Cheminformatics (Also Known As Chemoinformatics , chemioinformatics and chemical informatics ) is the use of computer and informational technology applied to a Range of problems in the field of chemistry . These in silico techniques are used, for example, 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 FK Brown [1] [2] in 1998:

Chemoinformatics is one of the most widely used methods for the identification and optimization of drugs.

Since then, both, spellings-have-been used, and Some Evolved-have 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 , computer science and information science for example in the areas of topology , chemical graph theory , information retrieval and data mining in the chemical space . [5] [6] [7] [8] Paths for the various industries, such as paper and pulp , dyes and allied industries.


Storage and retrieval

Main article: Chemical database

The primary application of pathformatics is in the storage, indexing and search of information relating to compounds. The efficient search of Stored Such information includes topics That are Dealt with in computer science as data mining , information retrieval , information extraction and machine learning . Related research topics include:

  • Unstructured data
    • Information retrieval
    • Information extraction
  • Structured data mining and mining of structured data
    • Database mining
    • Graph mining
    • Molecule mining
    • Sequence mining
    • Tree mining
  • Digital libraries

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 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.

Virtual libraries of classes of compounds (drugs, natural products, diversity-oriented synthetic products) were recently generated using the FOG (fragment optimized growth) algorithm. [9] This was done by using pathformatic tools to train the transition probabilities of a Markov chain on a series 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 possible biological activities 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-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 relation to chemometrics . Chemical expert systems sont également falling, since They Represent shares of Chemical knowledge as an in silico representation. There is a relatively new concept of matched molecular pair or prediction-driven MMPA which is coupled with QSAR in order to identify activity cliff. [10]

See also

  • Bioinformatics
  • Chemical file format
  • Cheminformatics toolkits
  • Chemogenomics
  • Computational chemistry
  • Data analysis
  • Journal of Chemical Information and Modeling
  • List of chemistry topics
  • Mathematical chemistry
  • Molecular Conceptor
  • Molecular design software
  • Molecular graphics
  • Molecular modeling
  • Pharmaceutical company
  • Scientific visualization
  • Software for molecular modeling
  • Statistics
  • WorldWide Molecular Matrix


  1. Jump up^ FK Brown (1998). “Chapter 35. Chemoinformatics: What Is It and How It Impacts Drug Discovery”. Annual Reports in Med. Chem . Annual Reports in Medicinal Chemistry. 33 : 375. ISBN  978-0-12-040533-6 . Doi : 10.1016 / S0065-7743 (08) 61100-8 .
  2. Jump up^ Brown, Frank (2005). “Editorial Opinion: Chemoinformatics – a ten year update”. Current Opinion in Drug Discovery & Development . 8 (3): 296-302.
  3. Jump up^ Chemometformatics or Chemoinformatics?
  4. Jump up^ Obernai Declaration
  5. Jump up^ Gasteiger J. (Editor), Engel T. (Editor):Chemoinformatics: A Textbook. John Wiley & Sons, 2004,ISBN 3-527-30681-1
  6. Jump up^ AR Leach, VJ Gillet:An Introduction to Chemoinformatics. Springer, 2003,ISBN 1-4020-1347-7
  7. Jump up^ Alexandre Varnek and Igor Baskin (2011). “Chemoinformatics as a Theoretical Chemistry Discipline”. Molecular Informatics . 30 (1): 20-32. Doi : 10.1002 / minf.201000100 .
  8. Jump up^ Barry A. Bunin (Author), Brian Siesel (Author), Guillermo Morales (Author), Jürgen Bajorath (Author):Chemoinformatics: Theory, Practice, & Products. Springer, 2006,ISBN 978-1402050008
  9. Jump up^ Kutchukian, Peter; Lou, David; Shakhnovich, Eugene (2009). “FOG: Fragment Optimized Growth Algorithm for the Novo Generation of Molecules Occupying Druglike Chemical”. Journal of Chemical Information and Modeling . 49 (7): 1630-1642. PMID  19527020 . Doi : 10.1021 / ci9000458 .
  10. Jump up^

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