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 History of Computers in Pharmaceutical Research and


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 Today, computers are so ubiquitous(found
everywhere) in pharmaceutical research and
development that it may be hard to imagine a time
when there were no computers to assist the medicinal
chemist or biologist.

 Now, computers are absolutely essential for
generating, managing, and transmitting information.

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 Computers are used for drug discovery.
 Computers began to be deployed at pharmaceutical

companies as early as the 1940s.
 These early computers were usually for the payroll and

for accounting, not for science.
 Pharmaceutical scientists did eventually gain access to


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 There were several scientific and engineering advances
that made possible a computational approach to what
had long been exclusively an experimental art and
science, namely, discovering a molecule with useful
therapeutic potential.

 One fundamental concept understood by chemists was
that chemical structure is related to molecular
properties including biological activity.

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 Another fundamental, well-established concept was
that a drug would exert its biological activity by
binding to and/or inhibiting some biomolecule in the

 This concept stems from Fischer’s famous lock-and-
key hypothesis(which states that enzymes have a
specific shape that directly correlates to the shape of
the substrate. Basically, substrates fit into an enzyme
the way a key fits into a lock)

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 Pioneering research in the 1950s attacked the problem
of linking electronic structure and biological activity.

 In 1964, the role of molecular descriptors in describing
biological activity was reduced to a simplified
mathematical form, and the field of quantitative
structure activity relationships (QSAR) was propelled
toward its modern visage(the face) . (A descriptor is
any calculated or experimental numerical property
related to a compound’s chemical structure.)

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 Quantitative Structure Activity Relationships:
 Structure-Activity Relationship (SAR) is an approach

designed to find relationships between chemical
structure (or structural-related properties) and
biological activity (or target property) of studied

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 People in the pharmaceutical industry to
perceive(come to understand.) that computer-aided
drug design was something that might be practical
and worthy of investigation.

 He pioneered a sustained, industrial research program
to use computers in drug design.

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 We can state confidently that in 1960 essentially 100%

of the computational chemists were in academia, not
industry. Of course, back then they were not called
computational chemists, a term not yet invented.

 The students coming from those academic
laboratories constituted the main pool of candidates
that industry could hire for their initial ventures into
using computers for drug discovery.

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 Thus was born The Quantum Chemistry Program
Exchange (QCPE) was born in 1962 .

 QCPE: Its purpose was to provide an inexpensive
mechanism for theoretical chemists and other
scientists to exchange software.

 QCPE proved instrumental in advancing the field of
Computational chemistry including that at
pharmaceutical companies.

 QCPE was the main resource for the entire community.

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 In the 1960s, drug discovery was by trial and error.
 The smaller pipeline was natural products, such as soil

microbes that produced biologically active
components or plants with medicinal properties.

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 A lead compound would be discovered by biological
screening or by reading the patent and scientific
literature published by competitors at other
pharmaceutical companies.

 From the lead, the medicinal chemists would use their
ingenuity(inventive), creativity, and synthetic
expertise to construct new compounds.

 These compounds would be tested by the appropriate
in-house pharmacologists, microbiologists.

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 The most potent compounds found would become the
basis for another round of analog design and synthesis.

 Thus would evolve in countless interactions a structure-
activity relationship (SAR), which in summary would
consist of a table of compounds and their activities.

 Structure–activity relationship(is the relationship between
the chemical or 3D structure of a molecule and its
biological activity.

 The analysis of SAR enables the determination of the
chemical group responsible for evoking(bring) a target
biological effect in the organism)

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 The only theoretical methods of the 1960s that could
treat drug-sized (200–500 Da) molecules were
inaccurate and limited.

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 Regarding hardware of the 1970s, pharmaceutical

companies invested money from the sale of their
products to buy better and better mainframes.

 Widely used models included members of the IBM
360 and 370 series.

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 Software was still written primarily in FORTRAN(is a third-
generation ( 3GL ) programming language ), now mainly
FORTRAN IV. The holdings of QCPE(Quantum Chemistry
Program Exchange) expanded.

 The prominent position of quantum mechanics led a
coterie(a small group of people with shared interests ) of
academic theoreticians to think that their approach could
solve research problems facing the pharmaceutical

 Quantum mechanics: which describes nature at the
smallest scales of energy levels of atoms and subatomic

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 These theoreticians, who met annually in Europe and
on Sanibel Island in Florida, invented the new
subfields of quantum biology and quantum
pharmacology .

 Computational chemists in the pharmaceutical
industry also expanded from their academic
upbringing by acquiring an interest in force field
methods, QSAR, and statistics.

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 The situation was that as medicinal chemists pursued
an SAR, calculations by the computational chemists
might suggest a structure worthy of synthesis.

 Maybe the design had the potential of being more
active. But the computational chemist was totally
dependent on the medicinal chemist to test the

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 Another problem was that on a computer it was easy to
change a carbon to a nitrogen or any other element.

 It was easy to attach a substituent at any position in
whatever stereochemistry seemed best for enhancing
activity. It was easy to change a six-member ring to a
five-member ring or vice versa.

 Such computer designs were frequently beyond the
possibilities of synthetic organic chemistry, or at least
beyond the fast-paced chemistry practiced in industry.

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 For organic chemists who had never used a computer,
it was necessary to gently dispel the notion that one
could push a button on a large box with blinking lights
and the chemical structure of the next $200 million
drug would tumble into the output tray of the
machine. (Back in those days, $200 million in annual
sales was equivalent to a blockbuster drug.)

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 Two new computer-based resources were launched in
the 1970s. One was the Cambridge Structural Database
(CSD), and the other was the Protein Data Bank

 Computational chemists recognized that these
compilations of 3D molecular structures would prove
very useful, especially as more pharmaceutically
relevant compounds were deposited.

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 Dr. Yvonne Martin began her scientific career as an
experimentalist in a pharmaceutical laboratory, but
after becoming interested in the potential of QSAR she
spent time learning the techniques at the side of Prof.
Corwin Hansch and also Prof. Al Leo of Pomona
College in California.

 As mentioned in her book, she encountered initial
resistance to a QSAR approach at Abbott Laboratories.

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 The decade of the 1980s was when the various

approaches of quantum chemistry, molecular
mechanics, molecular simulations, QSAR, and
molecular graphics coalesced(combine) into modern
computational chemistry.

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 The pharmaceutical companies certainly noticed the
development of the IBM personal computer (PC), but
its DOS (disk operating system) made learning to use
it difficult.

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 The second important software advance was
ChemDraw, which was released first for the Mac in
1986 . This program gave chemists the ability to
quickly create two-dimensional chemical diagrams.

 Every medicinal chemist could appreciate the
aesthetics(relating to) of a neat ChemDraw diagram.

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 The third software advance also had an aesthetic

 This was the technology of computer graphics, or
when 3D structures were displayed on the computer
screens, molecular graphics.

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 Lilly’s infrastructure was described at a national
meeting of the American Chemical Society in 1982.

 A few years later, a survey was conducted of 48
pharmaceutical and chemical companies that were
using computer-aided molecular design methods and
were operating in the United States .

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 As a result of this marketing, most pharmaceutical
companies purchased the software packages. The
computer-aided drug design (CADD) was oversold.

 Also in the 1980s, structure-based drug design (SBDD)
underwent a similar cycle.
Structure-based drug design (relies on knowledge of

the three dimensional structure of the biological target
obtained through methods such as x-ray crystallography
or NMR spectroscopy.)

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 Another factor was that there were only a few drug
targets that had their 3D structures solved prior to the
advancing methods for protein crystallography of the

 This protein became a favorite target of molecular
modeling/drug design efforts in industry and
elsewhere in the 1980s.

 In Japan, Koga employed classic (Hansch-type) QSAR
while discovering the antibacterial agent norfloxacin
around 1982.

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 As computers and software improved, SBDD became a
more popular approach to drug discovery.

 The insatiable need for more computing resources in
the 1980s sensitized the pharmaceutical companies to
the technological advances leading to the manufacture
of supercomputers.

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 By the mid-1980s, for example, several pharmaceutical
companies had acquired the Floating Point System
(FPS) 164(Attached Processor).

 Other pharmaceutical companies sought to meet their
needs by buying time and/or partnerships with one of
the state or national

 supercomputing centres formed in the United States,
Europe, and Japan.

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 The 1990s was a decade of fruition because the

computer-based drug discovery work of the 1980s
yielded an impressive number of new chemical entities
reaching the pharmaceutical marketplace.

