Skip to main content

Accessing and Using Chemical Databases

  • Protocol
  • First Online:
  • 4185 Accesses

Part of the book series: Methods in Molecular Biology ((MIMB,volume 930))

Abstract

Computer-based representation of chemicals makes it possible to organize data in chemical databases—collections of chemical structures and associated properties. Databases are widely used wherever efficient processing of chemical information is needed, including search, storage, retrieval, and dissemination. Structure and functionality of chemical databases are considered. The typical kinds of information found in a chemical database are considered—identification, structural, and associated data. Functionality of chemical databases is presented, with examples of search and access types. More details are included about the OASIS database and platform and the Danish (Q)SAR Database online. Various types of chemical database resources are discussed, together with a list of examples.

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

Buying options

Protocol
USD   49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   119.99
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. Halpin TA, Morgan AJ (2008) Information modeling and relational databases, 2nd edn. In: The Morgan Kaufmann series in data management systems. Elsevier, Amsterdam

    Google Scholar 

  2. Dalby A, Nourse JG, Hounshell DW et al (1992) Description of several chemical structure file formats used by computer programs developed at molecular design limited. J Chem Inf Comput Sci 32:244–255

    Article  CAS  Google Scholar 

  3. Hopfinger AJ, Wang S, Tokarski JS et al (1997) Construction of 3D-QSAR models using the 4D-QSAR analysis formalism. J Am Chem Soc 119:10509–10524

    Article  CAS  Google Scholar 

  4. Miller M (2002) Chemical database techniques in drug discovery. Nat Rev Drug Discov 1:220–227. doi:10.1038/nrd745

    Article  PubMed  CAS  Google Scholar 

  5. Barnard JM (1993) Substructure searching methods: old and new. J Chem Inf Comput Sci 33:532–538

    Article  CAS  Google Scholar 

  6. Jamil H (2011) Computing subgraph isomorphic queries using structural unification and minimum graph structures, Proc. of the 26th ACM symposium on applied computing SAC 2011, pp 1058–1065, doi:10.1145/1982185.1982415

  7. Ullmann JR (1976) An algorithm for subgraph isomorphism. J Assoc Mach 23(1):31–42. doi:10.1145/321921.321925

    Article  Google Scholar 

  8. Willett P (2003) Similarity searching in chemical structure databases. In: Gasteiger J (ed) Handbook of chemoinformatics. Wiley, Weinheim

    Google Scholar 

  9. Nikolov N, Grancharov V, Stoyanova G et al (2006) Representation of chemical information in OASIS Centralized 3D database for existing chemicals. J Chem Inf Model 46(6):2537–2551

    Article  PubMed  CAS  Google Scholar 

  10. Park J, Rosania GR, Shedden KA et al (2009) Automated extraction of chemical structure information from digital raster images. J Chem Cent 3 (1): 1-16, doi:10.1186/1752-153X-3-4

    Article  Google Scholar 

  11. http://opsin.ch.cam.ac.uk/index.html

  12. Hardy B, Douglas N, Helma C et al (2010) Collaborative development of predictive toxicology applications. J Cheminform 2(1):7

    Article  PubMed  Google Scholar 

  13. Stein LD (2008) Towards a cyberinfrastructure for the biological sciences: progress, visions and challenges. Nat Rev Genet 9:678–688. doi:10.1038/nrg2414

    Article  PubMed  CAS  Google Scholar 

  14. http://www.w3.org/standards/webofservices

  15. Murray-Rust P, Rzepa H, Wright M et al (2000) A universal approach to web-based chemistry using XML and CML. Chem Commun 1471–1472

    Google Scholar 

  16. Nature Vol. 451 (7179):648–651. http://www.w3.org/standards/semanticweb

  17. Murray-Rust P (2008) Chemistry for everyone. Nature 451:648–651. doi:10.1038/451648a

    Article  PubMed  CAS  Google Scholar 

  18. http://www.w3.org/standards/semanticweb/ontology

  19. http://www.oasis-lmc.org

  20. Mekenyan O, Pavlov T, Grancharov V et al (2005) 2D–3D migration of large chemical inventories with conformational multiplication. Application of the genetic algorithm. J Chem Inf Model 45(2):283–292

