IMPORTANCE OF CHEMOINFORMATICS IN PHARMA
- December 10, 2018
- Posted by: rasa
- Category: Cheminformatics
Chemoinformatics also known as chemical informatics / cheminformatics / chemical
information was given by F.K. Brown in 1998  . It is a tool of modern drug discovery. It is
also known as interface science at it combines all physics, chemistry, biology, biochemistry,
statics and information.
It is an inclusive term that encompasses the management, organization, creation, analysis,
visualization, and use of chemical information as a surrogate or index for other data and
knowledge  . Extraction of information from data and converting it into knowledge,
chemical informatics plays a vital link between drug design and in theoretical design. The
main aspect of chemoinformatics is to derive knowledge and information and can also be
used proactively to design and filter more relevant compounds to work with  .
NEED OF CHEMOINFORMATICS
Primary application of chemoinformatics includes storage, indexing, searching information
about the appropriate compounds  . It maintain access amount of chemical data and also
access it by using proper database.It is a significant application of information use to
organise, analyse, to solve other new problems and to understand scientific data in the
development of novel compounds  .
Role of chemoinformatics in pharma:
A huge amount of chemical is generated during the drug design process and thus
pharmaceutical community applied chemoinformatics successfully.
Prediction of properties:
It has been discovered in recent years that during the development of a new drug, it is
extremely essential to be vigilant about the optimization of its biological activity along with
ensuring whether it has the favourable physical , chemical , and biological properties such as
absorption , metabolism, distribution , excretion , and toxicity (ADME-TOX)  . Significant
work is going on in developing methods that can help predict these properties prior to the
synthesis of the respective compounds. These methods are used in the virtual screening of
large sets of compounds. The property that should especially be emphasized upon is aqueous
solubility because this property has to be in a certain range in order for a drug to be orally
administered and, along with that , to be absorbed in the body .
Analysis of analytical chemistry data:
High commercial interest can be seen in the analysis of samples to assign their quality, origin
or their age. Since complexity is observed in the relationships between the composition of a
sample and its quality , or age, chemo metrics and other inductive learning methods have
been employed since a long time  .
Computer assisted structure elucidation (CASE) :
Spectral data of various sorts is ( NMR , IR AND MS ) forms the basis of the elucidation of
the structure of a compound . The derivation of the structure of a compound from
spectroscopic data involves the processing of large amounts of information and numerous
decisions have to be made between the hosts of alternatives  .
Computer assisted synthesis design (CASD):
The consideration of many alternatives is involved in the design of the synthesis for an
organic compound . The design for synthesis is supposed to be drawn from a broad
knowledge of organic reactions , has to lay emphasis on a large selection of available starting
materials and has to consider the various economic consequences  .
Therefore it becomes one of the most tough problems in organic chemistry. It has emerged as
an attractive field of study and exercise for artificial intelligence techniques.
Recently the most important field for using Chemoinformatics methods
includes the area of drug design because of many reasons: to reduce the high costs needed for
developing a new drug and to reduce the time needed for this process there is an enormous
economic pressure, recently new experimental methods are introduced in the drug designing
process which are combinatorial chemistry and high-throughput docking  . It is clear that
prediction of biological activity of a compound can not be predicted therefore still needs
methods that learn from available data
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