this power point is all about rational drug design and pharmacophore based drug design approaches...

1. RATIONAL DRUG
DESIGN
M.SATHYA
M.PHARM I YEAR PHARMACOLOGY
DEPARTMENT OF PHARMACOLOGY
MADRAS MEDICAL COLLEGE

2. Table of contents
 Introduction
 Traditional drug design
 Rational drug design
 Concept of rational drug design
 Types of rational drug design
 Structure based drug design
 Ligand based drug design
 Pharmacophore based drug design approach

3. INTRODUCTION
Drug design is the inventive process of finding new medications based on the
knowledge of the biological target.
In the most basic sense, drug design involves design of small molecules that
are complementary in shape and charge to the bio-molecular target to which
they interact and therefore will bind to it.
Drug design frequently but not necessarily relies on computer modeling
techniques. This type of modeling is often referred to as computer-aided drug
design.

4. Modeling techniques for prediction of binding affinity are reasonably
successful.
However there are many other properties such as bioavailability, metabolic
half-life, lack of side effects, etc. that first must be optimized before a ligand can
become a safe and efficacious drug.
 These other characteristics are often difficult to optimize using rational drug
design techniques.

5. Traditional drug design
Traditional drug discovery involves the origin of drug discovery that evolved in natural
sources, accidental events.
It was not target based and not much systemised as today.
Improved and advancements in pharmaceutical science and technology made it
evolutionised to much more systemize modern drug discovery
 traditional methods of drug discovery (known as forward pharmacology), which rely
on trial-and-error testing of chemical substances on cultured cells or animals, and
matching the apparent effects to treatments.

6. Methods for traditional drug design
Random screening
Trial and error method
Ethnopharmacology approach
Serendipity method
Classical pharmacology
Chemical structure based drug discovery

7. Random screening
It includes random screening of synthetic compounds or chemicals or on
natural products by bioassay procedures. Involves two approaches:
1.Screening for selected class of compounds like alkaloids, flavonoids, etc
2. Screening of randomly selected plants for selected bioassays

9. Contribution of random screening
• Later, the National Cancer Institute (NCI) of National Institute of Health, USA,
studied about 35,000 plant species for anticancer activity, spending over two
decades from 1960 to 1980.
• It resulted in proving two success stories, which were those of paclitaxel and
camptothecin.

10. Trial and error method
Trial and error method includes berries, roots, leaves and barks could be
used for medicinal purposes to alleviate symptoms of illness.
Examples :Willow bark –contains salicin –fever reducing in general
• Cinchona bark – contains quinine – fever associated with malaria
• Chinese herbal remedies – used to treat many illness.

11. Ethnopharmacology approach
•Depends on empirical experiences related to the use of botanical drugs for the discovery of biologically active
New Chemical Entity.
•This process involves the observation, description, and experimental investigation of indigenous drugs.
•It is based on botany, chemistry, biochemistry, pharmacology, and many other disciplines like anthropology,
archaeology, history, and linguistics
•In history several examples are present.
•Andrographis paniculata was used for dysentery in ethnomedicine and the compounds responsible for the
activity were isolated as andrographolide.
•Morphine from Papaver somniferum,
•Berberine from Berberis aristata,
•Picroside from Picrorrhiza kurroa.

12. Contributions of Ethnopharmacology
•Discovery of artemisinin from Artemesia alba for malaria,
•guggul sterones from Commiphora mukul (for
hyperlipidemia), boswellic acids from Boswellia serrata (anti-
inflammatory)
• bacosides from Bacopa monnieri (nootropic and memory
enhancement) was based on the leads from these codified
systems of medicine prevailing in China and India.

13. Serendipity method
•“Serendipity” refers to “an accidental discovery;” i.e, “finding one thing while looking for
something else
•No scientific discovery has ever been made by pure luck.
•All happy accidents in science have one point in common: “each was recognized,
evaluated and acted upon in the light of the discoverer's total intellectual experience.”
•The serendipitous discovery of penicillin in 1928 by Alexander Fleming occurs.
•Fleming was engaged in research on influenza when one of his staphylococcus culture
plates had become contaminated and developed a, mold that created a, bacteria-free
circle
•Fleming recognized the possible significance of the bacteria-free circle and by isolating the
mold in pure culture Serendipity method.