    Article  PubMed  CAS  Google Scholar 

  21. Mekenyan O, Dimitrov D, Nikolova N et al (1999) Conformational coverage by a genetic algorithm. J Chem Inf Comput Sci 39(6):997–1016. doi:10.1021/ci990303g

    Article  CAS  Google Scholar 

  22. Mekenyan OG, Kamenska, V, Serafimova R et al (2002) Development and validation of an average mammalian estrogen receptor-based (Q)SAR model. In: Mekenyan O, Schultz TW (eds) Proceedings of quantitative structure activity relationships in environmental sciences—IX. SAR (Q)SAR Environ Res 13(6):579–595

    Google Scholar 

  23. http://www.mst.dk/English/Chemicals/Substances_and_materials/qsar

  24. http://ecbqsar.jrc.it

  25. http://qsar.food.dtu.dk

  26. http://www.oecd.org/document/54/0,3343,en_2649_34373_42923638_1_1_1_1,00.html

  27. http://toolbox.oasis-lmc.org

  28. Niemelä JR, Wedebye EB, Nikolov NG et al (2009) The advisory list for self-classification of dangerous substances: DK EPA environmental project No. 1303 2009—Danish EPA environmental report, p 62. In: Danish EPA Environmental Projects; 1303

    Google Scholar 

  29. http://www.mst.dk/English/Chemicals/Substances_and_materials/The_advisory_list_for_selfclassification

  30. http://www.chembiogrid.org/related/resources/databases.html

  31. http://www.liv.ac.uk/Chemistry/Links/refdatabases.html

  32. Valerio LG (2009) In silico toxicology for the pharmaceutical sciences. Toxicol Appl Pharmacol 241:356–370

    Article  PubMed  CAS  Google Scholar 

  33. O’Donnell TJ (2009) Design and use of relational databases in chemistry. CRC Press, Boca Raton, FL

    Google Scholar 

  34. Codd EF (1970) A relational model of data for large shared data banks. Commun ACM 13(6):377–387. doi:10.1145/362384.362685

    Article  Google Scholar 

  35. http://accelrys.com/products/databases/bioactivity/rtecs.html

  36. http://actor.epa.gov

  37. de Matos P, Alcántara R, Dekker A et al (2009) Chemical entities of biological interest: an update. Nucleic Acids Res (in press)

    Google Scholar 

  38. http://www.ebi.ac.uk/chebi

  39. http://www.cas.org

  40. http://www.chemaxon.com/products/jchem-base

  41. Chen J, Swamidass SJ, Dou Y et al (2005) Chemdb: a public database of small molecules and related chemoinformatics resources. Bioinformatics 21:4133–4139

    Article  PubMed  CAS  Google Scholar 

  42. http://cdb.ics.uci.edu

  43. http://chem.sis.nlm.nih.gov/chemidplus

  44. Pence HE, Williams A (2010) ChemSpider: an online chemical information resource. J Chem Educ 87(11):1123–1124. doi:10.102c1/ed100697w

    Article  CAS  Google Scholar 

  45. http://cs.m.chemspider.com

  46. http://www.daylight.com/products/daycart.html

  47. Wishart DS, Knox C, Guo AC et al (2008) DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res 36(1):D901–D906

    PubMed  CAS  Google Scholar 

  48. http://apps.echa.europa.eu/registered/registered-sub.aspx

  49. http://www.echemportal.org

  50. http://www.emolecules.com

  51. http://www.leadscope.com

  52. http://www.wwpdb.org

  53. Bolton E, Wang Y, Thiessen PA et al (2008) PubChem: integrated platform of small molecules and biological activities. Annual reports in computational chemistry, Apr 2008

    Google Scholar 

  54. http://pubchem.ncbi.nlm.nih.gov

  55. http://www.reaxys.com/info

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ovanes Mekenyan .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media, LLC

About this protocol

Cite this protocol

Nikolov, N., Pavlov, T., Niemelä, J.R., Mekenyan, O. (2013). Accessing and Using Chemical Databases. In: Reisfeld, B., Mayeno, A. (eds) Computational Toxicology. Methods in Molecular Biology, vol 930. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-059-5_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-62703-059-5_2

  • Published:

  • Publisher Name: Humana Press, Totowa, NJ

  • Print ISBN: 978-1-62703-058-8

  • Online ISBN: 978-1-62703-059-5

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics