history and current status of chemoinformaticss

8/10/2019 HISTORY AND CURRENT STATUS OF CHEMOINFORMATICSs

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specialised hardware. Also rapid access to not only the primary literature but,

possibly even more importantly, to the factual primary data about millions of

chemical compounds, to reactions, structures, and spectra, and to the genomic

data of various organisms including humans, can only be provided by digital

storage and retrieval techniques.

The roots of what we now call cheminformatics began very early in the history of

computing: 1950's for statistical models, 1960's for first computer

representations, mainly by curious chemists. However the term

"cheminformatics" wasn't adopted until the early 1990's (the spelling of this -

cheminformatics or chemoinformatics - is still in dispute). The bulk of the

foundational work was done in the 70's and 80's, and was strongly supported by

the pharmaceutical industry and the need for computational drug discovery

research.

The term chemoinformatics was defined by F.K. Brown in 1998. With the advent

of computers and the ability to store and retrieve chemical information, serious

efforts to compile relevant databases and construct information retrieval systems

began. One of the first efforts to have substantial long term impact was to collect

crystal structure information for small molecules by Olga Kennard.

Chemoinformatics is the mixing of those information resources to transform datainto 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.

Thefirst, and still the core, journal for the subject, The Journal of Chemical

Documentation, started in 1961( the name changed to The Journal of

Information and Computer Science in 1975)

The first book appeared in 1971 (Lynch, Harrison, Town and Ash, Computer

Handling of Chemical Structure Information)


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The first international conference on the subject was held in 1973 at

Noordwijkerhout and every three years since 1987.

HOW CAN CHEMOINFORMATICS HELP ??

Cheminformatics can help chemists and other scientists produce andmanage information. In silico analysis using cheminformatics

techniques can actually reduce the risks of developing a drug. Such

techniqes as virtual screening, library design, and docking figure into

the analysis. Physical properties that might have an impact on whether

a substance could potentially be developed as a drug are often

examined in cheminformatics as features that can be compared among

large numbers of substances. An example is clogP, a measure of the

amount of fattiness in the system. Sometimes, inferences can be drawn

about a related set of properties, as when Chris Lipinski formulated his

now famous Rule of Five that says that compounds which are drug-like

tend to have 5 or fewer hydrogen donor atoms, 10 or fewer hydrogen

acceptor atoms, calculated logP less than or equal to 5, and molecular

weight up to 500. Compounds that exhibit greater than these values

tend to have poor absorption or permeation.


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CURRENT STATUS OF

CHEMOINFORMATICS

In recent years, there has been an explosion in the availability of publicly

accessible chemical information, including chemical structures of small molecules,

structure-derived properties and associated biological activities in a variety of

assays. These data sources present us with a significant opportunity to develop

and apply computational tools to extract and understand the underlying

structure-activity relationships. Furthermore, by integrating chemical data

sources with biological information (protein structure, gene expression and so

on), we can attempt to build up a holistic view of the effects of small molecules in

biological systems. Equally important is the ability for non-experts to access and

utilize state of the art cheminformatics method and models.

The chemoinformatics field continues to evolve at the interface between

computer science and chemistry. Chemical information and computational

approaches in pharmaceutical research are major focal points of

chemoinformatics. However, the boundaries of this discipline are rather fluid and

the chemoinformatics spectrum is difficult to delineate.

In the area of methodology development, recent work on characterizing

structure-activity landscapes, Quantitative Structure Activity Relationship (QSAR)


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model domain applicability and the use of chemical similarity in text mining has

been done. In the area of infrastructure, a distributed web services framework

that allows easy deployment and uniform access to computational (statistics,

cheminformatics and computational chemistry) methods, data and modelshas

been done. The development of PubChem derived databases and highlight

techniques allow us to scale the infrastructure to extremely large compound

collections, by use of distributed processing on Grids.

SCOPE OF CHEMOINFORMATICS Representation and structure searching

Substructure searching

Similarity searching, clustering Diversity analysis

Searching databases

Computer-aided structure elucidation

3-D substructure searching QSAR and Docking

APPLICATIONS OF CHEMOINFORMATICS

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


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mining, information retrieval, information extraction and machine learning.