14. • He found that it, produced a substance that has a
powerful destructive effect on many of the common
bacteria that infect man.
• He named the antibacterial substance liberated
into the fluid in which the mold was grown
“penicillin” after Penicillium notatum, the
contaminant of the staphylococcus colony that led
to the discovery.

15. Classical pharmacology
•Also known as function based approach.
•Anciently, drug discovery programmes were often based-successfully-on
measuring a complex response in vivo.
•Such as prevention of experimentally induced seizures, lowering of blood sugar,
or suppression of an inflammatory response.
•Without the prior identification of a drug target.

16. Examples of FUNCTION based drug discovery
 Usually the Natural sorce derived drug comes in
this approach.
 Some of them enlisted in chart.

18. Chemical structure based drug discovery
In 1891: Paul Ehrlich – coined the term chemotherapy, used synthetic chemicals to try and cure
disease.

19. Concept of rational drug design
Rational drug design refers to the development of medications based on the
study of the structures and functions of target molecules. That is to say, the role
of rational drug design is to use a methodological approach to coming up with a
new drug, as opposed to blindly hoping some stroke of luck helps design a new
drug, or instead of randomly testing hundreds of drug molecules in hopes that
one of them binds to a receptor and exerts a therapeutic effect.
Rational drug design , invovles three general steps to create a new drug:
Step 1. Identify a receptor or enzyme that is relevant to a disease they are going
to design a drug for.
Step 2. Elucidate the structure and function of this receptor or enzyme.
Step 3. Use the information from step two in order to design a drug molecule
that interacts with the receptor or enzyme in a therapeutically beneficial way.

20. Basic requirement
Typically a drug target is a key molecule involved in a particular metabolic or signaling pathway
that is specific to a disease condition or pathology, or to the infectivity or survival of a microbial
pathogen.
Some approaches attempt to inhibit the functioning of the pathway in the diseased state by
causing a key molecule to stop functioning. Drugs may be designed that bind to the active region
and inhibit this key molecule. Another approach may be to enhance the normal pathway by
promoting specific molecules in the normal pathways that may have been affected in the
diseased state.
In addition, these drugs should also be designed in such a way as not to affect any other
important "off-target" molecules or anti targets that may be similar in appearance to the target
molecule, since drug interactions with off-target molecules may lead to undesirable side
effect. Sequence homology is often used to identify such risks.

22. Types of drug design
Rational drug design can be broadly divided into two
categories:
STRUCTURE BASED DRUG DESIGN- Relies on finding
new medication based on the knowledge of
the target. Also known as DIRECT DRUG DESIGN.
LIGAND BASED DRUG DESIGN- Relies on knowledge
of other molecules that bind to the biological target of
interest. Also known as INDIRECT DRUG DESIGN.

24. STRUCTURE BASED DRUG DESIGN
Structure based drug design (direct drug design) relies on knowledge of the three
dimensional structure of biological target obtained through methods such as X-
crystallography or NMR Spectroscopy.
If an experimental structure of a target is not available, it may be possible to
create a homology model Of the target based on the experimental structure of a
related protein.
Using the structure of the biological target, candidate drugs that are predicted to
bind with affinity and selectivity to the target may be designed using interactive
graphics and the intuition of a medicinal chemist

25.  Structure based design is one of the first techniques to be used in the
drug design.
 Structure based drug design that has helped in discovery process of new
drugs .
 In parallel , information about the structural dynamics and electronic
properties about ligands are obtained from calculations .
 This has encouraged the rapid development of the structure based drug
design

26. Steps involved in structure based drug design
1. In structure guided drug design, a known 3D structure of a target bound to its
natural ligand or a drug is determined either by X-ray crystallography or by
NMR to identify its binding site.
2. Once the ligand bound 3D structure is known, a virtual screening of large
collections of chemical compounds.
3. screening enables the identification of potential new drugs by performing
docking experiment of this collection of molecules. To enhance binding and
hence to improve binding affinity/specificity, a group of molecules with similar
docking scores is generally used for potency determination; this is High-
Throughput Screening (HTS).

