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
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Halpin TA, Morgan AJ (2008) Information modeling and relational databases, 2nd edn. In: The Morgan Kaufmann series in data management systems. Elsevier, Amsterdam
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
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
Miller M (2002) Chemical database techniques in drug discovery. Nat Rev Drug Discov 1:220–227. doi:10.1038/nrd745
Barnard JM (1993) Substructure searching methods: old and new. J Chem Inf Comput Sci 33:532–538
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
Ullmann JR (1976) An algorithm for subgraph isomorphism. J Assoc Mach 23(1):31–42. doi:10.1145/321921.321925
Willett P (2003) Similarity searching in chemical structure databases. In: Gasteiger J (ed) Handbook of chemoinformatics. Wiley, Weinheim
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
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
Hardy B, Douglas N, Helma C et al (2010) Collaborative development of predictive toxicology applications. J Cheminform 2(1):7
Stein LD (2008) Towards a cyberinfrastructure for the biological sciences: progress, visions and challenges. Nat Rev Genet 9:678–688. doi:10.1038/nrg2414
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
Nature Vol. 451 (7179):648–651. http://www.w3.org/standards/semanticweb
Murray-Rust P (2008) Chemistry for everyone. Nature 451:648–651. doi:10.1038/451648a
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
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
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
http://www.mst.dk/English/Chemicals/Substances_and_materials/qsar
http://www.oecd.org/document/54/0,3343,en_2649_34373_42923638_1_1_1_1,00.html
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
Valerio LG (2009) In silico toxicology for the pharmaceutical sciences. Toxicol Appl Pharmacol 241:356–370
O’Donnell TJ (2009) Design and use of relational databases in chemistry. CRC Press, Boca Raton, FL
Codd EF (1970) A relational model of data for large shared data banks. Commun ACM 13(6):377–387. doi:10.1145/362384.362685
http://accelrys.com/products/databases/bioactivity/rtecs.html
de Matos P, Alcántara R, Dekker A et al (2009) Chemical entities of biological interest: an update. Nucleic Acids Res (in press)
Chen J, Swamidass SJ, Dou Y et al (2005) Chemdb: a public database of small molecules and related chemoinformatics resources. Bioinformatics 21:4133–4139
Pence HE, Williams A (2010) ChemSpider: an online chemical information resource. J Chem Educ 87(11):1123–1124. doi:10.102c1/ed100697w
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
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
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights 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