Related research topics include:

Unstructured data

Information retrieval

Information extraction

Structured Data Mining and mining of Structured data

Database mining

Graph mining

Molecule mining

Sequence mining

Tree mining

Digital libraries

File formats

The in silico representation of chemical structures uses specialized formats suchas 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. [9] This was done by using cheminformatic tools to


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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.

MORE ABOUT CHEMOINFORMATICS

Implementing, handling and searching chemical databases is a crucial aspect of

chemoinformatics . Chemical database techniques and data mining methods will

improve as this field evolves, also due to more implementation of new data

structures . Methods for full text data mining are likely to be become very

powerful in the years to come, and will presumably play a highly important role in

the general area of chemoinformatics.

Figure 1 shows the number of references to the words bioinformatics,

chemoinformatics, chemogenomics and metabonomics in PubMed from 1992

to 2004. It is seen that the present trend for chemoinformatics resembles the


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trend in bioinformatics five to ten years ago. It should be mentioned that this

graph is based on one database only, PubMed, and is intended to give an idea

about the development in publishing frequency in these areas, and not as a

complete overview.

RECENT ADVANCES IN THE AREA OF DRUG DESIGNING WITH

THE HELP OF CHEMOINFORMATICS

It is clear that the drug discovery and optimization process is

undergoing very significant changes. Many more hits are found than

previously, especially due to the advances gained in combinatorial

chemistry and high throughput screening (HTS). The approach used in

drug discovery has been linear with respect to various relevant

properties, but more parallel approaches are evolving, where not only

the potency (activity) and selectivity of the lead is examined at an early

stage, but also other key properties. Many of the compounds drawn

out of combinatorial libraries may look promising at first, but they fail

at later stages in the drug discovery process due to undesired

properties. A compound can for example be feasible based onmolecular structure, but due to aggregation, limited solubility or limited

uptake in the human organism it is not useful as a drug. Many

pharmaceutical companies might even be repeating the same mistakes,

due to these problems. Methods for assessing these properties at a


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very early stage, both experimentally and computationally, are thus

highly desirable. This is expected to lower the cost of drug discovery

and optimization significantly, and hopefully provide an increased

number of useful leads. In cases where a lead has sufficiently highactivity, but various properties need to be improved, chemoinformatics

methods could be used to modify substructures within the lead space

with minimal effect on the activity profile.

Likewise, various computational methods are evolving rapidly at present.

Computational techniques used to search through chemical libraries and

databases, so-called virtual screening methods, have become increasingly popular

in drug discovery. A whole range of computational techniques are used for

searching for molecular similarities and dissimilarities, for extracting information

about pharmacophores (structural models of targets or binding sites) from

compound libraries, for prediction of properties, for studying molecular

interactions at the atomic level, among other things Chemoinformatics is strongly


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linked to computational chemistry and molecular modeling. Molecular modeling

methods are particularly useful for conducting conformational analysis of

molecules, and for accessing the strength of intermolecular interactions.

Newly established fields like chemogenomics (or chemical genomics),metabonomics and metabolomics also play increasingly important roles in

modern drug discovery and development. Chemogenomics (Browne et al., 2002)

deals with interactions between chemical compounds and living systems in terms

of induced genomic response. In metabonomics (Nicholson and Wilson, 2003)

relatively low-molecular weight materials produced during genomic expression

within a cell are studied, normally by use of1 H-NMR spectroscopy and

multivariate data analysis (chemometrics) (Geladi and Kowalski 1986b,a). It has

been shown to be a useful tool for understanding drug efficacy and toxicity.

Metabolomics is similar to metabonomics, but where metabonomics deals with

integrated, multicellular, biological systems, metabolomics deals with simple cell

systems.

CONCLUSION

Chemoinformatics is a rapidly growing field, with a huge application potential.

Chemoinformatics concerns the gathering and systematic use of chemical

information, and the use of those data to predict the behavior of unknown

compounds in silico.

history and current status of chemoinformaticss

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