27. 4. After the determination of biological potency, several
properties such as relationships (QSAR, QSPR, between potency and
docking scores) including statistical analysis can be performed to as-
certain the potential molecule(s) for lead drug discovery

28. Structure based drug design

29. Protein structure determination
For structure-based drug design, a priority before investigating receptor–
ligand relationship is to obtain the target structure. There are two major
methods for protein structure determination by physical measures, X-ray
diffraction and NMR.
The solved protein structures can be readily found at Protein Data Bank.
However, for proteins that have not been solved or are difficult to isolate,
modeling approach can be Used such as Homology modeling, folding
recognition, Ab initio protein modeling, hot spot prediction.

30. Homology modeling
Homology modeling also known as comparative modeling of protein, refers to
constructing an automatic-resolution model of the “target” protein from its
amino acid sequence and an experimental three –dimensional structure of a
related homologous protein ( the template).
Homology modeling is a fast method to obtain protein structures that can not
only be used in studying rational drug design but also for protein–protein
interaction and site-directed mutagenesis.
Proteins lacking structural information could be constructed if they have over
30% sequence identify with their related homologous proteins (templates).

31. • The modeled structures can be further modified in model refinement to be
consistent with the experiment data in covalent bonds, geometry, and energy
configuration.
• Force fields, such as CHARMM, AMBER, CVFF, CFF91, and GROMOS can also be
applied to molecules for calculating energy minimization, which uses the
function shown below:
Etotal = Estretching + Ebending + Edihedral +Eout-of-plane+
Ecrossterms + EVdW +Ecoulombic
• To ensure the rationality of the modeled structures, checks on stereochemistry,
energy profile, residue environment, and structure similarity are often needed.
• Stereochemistry considers the bond angles and lengths, the dihedral angles of
major chains, and the non-covalent bonds of amino acid residues within a
protein.

32. Folding recognition
Also known as ‘‘threading,’’ folding recognition was brought up in 1991 by
Bowie and colleagues whom employed this method to describe the
environment of residues interactions.
Folding recognition calculates the probabilities of the 3D structures could
form by given protein sequences. Both the environment of residues
interactions and the protein surface area are considered in the threading
protocol.
Structure with the highest probability is recommended to construct the
protein model.

33. Ab initio protein modeling
The ab initio method is based on physical principles, residue interaction center and
lattice representation of a protein to build the target.
• This method is extremely useful when the other protocols fail to predict an
unknown protein structure.However, the identity and accuracy given by ab
initio modeling could be lower than other approaches.
• Protein folding is not only a physical action, but also involves many biochemical
actions originated from inherent residues interaction
• Based on this concept, ab initio method hypothesizes that when a protein
folds, it would tend to achieve the most energetically favorable state

34. Hot spot prediction
Hot spot prediction in structure-based drug design is to determine the ligand active
site. While the active site may be determined via ligand location in the crystal lattice
after X-ray crystallography.
this method is not possible for proteins that cannot be crystallized. Several binding
site determination methods have been invented to address this issue and FTMAP.
 The primary strategy of FTMAP utilizes small molecular fragments as a probe for
exploring protein surface. Spots where molecular fragments clustered are predicted to
be the favorable druggable sites.
 Significant hydrogen bonds and non-bounded interactions can also be explored
between the probes and protein.

35. High throughtput screening
The pharmaceutical industry has adopted the experimental screening of large libraries of
chemicals against a therapeutically-relevant target (high-throughput screening or HTS) as
a means to identify new lead compounds.
Through HTS, active compounds, antibodies or genes, which modulate a particular
biomolecular pathway, may be identified.
These provide starting points for drug discovery and for understanding the role of a
particular biochemical process in biology.
Although HTS remains the method of choice for drug discovery in the pharma industry,
the various drawbacks of this method, namely the high cost, the time-demanding
character of the process as well as the uncertainty of the mechanism of action of the
active ingredient have led to the increasing employment of rational,structure-based drug
design (SBDD) with the use of computational methods.

