Cheminformatics (also known as chemoinformatics,
chemioinformatics and chemical informatics) is the use of
computer and informational techniques applied to a range of
problems in the field of chemistry. These in silico
techniques are used in, for example, pharmaceutical
companies in the process of drug
discovery. These methods can also be used in chemical and allied industries
in various other forms.
History
The term chemoinformatics was defined by F.K. Brown in 1998:
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 spellings have been used, and some have
evolved to be established as Cheminformatics, while European Academia settled
in 2006 for Chemoinformatics. The recent establishment of the Journal of Cheminformatics is a strong
push towards the shorter variant.
Basics
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.Cheminformatics can also be applied
to data analysis for various industries like paper
and pulp,
dyes and such allied industries.
Applications
Storage and retrieval
The primary application of cheminformatics is in the
storage, indexing and search of information relating to compounds. The
efficient search of such stored information includes topics that are dealt with
in computer science as data mining, information retrieval, information extraction and machine
learning. Related research topics include:
File formats
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 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. 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
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 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
products is screened.
Quantitative structure-activity relationship (QSAR)
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 as an in silico
representation.
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