This document discusses pharmacophore approach in rational drug design. It begins by defining a pharmacophore as an abstract description of molecular features necessary for molecular recognition by a biological macromolecule. It then discusses pharmacophore mapping, methods of pharmacophore screening, and applications. Key steps in developing a pharmacophore model include selecting a training set of ligands, conformational analysis, molecular superposition, abstraction, and validation. Pharmacophore models can be used to retrieve potential drug leads from databases and design new molecules.

1. Pharmacophore approach
in Rational drug
Designing
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2. CONTENTS
1. Introduction
2. Pharmacophore Mapping
3. Drug designing
4. Methods of Pharmacophore Screening
5. Application
6. Pharmacophore Screening Software
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3.  First introduced in 1990 by “Paul Herilich”.
 A pharmacophore is an abstract description of
molecular features which are necessary for molecular
recognition of a ligand by a biological
macromolecule.
 A pharmacophore is a representation of
generalized molecular features including;
 3D (hydrophobic groups, charged/ionizable groups,
hydrogen bond
donors/acceptors)
 2D (substructures)
 1D (physical or biological)
 properties that are considered to be responsible
for a desired biological activity.
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4. PHARMACOPHOREINDRUGDESIGNAND
DISCOVERY
 A pharmacophore model is a geometrical description of the
chemical functionalities required of a ligand to interact with
receptor. Modern medicinal chemistry is to reduce the
overall cost in drug discovery, by identifying the most
promising candidates to focus on the experimental efforts.
 Experimental screening for lead structure determination
suffers from limitation with respect to the possible number of
compounds that can be submitted to a high throughput
bioassay and with low number of hits obtained that is in the
range of 0.1%.
 The pharmacophore approach has proven to be successful,
Allowing(i) the perception and understanding of key
interaction between a target and a ligand and (ii) the
enrichment of hit rates obtained in experimental screening of
sub sets that have been obtained from in silico screening
experiments. 4

5. Pharmacophore mapping is one of the major
elements of drug design in the absence of
structural data of the target receptor. It can be
used as queries for retrieving potential leads
from the structural databases, for designing
molecules with specific desired attributes and
for assessing similarity and diversity of
molecules using pharmacophore fingerprints.
It can also be used to align molecules based on
the 3D arrangement of chemical features or to
develop predictive 3D QSAR models.
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6. PROCESSFORDEVELOPINGA
PHARMACOPHOREMODEL
They generally involves the following steps:
 Select a training set of ligands – Choose a
structurally diverse set of molecules that will be
used for developing the pharmacophore model. As a
pharmacophore model should be able discriminate
between molecules with and without bioactivity, the
set of molecules should include both active and
inactive compounds.
 Conformational analysis- Generate a set of low
energy conformations that is likely to contain
bioactive conformation for each of the selected
molecules.
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7.  Molecular superposition- Superimpose(“fit”) all combination
of the low energy conformation of the molecules. Similar
(bioisosteric) functional groups common to all molecules in
the set might be fitted (e.g. Phenyl rings or carboxylic acid
groups). The set of conformations (one conformations from
each active molecule) that results in the best fit is presumed
to be the active conformation.
 Abstraction- Transform the superimposed molecules into an
abstract representation. For example, superimposed phenyl
rings might be referred to more conceptually as an ‘aromatic
ring’ pharmacophore element. Likewise, hydroxy groups
could be designated as a ‘hydrogen –bond donor/acceptor’
pharmavophoreelement.
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8.  Validation- A pharmacophore model is a
hypothesis accounting for the observed
biological activities of a setoff molecules
that bind to a common biological target.
The model is only valid so far as it is able
to account for differences in biological
activity of a range of molecules.
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9.  Pharmacophore Mapping is the definition and
placement of pharmacophoric features and the
alignment techniques used to overlay 3D.
 Two somewhat distinct usages:
 That substructure of a molecule that is
responsible for its pharmacological activity
(c.f. chromophore)
 A set of geometrical constraints between specific
functional groups
that enable the molecule to have biological activity
 The process of deriving pharmacophore
is known as pharmacophore mapping.
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10.  It consist of three steps:
(1) identifying common binding element that are
responsible for the biological activity;
(2) generating potential conformations that active
compound may adopt; and
(3) determining the 3D relationship between
pharmacophore element in each conformation
generated.
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12. The process of finding drug by design.
Based on what the drug targeting?
Metabolic or Signaling pathway
Specific for disease or pathology.
Drugs
Bind to active site & Work.
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13.  Usually pharmacophore based search are done in two
steps.
 First the software checks whether the compound has
the atom types or functional groups required by the
pharmacophore,
 than its checks whether the spatial arrangement of this
element matches the query.
 Flexible 3D searches identified a higher number of hits
than rigid searches do.
 However flexible searches are more time consuming than
rigid
 ones.
 There are two main approaches for including
conformational flexibility in to the search
 one is top generate a user defined number of representative
conformation for each molecules when the database is to
created,
 the other is to generate conformation during the search.
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14.  Pharmacophore model provide powerful filter tools for
virtual screening even in case where the protein
structure is not available, pharmacophore filter are
much faster than docking approaches, and there for
greatly reduce the number of compound subjected to
the more expensive docking application.
 Another interesting aspect of pharmacophore in
virtual screening is 3D- pharmacophore diversity.
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16. 2-D PHARMACOPHORE
SEARCHING
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 Searching of 2D database is of great importance for
accelerating the drug discovery different strategies are
pursued to search a2D database to identified the
compound of the interest Substructure search
identified larger molecules that contain user define
query irrespective of the environment in which the
query substructure occur.
 Biochemical data obtainable from these compounds
can be used for generating structure-activity-
relationship (SAR) even before synthetic plans are
made for leadoptimization.
 In contrast, superstructure search are used to
find smaller molecules that are embedded in the
query.

