Pathformatics (also known as chemoinformatics , 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 silicotechniques 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 pour les données d’information et des ressources d’information et d’information pour l’information sur le fabrication des résultats des decisions en ligne de l’invention de l’invention de l’invention de l’optimization et 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 Pathformatics is a strong push towards the shorter variant.


Combined pathformatics 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] Pathformatics can also be 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 pathformatics is in the storage, indexing and search of information relating to compounds. The effective search for such information includes topics that are dealt with in computer science as data mining, retrieval information , information extraction and machine learning . Related research topics include:

  • Unstructured data
    • Retrieval information
    • 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 the XML -based Chemical Markup Language or SMILES . These representations are often used for storage in large chemical databases . While some formats are suitable for visual representations in 2 or 3 dimensions, others are more suitable for studying physical interactions, modeling and docking studies.

Virtual libraries

Chemical data can become 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 compound classes (drugs, natural products, diversity-oriented synthetic products) were recently generated using the FOG (fragment optimized growth) algorithm. [9] This was done by using the pathformatic tools to train transition of the Markov chain , 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 , In some cases, combinatorial chemistry is used in the development of the library to increase 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 relationship to chemometrics . Chemical expert systems are also relevant, since they represent parts of chemical knowledge 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. [10]

See also

  • Bioinformatics
  • Chemical file format
  • Pathformatics toolkits
  • Chemogenomics
  • Computational chemistry
  • Data analysis
  • Journal of Chemical Information and Modeling
  • List of chemistry topics
  • Mathematical Chemistry
  • Molecular Conceptor
  • Molecular software design
  • 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 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  978-0-12-040533-6 .
  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^ Pathformatics 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, Gillet VJ:An Introduction to Chemoinformatics. Springer, 2003,ISBN 1-4020-1347-7
  7. Jump up^ Alexander Varnek and Igor Baskin (2011). “Chemoinformatics as a Theoretical Chemistry Discipline”. Molecular Informatics . 30 (1): 20-32. doi : 10.1002 / min.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. doi : 10.1021 / ci9000458 . PMID  19527020 .
  10. Jump up^