Chemical informatics

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The Jmol open-source Java viewer for chemical 3D structures is an example of a software application that may be used in the field of chemical informatics.

Chemical informatics (more commonly known as chemoinformatics and cheminformatics) is the use of computer and informational techniques applied to a range of problems in the field of chemistry. While the field has roughly been around around since the 1990s, the rise in high-throughput screening (a scientific experimentation method primarily used in drug discovery) and combinatorial chemistry (a method of synthesizing a large number of compounds in a single process), as well as increases in computing power and data storage sizes, have increased interest in the field in the twenty-first century.[1]

Outside of pharmaceutical research, other applications of chemical informatics include the area of topology, chemical graph theory, and mining the chemical space. It can also be applied to data analysis for the paper, pulp, and dye industries.[2][1]

History

The 1960s saw the introduction of databases for the storage and retrieval of chemical structures, as well as three-dimensional molecular modeling methods, laying the groundwork for future generations to improve computational methods of chemical and molecular analysis.[2]

The term "chemoinformatics" was defined by F.K. Brown[3][4] in 1998 as such:

Chemoinformatics is the mixing of those information resources to transform data into information and information into knowledge for the intended purpose of making better decisions faster in the area of drug lead identification and optimization.

Since then, both the "chem" and "chemo" spellings have been used. European academia settled on the term "chemoinformatics" for its 2006 Obernai research and teaching workshop.[5] Other entities like the Journal of Cheminformatics and Slovak company Molinspiration have trended towards "cheminformatics."[6][7]

Application

Storage and retrieval

The primary application of chemical informatics is in the storage and retrieval of both structured and unstructured information relating to chemical structures, molecular models and other chemical data. Efficiently querying and retrieving that stored information extends into other realms of computer science like data mining and machine learning. Other forms of data querying include graph, molecule, sequence, and tree mining.[8]

Representation

The in silico representation of chemical structures uses specialized formats such as the XML-based Chemical Markup Language or Simplified Molecular-Input Line-Entry System (SMILES) specifications. These representations are often used for storage in large chemical databases. While some formats are suited for visual representations in two or three dimensions, others are more suited for studying physical interactions, modeling, and docking studies.[8]

Virtual libraries

Stored chemical data can pertain to both real and virtual molecules. Virtual libraries of such molecules and compounds may be generated in various ways to explore chemical space and hypothesize novel compounds with desired properties. The Fragment Optimized Growth (FOG) algorithm, for example, was developed to "grow" novel classes of compounds like drugs, natural products, and diversity-oriented synthetic products from a training database of existing compounds.[9][10]

Virtual screening

In contrast to high-throughput screening, virtual screening involves computationally screening in silico libraries of compounds, by means of various methods such as docking, to identify members likely to possess desired properties such as biological activity against a given target. In some cases, combinatorial chemistry is used in the development of the library to increase the efficiency in mining the chemical space. More commonly, a diverse library of small molecules or natural products is screened.[1]

Quantitative structure-activity relationship (QSAR)

This is the calculation of quantitative structure-activity relationship and quantitative structure property relationship values, used to predict the activity of compounds from their structures. In this context there is also a strong relationship to chemometrics, the science of extracting information from chemical systems by data-driven means. Chemical expert systems are also relevant since they represent parts of chemical knowledge as an in silico representation.[1]

See also

External links

Notes

This article reuses portions of content from the Wikipedia article.

References

  1. 1.0 1.1 1.2 1.3 Leach, Andrew R.; Gillet, Valerie J. (2007). An Introduction to Chemoinformatics. Springer. pp. 256. ISBN 9781402062902. http://books.google.com/books?id=4z7Q87HgBdwC&printsec=frontcover. Retrieved 19 May 2014. 
  2. 2.0 2.1 Gasteiger, Johann (ed.) ; Engel, Thomas (ed.) (2006). "Chapter 1: Introduction". Chemoinformatics: A Textbook. John Wiley & Sons. pp. 1–14. ISBN 9783527606504. http://books.google.com/books?id=LCD-1vHBHIAC&printsec=frontcover. 
  3. Brown, F.K. (1998). "Chapter 35. Chemoinformatics: What is it and how does it impact drug discovery". Annual Reports in Medicinal Chemistry 33: 375–384. doi:10.1016/S0065-7743(08)61100-8. http://www.sciencedirect.com/science/article/pii/S0065774308611008. 
  4. Brown, Frank (May 2005). "Editorial Opinion: Chemoinformatics – a ten year update". Current Opinion in Drug Discovery & Development 8 (3): 298–302. http://www.ncbi.nlm.nih.gov/pubmed/15892243. 
  5. "Workshop Chemoinformatics in Europe: Research and Teaching". Laboratoire de Chémoinformatique, University of Strasbourg. 2006. http://infochim.u-strasbg.fr/chemoinformatics/. Retrieved 19 May 2014. 
  6. "Cheminformatics or Chemoinformatics?". Molinspiration Cheminformatics. December 2009. http://www.molinspiration.com/chemoinformatics.html. Retrieved 19 May 2014. 
  7. "About Journal of Cheminformatics". Chemistry Central. http://www.jcheminf.com/about. Retrieved 19 May 2014. 
  8. 8.0 8.1 Gasteiger, Johann (ed.) ; Engel, Thomas (ed.) (2006). "Chapter 2: Representation of Chemical Compounds". Chemoinformatics: A Textbook. John Wiley & Sons. pp. 15–157. ISBN 9783527606504. http://books.google.com/books?id=LCD-1vHBHIAC&printsec=frontcover. 
  9. Kutchukian, Peter S.; Lou, David; Shakhnovich, Eugene I. (2009). "FOG: Fragment Optimized Growth Algorithm for the de Novo Generation of Molecules Occupying Druglike Chemical Space". Journal of Chemical Information and Modeling 49 (7): 1630–1642. doi:10.1021/ci9000458. PMID 19527020. http://pubs.acs.org/doi/abs/10.1021/ci9000458. 
  10. Kutchukian, Peter S.; Virtanen, Salla I.; Lounkine, Eugen; Glick, Meir; Shakhnovich, Eugene I.; Schneider, Gisbert (ed.) (2013). "Chapter 13: Construction of Drug-Like Compounds by Markov Chains". De novo Molecular Design. John Wiley & Sons. ISBN 9783527677009. http://books.google.com/books?id=Jf1QAQAAQBAJ&pg=PA311. Retrieved 19 May 2014.