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 Ultimately, then, supercomputing would speed the
identification of promising new drug candidates.

 Scientists closer to the task of using the
supercomputer saw the machine primarily as a tool for
performing longer molecular dynamics simulations
and quantum mechanical calculations on large

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 However, if some other computational technique such
as QSAR or data mining was more effective at
discovering and optimizing new lead compounds,
then the supercomputer might not fulfil the dreams
envisioned for it.

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 Software use for drug discovery and development can
be classified in various ways.

 One way is technique based. Examples would be
programs based on force fields or on statistical fitting
(the latter including log P(which is the logarithm of its
partition coefficient between n-octanol and water )
prediction or toxicity prediction).

 Another way to classify software is according to
whether the algorithm can be applied to cases in
which the 3D structure of the target receptor is known
or not.

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 An example of software useful when the receptor
structure is not known is Catalyst .

 This program, which became available in the early
1990s, tried to produce a 3D model of
pharmacophore(a part of a molecular structure that is
responsible for a particular biological or
pharmacological interaction that it undergoes) based
on a small set of compounds with a range of activities
against a given target.

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 1990s saw more crystal structures of pharmaceutically
relevant proteins being solved and used for ligand
design(design of a molecule that will bind tightly to its

 A new generation of managers at pharmaceutical
companies now realized that computer-assisted
molecular design and library design were critical
components of their company’s success.

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 Following the example of other industries, some
pharmaceutical companies turned over the technical
work of managing their networks of PCs to outside

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 QSAR proved to be one of the best approaches to
providing assistance to the medicinal chemist in the
1990s. Computational chemists were inventive in
creating new molecular descriptors.

 Authors have always felt strongly that the term
“computer-aided drug design” should be more than
just doing a calculation; it should be providing
information or ideas that directly help with the
conception of a useful new structure.

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 The computational techniques used to find these
seven compounds included QSAR, molecular orbital
calculations, molecular modeling, molecular shape
analysis ,docking, active analog approach,molecular
mechanics, and SBDD.

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 Computer-based technologies are clearly making a
difference in helping bring new medicines to patients.

 Papers that were indexed as pertaining to “computer or
calculation” and that came from pharmaceutical

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 First, industrial scientists do publish.
 Second, the figure includes a list of pharmaceutical

companies that have done the most publishing of
papers pertaining to computers or calculations.

 since 1994 the sum of the annual number of papers
published by the 15 companies has zigzagged around
325 papers per year.

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 Demand for computational chemists leaped to new
highs each year in the second half of the 1990s.

 Most of the new jobs were in industry, and most of
these industrial jobs were at pharmaceutical or
biopharmaceutical companies.

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 In 1960 there were essentially no computational
chemists in industry. But 40 years later, perhaps well
over half of all computational chemists were working
in pharmaceutical laboratories.

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 The outlook for computational chemistry is therefore
very much linked to the health of the pharmaceutical
industry itself.

 Forces that adversely affect pharmaceutical companies
will have a negative effect on the scientists who work
there as well as at auxiliary companies such as software
vendors that develop programs and databases for use
in drug discovery and development.

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 By 2003, R&D investments had grown to $34,500

 In 2004, the total jumped to $38,800 million.
 The United States pharmaceutical industry invests far

more in discovering new and better therapies than the
pharmaceutical industry in any other country or any
government in the world.

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 The computational chemist’s goal should be to help
the medicinal chemist by providing information about
structural and electronic requirements to enhance
activity, namely, information about which regions of
compound space are most propitious for exploration.

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 Fortunately, all the effort that goes into
pharmaceutical R&D does benefit society.

 In nations where modern medicines are available, life
expectancy has increased and disability rates among
the elderly have declined.

 Considering all of the things that can go wrong with
the human body, many challenges remain for the
pharmaceutical researcher.

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 Hopefully, this chapter will inspire some young readers to
take up the challenge and join the noble quest to apply
science to help find cures to improve people’s lives.

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 Computer Applications in Pharmaceutical Research

and Development,Sean Ekins,2017,John Wiley &
Sons.Page no:03 to 38

 Computer Aided Applications in Pharmaceutical
Technology, 1st Edition ,Jelena Djuris, Woodhead

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