COMPUTER AIDED DRUG DESIGN

1. CADD Sarita Maurya
Assignment-3
1.What is pharmacophore? Explainpharmacophore modeling
2.What is pharmacophore mapping? Explainfollowing methods for mapping of
pharmacophore
a)Constrainedsystemic searchapproach,
b). Clique detection,
c). Maximumlikelihoodapproach
3. Discuss HipHoptheory indetail for pharmacophore genration.
4. Discuss applications of pharmacophore withrefrence tofollowings
a) Pharmacophore basedde-novodesignof ligands
b) Pharmacophore based leadoptimization
1.What is pharmacophore? Explainpharmacophore modeling
Ans:- What is pharmacophore?
• "a molecular framework that carries (phoros) the essential features
responsible for a drug’s (=pharmacon's) biological activity is known as
pharmacophore" (Paul Ehrlich in 1909).
• The spatial arrangement of chemical groups or features in a molecule that are
known or thought to determine its activity. The most popular pharmacophore
models consist of three or four points separated by defined distance ranges.
• Specific 3D arrangement of chemical groups common to active molecules and
essential to their biological activities.
• Pharmacophore is the ensemble of steric and electron features that is
necessary to insure the optimal molecular interactions with a specific biological
targets and to trigger/block its biological response (Camille G. Wermuth).
pharmacophore modelling-
• Step 1: Selection of a set of high affinity ligand
• Step 2: Identification of pharmacophore features (Probes)
• Step 3: Conformational search

2. • Step 4: Generate pharmacophore hypothesis (model)
Step 1: Selection of a set of high affinity ligand
• Usually ligand are selected considering most relevant structural features of
biological active molecules.
• Sources: Chemical libraries/ Combinatorial libraries.
Step 2: (functional group) feature extraction
The functional group considered essential/important for biological
activity and seems relevant to pharmacophore pattern are identified
in each ligand:
A. Function based:
• H-bond acceptor
• H-bond Donar
• Base (+ Charge)
• Acid (- Charge)
• Aromatic ring
• Hydrophobic group
B. Topological based: In some methods atoms are grouped into topological
features i.e. phenyl ring & carbonyl group.
C. Atom based: Features define by the 3D position of an atom, associated with
atom type.
Step 3: Conformational search
• Its generally depend on the flexibility of the compounds under investigation
Strategies:
• Generally to find the receptor bound conformation we explore all possible
conformation of the ligand molecule
and consider the minimum energy conformation as receptor bound
conformation.

3. 2.What is pharmacophore mapping? Explain following methods for mapping
of pharmacophore?
pharmacophore mapping
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.
 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.
Methods of Pharmacophore Mapping-
Various methods have been devised for performing pharmacophore mapping;
the most commonly used techniques are-
• Constrained systemic search approach,
• Clique detection,
• Maximum likelihood approach,
• Genetic algorithm based approach
• Constrained systemic search approach-
• The constrained systematic search approach has its origins in the
development of efficient algorithms for systematically exploring
conformational space.
• systematic conformational search methods have an exponential dependence
on the number of rotatable bonds in the molecule.
– By making use of tree-pruning methods to eliminate unfavourable, high-
energy conformations the efficiency of these algorithms can be considerably
improved.
• The constrained systematic search procedure for pharmacophore generation

4. further improves the efficiency by deriving additional constraints on the
torsional angles.
• The first part of the procedure is to identify the pharmacophoric groups in
each molecule that will be overlaid in the final pharmacophore. The most rigid
molecule is then taken and its conformational space explored. – During the
conformational search the distances between all pairs of the selected
pharmacophoric groups are recorded.
• The second most rigid molecule is then taken, and using the inter-
pharmacophore distance ranges derived from the first molecule, constraints on
the values permitted to each of its torsion angles are derived.
– Thus the only torsion angle values explored for the rotatable bonds in the
second molecule are those that may permit it to match the pharmacophore
distances found for the first molecule.
• As more and more molecules are considered so the distance ranges become
more and more restricted.
• When the more flexible compounds are considered the expectation is that
only very limited ranges are possible on each of its torsion angles, making the
search very efficient.
• A drawback of the constrained systematic search approach is that it is
necessary to specify manually beforehand which pharmacophoric groups in
each molecule are involved in interactions with the receptor, and the
correspondences between these groups.
– It may be difficult to do this when there are many potential pharmacophores
in the molecules, giving rise to many possible matches.
Clique Detection-
• A clique in a graph is a completely connected sub- graph. It contains a subset
of the nodes in the graph such that there is an edge between every
pair of nodes in the sub-graph.
– A key characteristic of a clique is that it cannot be extended by the addition of
extra nodes. – A maximum clique is the largest such sub-graph that is
present.

5. Maximum Likelihood Method
• The maximum likelihood method (Catalyst/HipHop) also uses a pre- calculated
set of low-energy conformations.
• Typically, these are obtained using poling, a conformational search
method designed to generate a relatively small set of conformations that
“covers” pharmacophore space.
• The poling method adds an additional penalty term to the energy function
during the minimisation part of the conformational analysis.
– This penalty term has the effect of “pushing” the conformation away from
those found previously. Having generated a set of conformations for each
molecule,
• the first step in the maximum likelihood method is to identify all
configurations of pharmacophoric groups that are present in the molecules.
• Each molecule is taken in turn as a reference structure and its conformations
examined.
• All possible combinations of pharmacophoric groups contained within it
are generated exhaustively (e.g. donor– acceptor–aromatic ring centroid;
acceptor–acceptor–hydrophobe–donor).
• Each of these configurations of pharmacophoric groups in 3D space is then
compared to the other molecules in the set in order to determine whether
they possess a conformation that can be successfully superimposed on the
configuration.
• In this step it is not required that all molecules match all of the features
(i.e. a molecule can be active despite lacking a feature that is present in
the binding motif of other active molecules).
• This first step can generate a large number of possible configurations which
are then scored and ranked according to how well the molecules map onto
them and also according to their “rarity”.

