience to properties; unicity using the InChI code and diversity using molecular frameworks and fingerprints. The software has been conceived in order to facilitate the update of the database. ‘ScreeningAssistant’ is freely available under the GPL license.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Abbreviations

HBA:

H bond acceptor

HBD:

H bond donor

HTS:

high-throughput screening

InChI:

IUPAC International Chemical Identifier

JNI:

Java Native Interface

MW:

molecular weight

RO5:

rule-of-five

SCA:

stochastic clustering analysis

SSSR:

smallest set of smallest rings

References

  1. Bradley, M.P., An overview of the diversity represented in commercially-available databases, J. Comput. Aided Mol. Des., 16 (2002) 299–300.

    Article  Google Scholar 

  2. Mozziconacci, J.C., Arnoult, E., Baurin, N., Marot, C. and Morin-Allory, L., Preparation of a molecular database from a set of 2 million compounds for virtual screening applications : Gathering, structural analysis and filtering, 9th Electronic Computational Chemistry Conference, World Wide Web, March (2003).

  3. Sirois, S., Hatzakis, G., Wei, D., Du, Q., Chou, K.C., Assessment of chemical libraries for their druggability, Comput. Biol. Chem., 29 (2005) 55–67.

    Article  PubMed  CAS  Google Scholar 

  4. Baurin, N., Baker, R., Richardson, C., Chen, I., Foloppe, N., Potter, A., Jordan, A., Roughley, S., Parratt, M., Greaney, 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) 643–657.

    Article  PubMed  CAS  Google Scholar 

  5. Cummins, D.J., Andrews, C.W., Bentley, J.A. and Cory, M., Molecular diversity in chemical databases: Comparison of medicinal chemistry knowledge bases and databases of commercially available compounds, J. Chem. Inf. Comput. Sci., 36 (1996) 750–763.

    Article  PubMed  CAS  Google Scholar 

  6. 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.

    Article  PubMed  CAS  Google Scholar 

  7. Monge, A., Screening assistant, http://screenassistant.sourceforge.net/

  8. Wegner, J.K., JOELib, http://joelib.sourceforge.net

  9. Corina. Molecular Networks GmbH. http://www.mol-net.com

  10. The IUPAC International Chemical Identifier Project, http://www.iupac.org/inchi/

  11. Murray-Rust, P., Rzepa, H.S., Stewart, J.J., Zhang, Y., A global resource for computational chemistry, J. Mol. Model., 11 (2005) 532–541.

    Article  PubMed  CAS  Google Scholar 

  12. Coles, S.J., Day, N.E., Murray-Rust, P., Rzepa, H.S. and Zhang, Y., Enhancement of the chemical semantic web through the use of InChI identifiers, Org. Biomol. Chem., 3 (2005) 1832–1834.

    Article  PubMed  CAS  Google Scholar 

  13. Prasanna, M.D., Vondrasek, J., Wlodawer, A. and Bhat, T.N., Application of InChI to curate, index, and query 3-D structures, Proteins, 60 (2005) 1–4.

    Article  PubMed  CAS  Google Scholar 

  14. Molecular Operating Environment (MOE), Chemical Computing, http://www.chemcomp.com

  15. OEChem, OpenEye Scientific Software, http://www.eyesopen.com

  16. Marvin, ChemAxon. http://www.chemaxon.com

  17. Groupement De Service Chimiothèque Nationale, http://chimiotheque-nationale.enscm.fr

  18. Reynolds, C.H., Druker, R. and Pfahle, L.B., Lead discovery using stochastic cluster analysis (SCA): A new method for clustering structurally similar compounds, J. Chem. Inf. Comput. Sci., 38 (1998) 305–312.

    Article  CAS  Google Scholar 

  19. Xue, L., Godden, J.W. and Bajorath, J., Database searching for compounds with similar biological activity using short binary bit string representations of molecules, J. Chem. Inf. Comput. Sci., 39 (1999) 881–886.

    Article  PubMed  CAS  Google Scholar 

  20. Bemis, G.W. and Murcko, M.A., The properties of known drugs. 1. Molecular frameworks, J. Med. Chem., 39 (1996) 2887–2893.

    Article  PubMed  CAS  Google Scholar 

  21. Lajiness, M.S., Vieth, M. and Erickson, J., Molecular properties that influence oral drug-like behavior, Curr. Opin. Drug Discov. Devel., 7 (2004) 470–477.

    PubMed  CAS  Google Scholar 

  22. Walters, W.P. and Murcko, M.A., Prediction of ‘drug-likeness’, Adv. Drug Delivery Rev., 54 (2002) 255–271.

    Article  CAS  Google Scholar 

  23. Clark, D.E., Pickett, S.D., Computational methods for the prediction of ‘druglikeness’, Drug Discov. Today, 5 (2000), 49–58.

    Article  PubMed  CAS  Google Scholar 

  24. Muegge, I., Selection criteria for drug-like compounds, Med. Res. Rev., 23 (2003) 302–321.

    Article  PubMed  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  26. Lipinski, C.A., Lead- and drug-like compounds: The rule-of-five revolution, Drug Discov. Today, 1 (2004) 337–341.