36. Virtual screening
SBVS starts with processing the 3D target structural information of interest. The target structure may be
derived from experimental data (X-ray, NMR or neutron scattering spectroscopy), homology modeling, or
from Molecular Dynamics (MD) simulations.
There are numerous fundamental issues that should be examined when considering a biological target for
SBVS; for example, the druggability of the receptor, the choice of binding site, the selection of the most
relevant protein structure, incorporating receptor flexibility, suitable assignment of protonation states, and
consideration of water molecules in a binding site.
 In fact, the identification of ligand binding sites on biological targets is becoming increasingly important..
Another consideration for SBVS includes the careful choice of the compound library to be screened in the
VS exercise according to the target in question, and the preprocessing of libraries in order to assign the
proper stereochemistry, tautomeric, and protonation states.

37. Active site identification
Active site identification is the first step in this program. It analyzes the
protein to find the binding pocket, derives key interaction sites within the
binding pocket, and then prepares the necessary data for Ligand
fragment link.
The basic inputs for this step are the 3D structure of the protein and a
pre-docked ligand in PDB format, as well as their atomic properties.

38. •Both ligand and protein atoms need to be classified and
their atomic properties should be defined, basically, into
four atomic types:
hydrophobic atom: all carbons in hydrocarbon chains or in
aromatic groups.
•H-bond donor: Oxygen and nitrogen atoms bonded to
hydrogen atom(s).
•H-bond acceptor: Oxygen and sp2 or sp hybridized
nitrogen atoms with lone electron pair(s).
•Polar atom: Oxygen and nitrogen atoms that are neither
H-bond donor nor H-bond acceptor, sulfur, phosphorus,
halogen, metal and carbon atoms bonded to hetero-
atom(s).

39.  The space inside the ligand binding region
would be studied with virtual probe atoms of
the four types above so the chemical
environment of all spots in the ligand binding
region can be known.
 Hence we are clear what kind of chemical
fragments can be put into their
corresponding spots in the ligand binding
region of the receptor.

40. Docking
Docking refers to the ability to position a ligand in the active or a
designed site of a protein and calculate the specific binding affinities.
Docking algorithms can be used to find ligands and binding
confirmation at a receptor site close to experimentally determined
structures.
Docking algorithms are also used to identify multiple proteins to which
a small molecule can bind.
Some of the docking programs are GOLD(Genetic optimization for
ligand Docking), AUTODOCK,LUDI,HEX etc.

41. • Docking attempts to find the “best” matching between two
molecules it includes finding the Right key for the lock.
• Given two biological molecules determine: Whether two
molecules “interact” If so, what is the orientation that maximizes
“interaction” while minimizing the total “energy” of the complex.
• GOAL: To be able to search a database of molecular structures
and retrieve all molecules that can interact with the query
structure.

42. • Docking works by generating
a molecular surface of
proteins
• Cavities in the receptor are
used to define spheres
(blue), the centres are
potential locations for ligand
atoms.
• Sphere centres are matched
to ligand atoms , to
determine possible
orientations for the ligand.

43. Scoring Method
1. The basic assumption underlying structure-based drug design is that a
good ligand molecule should bind tightly to its target. Thus, one of the
most important principles for designing or obtaining potential new ligands
is to predict the binding affinity of a certain ligand to its target and use it as
a criterion for selection. A breakthrough work was done by Böhm to
develop a general-purposed empirical function in order to describe the
binding energy.

44. • The concept of the “Master Equation” was raised. The basic idea is
that the overall binding free energy can be decomposed into
independent components which are known to be important for the
binding process.
• Each component reflects a certain kind of free energy alteration
during the binding process between a ligand and its target receptor.
The Master Equation is the linear combination of these components.
• According to Gibbs free energy equation, the relation between
dissociation equilibrium constant, Kd and the components of free
energy alternation was built.
• The sub models of empirical functions differ due to the consideration
of researchers. It has long been a scientific challenge to design the
sub models. Depending on the modification of them, the empirical
scoring function is improved and continuously consummated.