6. • The general strategy is to score more highly those configurations (referred to
as hypotheses) that are well matched by the active molecules in the set but
which are less likely to be matched by a large set of arbitrary molecules.
3. Discuss HipHop theory in detail for pharmacophore genration.
Pharmacophore Generation: methods
On the basis of Information-
1. Ligand based pharmacophore generation
2. Receptor based pharmacophore generation
On the basis of approach-
1. Manual Pharmacophore Generation
2. Automatic pharmacophore generation
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
compounds bind in the similar similar way to the protein, protein, then common
group should interact with the same protein residue.
Thus, a pharmacophore capturing this compound feature should be able to
identify from a database novel compounds that binds to the same site of the
protein as the known compounds do. The process of deriving pharmacophore is
known as pharmacophore modeling/ mapping.
Pharmacophore modeling/ mapping consist of four main steps:
–Identifying common binding element that are responsible for the biological
activity
– Feature extraction
– Generating potential conformations that active compound may adopt;
– Determining the 3D relationship between pharmacophore elements in each
conformation generated.

7. 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 information can be used for binding binding pharmacophores
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.
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 evidence that same functional 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.
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 HipHop, Hypogen, Disco, Gaps, flo, APEX, and ROCS,
that can automatically automatically generate 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.
HipHop

8. Feature-based pharmacophore modeling:
– No activity data required
– Identifies binding features for drug-receptor interactions
– Generates alignment of active leads
– The flexibility is achieved by using multiple conformers
– Alignment can be used for 3D-QSAR analysis
Hiphop provides feature-based alignment of a collection of compounds without
considering activity. Hiphop matches the chemical features of a molecule,
against drug candidate molecules or searchs of 3D databases. The resulting
hypotheses can be used to iteratively search chemical databases to find new
lead candidates.
• Hiphop perform an exhaustive search starting with the simplest
pharmacophore configurations i.e. all possible combinations of two-feature
pharmacophore.
• Once all two-feature are exhausted it then move to the three-features
combinations.
• The process continues until HipHop can no longer generate common
pharmacophore combinations.
Conceptually, the approach by Hiphop is very similar to the constructive phase
of HypoGen. The objective is to identify and enumerate all possible
pharmacophore configurations that are common to the training set.
4. Discuss applications of pharmacophore with refrence to followings
a) Pharmacophore based de-novo design of ligands
b) Pharmacophore based lead optimization
a) Pharmacophore based de-novo design of ligands
Introduction
• The computer-based design of hit and lead structure candidates has emerged
as a complementary approach to high-throughput
screening.

9. • Although many challenges remain, de novo design supports drug discovery
projects by generating novel pharmaceutically active agents with desired
properties in a cost- and time-efficient manner.
• Pharmacophore can be utilized in de novo design of ligands.
• The compounds obtained from pharmacophore-based VS are usually existing
chemicals, which might be patent protected.
• In contrast to pharmacophore-based VS, the de novo design approach can be
used to create completely novel candidate structures that conform to the
requirements of a given pharmacophore.
NEWLEAD
• The first pharmacophore- based de novo design program is NEWLEAD which
uses as input a set of disconnected molecular fragments that are consistent with
a pharmacophore model, and the selected sets of disconnected pharmacophore
fragments are subsequently connected by using linkers (such as atoms, chains
or ring moieties).
Limitations-
– NEWLEAD can only handle the cases in which the pharmacophore
features are concrete functional groups (not abstract chemical
features).
– Other shortcomings of the NEWLEAD program include that the
sterically forbidden region of the binding site is not considered and
that, as in traditional de novo design programs, the compounds
created by the NEWLEAD program might be difficult to chemically
synthesize.
PhDD (a pharmacophore-based de novo design method of
drug-like molecules)-
– PhDD can automatically generate drug-like molecules that
satisfy the requirements of an input pharmacophore hypothesis.
– The pharmacophore used in PhDD can be composed of a set of

10. abstract chemical features and excluded volumes that are the
sterically forbidden region of the binding site.
1) PhDD first generates a set of new molecules that completely
conform to the requirements of the given pharmacophore
model.
2) Then a series of assessments to the generated molecules are
carried out, including assessments of drug-likeness, bioactivity and synthetic
accessibility.
de novo design Vs pharmacophore searching
de novo design and pharmacophore searching are two ways for lead generation.
The difference between them is that –
– de novo design generates totally novel molecules, whereas pharmacophore
searching identifies existing molecules, which can be easily obtained from
databases of compounds.
Thus, pharmacophore searching is faster and easier. However, in the absence of
existing active ligands, only de novo design is applicable.
lead identification/optimization is the one of the most important steps in drug
development. The chemical structure of the lead compound is used as a starting
point for chemical modifications in order to improve potency,selectivity or
pharmacokinetic parameters. Once a molecule is identified,the next step to the
check its ADMET( absorption, distribution, metabolism,excretion and toxicity)
properties.
- if the molecule has no toxicity no mutagenicity either, it has potential for use
as lead molecule. Further optimization gives better quantity of lead molecules .
These may subsequently be developed as drug.
- lead optimization is a subsequent tool to lead generation specifiacally , the
compounds synthesized during lead optimization are generally similar to the
original leads. But they may have increased binding affinities to the desired

11. target. In contrast, the compounds discoveredor developed by a lead generation
method (example- pharmacophore based de novo design or pharmacophore
searching ) may be totally new compounds. These compound display the
pharmacophore and thus have a good chance of being bioactive , yet they mey
differ in other parts of the molecule.