 | This articleneeds additional citations forverification. Please helpimprove this article byadding citations to reliable sources. Unsourced material may be challenged and removed. Find sources: "Cheminformatics" – news ·newspapers ·books ·scholar ·JSTOR(February 2020) (Learn how and when to remove this message) |
Cheminformatics (also known aschemoinformatics) refers to the use ofphysical chemistry theory withcomputer andinformation science techniques—so called "in silico" techniques—in application to a range of descriptive and prescriptive problems in the field ofchemistry, including in its applications tobiology andrelated molecular fields. Suchin silico techniques are used, for example, bypharmaceutical companies and in academic settings to aid and inform the process ofdrug discovery, for instance in the design of well-definedcombinatorial libraries of synthetic compounds, or to assist instructure-based drug design. The methods can also be used in chemical and allied industries, and such fields asenvironmental science andpharmacology, where chemical processes are involved or studied.[1]
Cheminformatics has been an active field in various guises since the 1970s and earlier, with activity in academic departments and commercial pharmaceutical research and development departments.[2][page needed][citation needed] The term chemoinformatics was defined in its application to drug discovery by F.K. Brown in 1998:[3]
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 terms, cheminformatics and chemoinformatics, have been used,[citation needed] although,lexicographically, cheminformatics appears to be more frequently used,[when?][4][5] despite academics in Europe declaring for the variant chemoinformatics in 2006.[6] In 2009, a prominent Springer journal in the field was founded by transatlantic executive editors named theJournal of Cheminformatics.[7]
Cheminformatics combines the scientific working fields of chemistry, computer science, and information science—for example in the areas oftopology,chemical graph theory,information retrieval anddata mining in thechemical space.[8][page needed][9][page needed][10][11][page needed] Cheminformatics can also be applied to data analysis for various industries likepaper andpulp, dyes and such allied industries.[12]
| This sectiondoes notcite anysources. Please helpimprove this section byadding citations to reliable sources. Unsourced material may be challenged andremoved.(February 2020) (Learn how and when to remove this message) |
A primary application of cheminformatics is the storage, indexing, and search of information relating to chemical compounds.[citation needed] The efficient search of such stored information includes topics that are dealt with in computer science, such as data mining, information retrieval,information extraction, andmachine learning.[citation needed] Related research topics include:[citation needed]
Thein silico representation of chemical structures uses specialized formats such as theSimplified molecular input line entry specifications (SMILES)[13] or theXML-basedChemical Markup Language.[14] These representations are often used for storage in largechemical databases.[citation needed] 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]
| This sectionrelies excessively onreferences toprimary sources. Please improve this section by addingsecondary or tertiary sources. Find sources: "Cheminformatics" – news ·newspapers ·books ·scholar ·JSTOR(February 2020) (Learn how and when to remove this message) |
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.[15] This was done by using cheminformatic tools to train transition probabilities of aMarkov chain on authentic classes of compounds, and then using the Markov chain to generate novel compounds that were similar to the training database.
| This sectiondoes notcite anysources. Please helpimprove this section byadding citations to reliable sources. Unsourced material may be challenged andremoved.(February 2020) (Learn how and when to remove this message) |
In contrast tohigh-throughput screening, virtual screening involves computationallyscreeningin silico libraries of compounds, by means of various methods such asdocking, to identify members likely to possess desired propertiessuch asbiological 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 ornatural products is screened.
This is the calculation ofquantitative structure–activity relationship andquantitative structure property relationship values, used to predict the activity of compounds from their structures. In this context there is also a strong relationship tochemometrics. Chemicalexpert systems are also relevant, since they represent parts of chemical knowledge as anin silico representation. There is a relatively new concept ofmatched molecular pair analysis or prediction-driven MMPA which is coupled with QSAR model in order to identify activity cliff.[16]
