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,[3] while European Academia settled in 2006 for Chemoinformatics.[4] The recent establishment of the Journal of Cheminformatics is a strong push towards the shorter variant.


Cheminformatics combines the scientific working fields of chemistry and computer science for example in the area of topology and chemical graph theory and mining the chemical space.[5][6] Cheminformatics can also be applied to data analysis for various industries like paper and pulp, dyes and such allied industries.


Storage and retrieval

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 with in computer science as data mining and machine learning. Related research topics include:

File formats

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 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. [7] 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

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)

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 (1998). "Chapter 35. Chemoinformatics: What is it and How does it Impact Drug Discovery". Annual Reports in Med. Chem.. Annual Reports in Medicinal Chemistry 33: 375. doi:10.1016/S0065-7743(08)61100-8. ISBN 9780120405336. 
  2. ^ Brown, Frank (2005). "Editorial Opinion: Chemoinformatics – a ten year update". Current Opinion in Drug Discovery & Development 8 (3): 296–302. 
  3. ^ Cheminformatics or Chemoinformatics ?
  4. ^ Obernai Declaration
  5. ^ Gasteiger J.(Editor), Engel T.(Editor): Chemoinformatics : A Textbook. John Wiley & Sons, 2004, ISBN 3-527-30681-1
  6. ^ A.R. Leach, V.J. Gillet: An Introduction to Chemoinformatics. Springer, 2003, ISBN 1-4020-1347-7
  7. ^ Kutchukian, Peter; Lou, David; Shakhnovich, Eugene (2009). "FOG: Fragment Optimized Growth Algorithm for the de Novo Generation of Molecules occupying Druglike Chemical". Journal of Chemical Information and Modeling 49 (7): 1630–1642. doi:10.1021/ci9000458. PMID 19527020. 

External links

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