45. Binding free energy
Information on the energy status of the protein–ligand complex, free ligands and
unbound protein must be pre-determined. The energy is calculated using the
formula
Energy of binding = energy of complex energy of ligand
+ energy of receptor.

46. De novo evolution
After docking program, we can modify ligands by
two method
The first method is based on active site features to
identify functional groups that can establish strong
interactions with the receptor. Then, the functional
groups can be linked or attached to the original
ligand scaffolds.
 The second method uses the original ligand
scaffolds to develop derivatives that can
complement the receptor.

47. De Novo Drug Design
De Novo Drug Design De novo is a Latin expression meaning "from the
beginning". Active site of drug targets when characterized from a structural
point of view will shed light on its binding features.
This information of active site composition and the orientation of various amino
acids at the binding site can be used to design ligands specific to that particular
target.
The computer aided ligand design methods and distinguished them as six major
classes:
Fragment location methods: To determine desirable locations of atoms or small
fragments within the active site.

48. • Site point connection methods: To determine locations (“site
points”) and then place fragments within the active site so that
those locations are occupied by suitable atoms.
• Fragment connection methods: Fragments are positioned and
“linkers” or “scaffolds” are used to connect those fragments and
hold them in a desirable orientation.
• Sequential buildup methods: Construct a ligand atom by atom, or
fragment by fragment.
• Whole molecule methods: Compounds are placed into active site
in various conformations, assessing shape and/or electrostatic
complementarity.
• Random connection methods: A special class of techniques
combining some of the features of fragment connection and
sequential buildup methods, along with bond disconnection
strategies and ways to introduce randomness.

50. Ligand based drug design
Ligand-based drug design (or indirect drug design) relies on knowledge of other molecules that bind to the
biological target of interest.
These other molecules may be used to derive a pharmacophore model which defines the minimum
necessary structural characteristics a molecule must possess in order to bind to the target.
In other words, a model of the biological target may be built based on the knowledge of what binds to it
and this model in turn may be used to design new molecular entities that interact with the target.
Alternatively, a quantitative structure-activity relationship (QSAR) in which a correlation between
calculated properties of molecules and their experimentally determined biological activity may be derived.
These QSAR relationships in turn may be used to predict the activity of new analogs.

51. LIGAND BASED DRUG DESIGN
QSAR
SCAFFOLD
HOPPING
PHAEMACOPHORE
APPROACH
PSEUDO
RECEPTORS
2D 3D
CoMFA CoMSIA

52. Quantitative structure–activity relationship
Quantitative structure–activity relationship is a widely used technique in
drug designing process.
It employs statistics and analytical tools to investigate the relationship
between the structures of ligands and their corresponding effects.
 Hence, mathematical models are built based on structural parameters to
describe this structure–activity relationship.

53. 2D-QSAR
2D-QSAR was widely used to link structural property descriptors (such as
hydrophobicity, steric, electrostatic and geometric effects) to molecular
biological activity.
the results were often analyzed with multiple regression analysis. One of the
most commonly used 2DQSAR methods was proposed by Hansch.
2D-QSAR cannot accurately describe the correlation between the 3D spatial
arrangement of the physiochemical properties, and the biological activities,so
3D-QSAR approaches have been adapted.

54. 3D-QSAR
Frequently applied 3D-QSAR methodologies:
Comparative molecular field analysis (CoMFA)
Comparative molecular similarity indices analysis (CoMSIA).

55. CoMFA
Comparative molecular field analysis (CoMFA) is established on the concept that
the biological activity of a molecule is dependent of the surrounding molecular
fields, such as steric and electrostatic fields.
The steric and electrostatic fields were calculated by CoMFA using Lennard–Jones
potential, and coulombic potential, respectively. Although this method has been
widely adopted, it has several problems.
Both potential functions changes dramatically near the van der Waals surface of
the molecule and thus, cut-off values are often required. In addition, alignment of
ligands must be conducted before energy calculation, but the orientation of the
superimposed molecules is correlative to the calculation grid.
It could cause large changes in CoMFA results. Moreover, in order to examine both
fields in the same PLS analysis, a scaling factor needs to be added to the steric field.

