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Cheminformatics (also known as chemoinformatics and chemical informatics) is the study of large amounts of chemical information. It is done mostly with the help of computers. These tools are used by pharmaceutical companies to discovery new drugs.

Cheminformatics uses computer science and information technology to help solve chemistry problems. Cheminformatics deals with algorithms, databases and information systems, web technologies, artificial intelligence and soft computing, information and computation theory, software engineering, data mining, image processing, modeling and simulation, signal processing, discrete mathematics, control and system theory, circuit theory, and statistics, for generating new knowledge of chemistry.

History[change | change source]

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

Basics[change | change source]

Cheminformatics combines the scientific working fields of chemistry and computer science.[3][4] Cheminformatics can also be applied to data analysis for the paper, pulp and dye industries.

Uses[change | change source]

Storage and retrieval[change | change source]

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.

File formats[change | change source]

Computers represent chemical structures in specialized formats such as the XML-based Chemical Markup Language or SMILES. 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[change | change source]

Chemical data can pertain to real or virtual molecules. Virtual compounds may be used to explore chemical space and predict new 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. [5]

Virtual screening[change | change source]

Instead og testing the actual chemicals, virtual screening involves screening compounds by computer, to identify members likely to possess desired properties such as biological activity against a given target.

Quantitative structure-activity relationship (QSAR)[change | change source]

This is to predict the activity of compounds from their structures. These studies link cheminofrmatics to chemometrics. Chemical expert systems are also relevant. They represent parts of chemical knowledge in computers.

References[change | change source]

  1. F.K. Brown (1998). "Chapter 35. Chemoinformatics: What is it and How does it Impact Drug Discovery". Annual Reports in Med. Chem. 33: 375. doi:10.1016/S0065-7743(08)61100-8.
  2. Brown, Frank (2005). "Editorial Opinion: Chemoinformatics – a ten year update". Current Opinion in Drug Discovery & Development. 8 (3): 296–302.
  3. Gasteiger J.(Editor), Engel T.(Editor): Chemoinformatics : A Textbook. John Wiley & Sons, 2004, ISBN 3-527-30681-1
  4. A.R. Leach, V.J. Gillet: An Introduction to Chemoinformatics. Springer, 2003, ISBN 1-4020-1347-7
  5. Kutchukian, Peter (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. Unknown parameter |coauthors= ignored (|author= suggested) (help)

Other websites[change | change source]