Cheminformatics

Source: Wikipedia, the free encyclopedia.

Cheminformatics (also known as chemoinformatics) refers to the use of

structure-based drug design. The methods can also be used in chemical and allied industries, and such fields as environmental science and pharmacology, where chemical processes are involved or studied.[1]

History

Cheminformatics has been an active field in various guises since the 1970s and earlier, with activity in academic departments and commercial pharmaceutical research and development departments.[2][page needed][citation needed] The term chemoinformatics was defined in its application to drug discovery by F.K. Brown in 1998:[3]

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 terms, cheminformatics and chemoinformatics, have been used,[citation needed] although, lexicographically, cheminformatics appears to be more frequently used,[when?][4][5] despite academics in Europe declaring for the variant chemoinformatics in 2006.[6] In 2009, a prominent Springer journal in the field was founded by transatlantic executive editors named the Journal of Cheminformatics.[7]

Background

Cheminformatics combines the scientific working fields of chemistry, computer science, and information science—for example in the areas of

pulp, dyes and such allied industries.[12]

Applications

Storage and retrieval

Main article: Chemical database

A primary application of cheminformatics is the storage, indexing, and search of information relating to chemical compounds.[according to whom?][citation needed] The efficient search of such stored information includes topics that are dealt with in computer science, such as data mining, information retrieval, information extraction, and machine learning.[citation needed] Related research topics include:[citation needed]

File formats

Main article: Chemical file format

The in silico representation of chemical structures uses specialized formats such as the

Simplified molecular input line entry specifications (SMILES)[13] or the XML-based Chemical Markup Language.[14] These representations are often used for storage in large chemical databases.[citation needed] 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.[citation needed
]

Virtual libraries

Chemical data can pertain to real or virtual molecules. Virtual libraries of compounds may be generated in various ways to explore chemical space and hypothesize novel compounds with desired properties. Virtual libraries of classes of compounds (drugs, natural products, diversity-oriented synthetic products) were recently generated using the FOG (fragment optimized growth) algorithm.[15] This was done by using cheminformatic tools to train transition probabilities of a Markov chain on authentic classes of compounds, and then using the Markov chain to generate novel compounds that were similar to the training database.

Virtual screening

Main article: 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.

Quantitative structure-activity relationship (QSAR)

Main article: Quantitative structure–activity relationship

This is the calculation of

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. Chemical expert systems are also relevant, since they represent parts of chemical knowledge as an in silico representation. There is a relatively new concept of matched molecular pair analysis or prediction-driven MMPA which is coupled with QSAR model in order to identify activity cliff.[16]

See also

References

  1. PMID 17125169
    .
  2. ISBN 9780824765743
    .
  3. Current Opinion in Drug Discovery & Development
    . 8 (3): 296–302.
  4. ^ "Cheminformatics or Chemoinformatics ?". Archived from the original on 2017-06-21. Retrieved 2006-03-31.
  5. ^ "Biopharmaceutical glossary Tips & FAQs".
  6. ^ http://infochim.u-strasbg.fr/chemoinformatics/Obernai%20Declaration.pdf Archived 2016-03-03 at the Wayback Machine [bare URL PDF]
  7. ^ Willighagen, Egon. "Open Access Journal of Cheminformatics now live! « SteinBlog". Retrieved 2022-06-20.
  8. ISBN 3527306811
    .
  9. ISBN 1402013477
    .
  10. S2CID 21604072
    .
  11. ISBN 9781402050008
    .
  12. ^ Williams, Tova; University, North Carolina State. "Cheminformatics approaches to creating new hair dyes". phys.org. Retrieved 2022-06-20.
  13. S2CID 5445756
    .
  14. doi:10.1021/ci990052b
    .
  15. PMID 19527020
    .
  16. PMID 25544551
    .

Further reading

External links
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