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Cheminformatics (also known as chemoinformatics and chemical informatics) is the use of computer and informational techniques, applied to a range of problems in the field of chemistry. These in silico techniques are used in pharmaceutical companies in the process of drug discovery. These methods can also be used in chemical and allied industries in various other forms.



The term Chemoinformatics was defined by F.K. Brown [1][2] in 1998:

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 spellings have been used, and some have evolved to be established as Cheminformatics [1], while European Academia settled in 2006 for Chemoinformatics. [3]


Cheminformatics combines the scientific working fields of chemistry and computer science for example in the area of chemical graph theory and mining the chemical space.[4][5] It is to be expected that the chemical space contains at least 1060 molecules. Cheminformatics can also applied to data analysis for various industries like paper and pulp,dyes and such allied industries.


Storage and retrieval

Main article: Chemical database

The primary application of cheminformatics is in the storage of information relating to compounds. The efficient search of such stored information includes topics that are dealt in computer science as data mining and machine learning. Related research topics include:

  • Unstructured data
  • Structured Data Mining and mining of Structured data

File formats

Main article: Chemical file format

The in silico representation of chemical structures uses specialized formats such as the XML-based Chemical Markup Language, or SMILES. These representations are often used for storage in large chemical databases. While some formats are suited for visual representations in 2 or 3 dimensions, others are more suited for studying physical interactions, modeling and docking studies.

Virtual screening

Main article: Virtual_high_throughput_screening

In contrast to high-throughput screening, virtual screening involves the creation of large in silico virtual libraries of compounds, which are then submitted to a docking program in order to identify the most active members. 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)

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. Chemical expert systems are also relevant, since they represent parts of chemical knowledge as an in silico representation.

See also


  1. ^ F.K. Brown Chapter 35. Chemoinformatics: What is it and How does it Impact Drug Discovery. Annual Reports in Med. Chem., Ed. James A. Bristol, 1998, Vol. 33, pp. 375.
  2. ^ Brown, Frank. Editorial Opinion: Chemoinformatics – a ten year update Current Opinion in Drug Discovery & Development (2005), 8(3), 296-302.
  3. ^ Obernai Declaration
  4. ^ Gasteiger J.(Editor), Engel T.(Editor): Chemoinformatics : A Textbook. John Wiley & Sons, 2004, ISBN 3-527-30681-1
  5. ^ A.R. Leach, V.J. Gillet: An Introduction to Chemoinformatics. Springer, 2003, ISBN 1-4020-1347-7
This article is licensed under the GNU Free Documentation License. It uses material from the Wikipedia article "Cheminformatics". A list of authors is available in Wikipedia.
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