Editing Cheminformatics (section)

== Applications ==
===Storage and retrieval===
{{unreferenced section|date=February 2020}}
{{main|Chemical database}}
A primary application of cheminformatics is the storage, indexing, and search of information relating to chemical compounds.{{cn|date=March 2025}} The efficient search of such stored information includes topics that are dealt with in computer science, such as data mining, information retrieval, [[information extraction]], and [[machine learning]].{{citation needed|date=February 2020}} Related research topics include:{{citation needed|date=February 2020}}
{{div col}}
* [[Digital libraries]]
* [[Unstructured data]]
* [[Structured data mining]] and mining of [[structured data]]
**[[Database mining]]
**[[Graph mining]]
**[[Molecule mining]]
**[[Sequence mining]]
**[[Tree mining]]
{{div col end}}

==== File formats ====
{{main|Chemical file format}}
The ''in silico'' representation of chemical structures uses specialized formats such as the [[Simplified molecular input line entry specification]]s (SMILES)<ref>{{cite journal|title=SMILES, a Chemical Language and Information System: 1: Introduction to Methodology and Encoding Rules|author=Weininger, David|journal=Journal of Chemical Information and Modeling|year=1988|volume=28|issue=1|pages=31–36|doi=10.1021/ci00057a005|s2cid=5445756 }}</ref> or the [[XML]]-based [[Chemical Markup Language]].<ref>{{cite journal|title=Chemical Markup, XML, and the Worldwide Web. 1. Basic Principles|author=Murray-Rust, Peter|author2=Rzepa, Henry S.|journal=Journal of Chemical Information and Computer Sciences|year=1999|volume=39|issue=6|pages=928–942|doi=10.1021/ci990052b}}</ref>  These representations are often used for storage in large [[chemical database]]s.{{citation needed|date=February 2020}} While some formats are suited for visual representations in two- or three-dimensions, others are more suited for studying physical interactions, modeling and docking studies.{{citation needed|date=February 2020}}

=== Virtual libraries ===
{{primary sources|section|date=February 2020}}
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.<ref>{{cite journal|title=FOG: Fragment Optimized Growth Algorithm for the de Novo Generation of Molecules occupying Druglike Chemical | last=Kutchukian | first=Peter  |author2=Lou, David |author3=Shakhnovich, Eugene  |journal=Journal of Chemical Information and Modeling | year=2009 |volume=49 | pages=1630–1642|doi=10.1021/ci9000458|pmid=19527020|issue=7 }}</ref>  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 ===
{{unreferenced section|date=February 2020}}
{{main|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 (molecular)|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 product]]s is screened.

===Quantitative structure-activity relationship (QSAR)===
{{main|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 system]]s are also relevant, since they represent parts of chemical knowledge as an ''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.<ref name="Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process">{{Cite journal | doi=10.1186/s13321-014-0048-0| pmid=25544551| pmc=4272757| title=Prediction-driven matched molecular pairs to interpret QSARs and aid the molecular optimization process| journal=Journal of Cheminformatics| volume=6| issue=1| pages=48| year=2014| last1=Sushko| first1=Yurii| last2=Novotarskyi| first2=Sergii| last3=Körner| first3=Robert| last4=Vogt| first4=Joachim| last5=Abdelaziz| first5=Ahmed| last6=Tetko| first6=Igor V.| doi-access=free}}</ref>