    CAS  Google Scholar 

  27. Frimurer, T.M., Bywater, R., Nærum, L., Lauritsen, L.N. and Brunak, S., Improving the odds in discriminating “drug-like” from “non drug-like” compounds, J. Chem. Inf. Comput. Sci., 40 (2000), 1315–1324.

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  29. Xu, J., 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.

    Article  PubMed  CAS  Google Scholar 

  30. Veber, D.F., Johnson, S.R., Cheng, H.Y., Smith, B.R., Ward, K.W., Kopple, K.D., Molecular properties that influence the oral bioavailability of drug candidates, J. Med. Chem., 45 (2002) 2615–2623.

    Article  PubMed  CAS  Google Scholar 

  31. Zheng, S., Luo, X., Chen, G., Zhu, W., Shen, J., Chen, K. and Jiang, H., A new rapid and effective chemistry space filter in recognizing a druglike database, J. Chem. Inf. Comput. Sci., 45 (2005) 856–862.

    CAS  Google Scholar 

  32. Muegge, I., Heald, S.L. and Brittelli, D., Simple selection criteria for drug-like chemical matter, J. Med. Chem., 44 (2001) 1841–1846.

    Article  PubMed  CAS  Google Scholar 

  33. Zernov, V.V., Balakin, K.V., Ivaschenko, A.A., Savchuk, N.P. and Pletnev, I.V., Drug Discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions, J. Chem. Inf. Comput. Sci., 43 (2003), 2048–2056.

    Article  PubMed  CAS  Google Scholar 

  34. Ajay, A., Walters, W.P. and Murcko, M.A., Can we learn to distinguish between “drug-like” and “nondrug-like” molecules?, J. Med. Chem., 41 (1998) 3314–3324.

    Article  PubMed  CAS  Google Scholar 

  35. Sadowski, J. and Kubinyi, H., A scoring scheme for discriminating between drugs and nondrugs, J. Med. Chem., 41 (1998) 3325–3329.

    Article  PubMed  CAS  Google Scholar 

  36. Charifson, P.S. and Walters, W.P., Filtering databases and chemical libraries, J. Comput. Aided Mol. Des., 16 (2002) 311–323.

    Article  PubMed  CAS  Google Scholar 

  37. Rishton, G.M., Reactive compounds and in vitro false positives in HTS, Drug Discov. Today, 2 (1997) 382–384.

    Article  CAS  Google Scholar 

  38. Wildman, S.A. and Crippen, G.M., Prediction of physicochemical parameters by atomic contributions, J. Chem. Inf. Comput. Sci., 39 (1999) 868–873.

    Article  CAS  Google Scholar 

  39. 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.

    Article  PubMed  CAS  Google Scholar 

  40. Oprea, T.I., Current trends in lead discovery: Are we looking for the appropriate properties?, J. Comput. Aided Mol. Des., 16 (2002) 325–334.

    Article  PubMed  CAS  Google Scholar 

  41. Davis, A.M., Teague, S.J. and Kleywegt, G.J., Application and limitations of X-ray crystallographic data in structure-based ligand and drug design, J. Chem. Inf. Comput. Sci., 42 (2003) 2718–2736.

    CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  43. Wenlock, M.C., Austin, R.P., Barton, P., Davis, A.M. and Leeson P.D., A comparison of physiochemical property profiles of development and marketed oral drugs, J. Med. Chem., 46 (2003) 1250–1256.

    Article  PubMed  CAS  Google Scholar 

  44. Hou, T.J., Xia, K., Zhang, W. and Xu, X.J., ADME evaluation in drug discovery. 4. Prediction of aqueous solubility based on atom contribution approach, J. Chem. Inf. Comput. Sci., 44 (2004) 266–275.

    Article  PubMed  CAS  Google Scholar 

  45. Ertl, P., Rohde, B. and Selzer, P., Fast calculation of molecular polar surface area as a sum of fragment-based contributions and its application to the prediction of drug transport properties, J. Med. Chem., 43 (2000) 3714–3717.

    Article  PubMed  CAS  Google Scholar 

  46. Palm, K., Stenberg, P., Luthman, K. and Artursson, P., Polar molecular surface properties predict the intestinal absorption of drugs in humans, Pharm. Res., 14 (1997) 568–571.

    Article  PubMed  CAS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Institut de Chimie Organique et Analytique, UMR CNRS 6005, Université d’Orléans, BP 6759, 45067, Orléans Cedex 2, France

    Aurélien Monge, Alban Arrault, Christophe Marot & Luc Morin-Allory

Authors
  1. Aurélien Monge
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alban Arrault
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Christophe Marot
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Luc Morin-Allory
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Aurélien Monge.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Monge, A., Arrault, A., Marot, C. et al. Managing, profiling and analyzing a library of 2.6 million compounds gathered from 32 chemical providers. Mol Divers 10, 389–403 (2006). https://doi.org/10.1007/s11030-006-9033-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11030-006-9033-5

Key words

Search

Navigation