17.  Beyond structure similarity, activity similarity
has also been subject of several studies.
 Similarity search can be combined with substructure
for limiting
the number of compound selected.
 Flexible searches are used to identify the
compound that differs from the query structure in
user-specified ways.
 3-D Pharmacophore searching
1.Ligand based pharmacophore generation
 Ligand based pharmacophores are generally used
when crystallographic;solution structure or molded
structure of protein cannot be obtained.
 When a set of active compound is known and it is
hypothesized that all the compounds bind in the
similar way to the protein, then common group should
interact with the same protein residue.
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18.  Thus, a pharmacophore capturing this compound
feature should be able to identified from a database
novel compounds that binds to the same site of the
protein as the known compounds do.
2. Manual pharmacophore generation
 Manual pharmacophore generation is used when there
is an easy way to identify the common feature in a
set of active compounds and/or there is experimental
evidence that same functional groups should be
present in the ligand for good activity.
 An example is the development of a
pharmacophore model for dopamine-transporter
(DAT) inhibitor.
 Pharmacophores should also have some flexibility
built in, thus
justifying the use of distance ranges.
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19. 3. Automatic pharmacophore generation
 Pharmacophore generation through conformational
analysis and manual alignment is a very time
consuming task, especially when the list of the
active ligands is large and the elements of the
pharmacophore model are not obvious.
 There are several programs Hip Hop, Hypogen,
Disco, Gaps, flo, APEX, and ROCS, that can
automatically generate potential pharmacophore
from a list of known inhibitors.
 The performance of these programs in automated
pharmacophore generation varies depending on the
training set.
 These all program use algorithms that identified the
common pharmacophore features in the training set
molecules; they scoring function to rank the
identified pharmacophores.
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20.  4. Receptor based pharmacophore generation
 If the 3D structure of receptor is known, a
pharmacophore model can be derived based on the
receptor active site.
 Biochemical data used to identify the key residue that
is important for substrate and/or inhibiting binding.
 This information can be used for binding
pharmacophores targeting the region defined by key
residue or for choosing among pharmacophore
generated by automated program.
 This can greatly improve the chance of finding small
molecules that inhibit the protein because the
search is focused on a region of the binding side
that is crucial for binding substrate and inhibitors.
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22.  Discovery studio :
 Window ® and Linux® based protein
modeling software.
 Produced by Accelrys software company.
 Easy to use interface.
 Examples of the programs that perform
pharmacophore based searches are 3D search UNITY,
MACCS-3D and ROCS.
 ROCS is using as shape based super position
for identifying compound that have similar
shaped.
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