56. CoMSIA
Comparative molecular similarity index analysis (CoMSIA) is a method developed
recently as an extension of CoMFA.
The CoMSIA method includes more additional field properties they are steric,
electrostatic, hydrophobic, hydrogen bond donor and hydrogen bond acceptor.
CoMSIA is insensitive to the orientation of the aligned molecules and correlates to the
grid by using Gaussian function.
Furthermore, the improved function algorithm is least influenced by the relative
distance to the van der Waals surface.
Overall, this model can offer a more accurate structural–activity relationship than
CoMFA

57. Scaffold hopping
 Scaffold hopping is to identify isofunctional molecular structure with different
molecular backbones having similar or improved properties.
It is used to discover structurally novel compound by modifying the central core
structure of the molecule.
Their application has led to several molecules with chemically different core
structure, and yet binding to the same receptor. In this process, the bioactivity is
retained or improved.
Scaffold folding approach has been well illustrated by molecules like diazepam,
zolpidem, zaleplon, and zopiclone which exert the same biological response acting as
full agonists of GABA-A (γ-aminobutyric acid) receptor at the benzodiazepine site
though a structural analogy is barely found.

58. An alteration of the central chemical template of a compound is often
desirable for several reasons:
i) A replacement of a lipophilic scaffold by a more polar one for
increased solubility,
ii) A substitution of a metabolically labile scaffold with a more stable or
less toxic one for improving the pharmacokinetic properties,
iii) A replacement of a very flexible scaffold (such as a peptide
backbone) by a rigid central scaffold for significantly improving the
binding affinity and a change in the central scaffold for generating a
novel structure that is patentable.

59. Pseudo receptors
 Pseudoreceptor models combine the advantages of these
two strategies and represent a unifying concept for both
receptor mapping and ligand matching.
They can provide an entry point for structure-based modelling
in drug discovery projects that lack a high-resolution structure
of the target.

60. Pharmacophore based approaches
A pharmacophore is the ensemble of steric and electronic features that is necessary
to ensure the optimal supramolecular interactions with a specific biological target and
to trigger (or block) its biological response.
The pharmacophore should be considered as the largest common denominator of
the molecular interaction features shared by a set of active molecules.
Thus a pharmacophore does not represent a real molecule or a set of chemical
groups, but is an abstract concept.
“A pharmacophore is the pattern of features of a molecule that is responsible for a
biological effect”.

61. • Every type of atom or group in a molecule that exhibits certain
properties related to molecular recognition can be reduced to a
pharmacophore feature.
• These molecular patterns can be labeled as hydrogen bond donors
or acceptors, cationic, anionic, aromatic, or hydrophobic, and any
possible combinations.
• Different molecules can be compared at the pharmacophore level this
usage is often described as “pharmacophore fingerprints.”
• When only a few pharmacophore features are considered in a 3D
model the pharmacophore is sometimes described as a “query.”

62. Pharmacophore finger prints
the pharmacophore representation reduces a molecule to a collection of features at the 2D or
3D level. A pharmacophore fingerprint is an extension of this concept, and typically explain a
molecule as a unique data string.
All possible three-point or four-point sets of pharmacophore features (points) are
enumerated for each ligand. The distance between the feature points is counted in bonds or by
distance-binning when using 3D fingerprints.
Such a fingerprint can be used to analyze the similarity between molecules or among a library
of molecules. Alternatively, a fingerprint model can be used to analyze the common elements
of active ligands to identify the key contributing features to the biological function.

64. Pharmacophore query
A pharmacophore model consists of a
few features organized in a specific 3D
pattern.
Each feature is typically represented as
a sphere (although variants exist) with
a radius determining the tolerance on
the deviation from the exact position.

65. Ligand based pharmacophore modeling
Ligand based pharmacophore modeling is usually carried out by extracting common
chemical features from 3D structures of a set of known ligands representative of essential
interactions between the ligands and a specific macromolecular target.
In general, pharmacophore generation from multiple ligands (usually called training set
compounds) involves two main steps:
1.Creating the conformational space for each ligand in the training set to represent
conformational flexibility of ligands.
2.Aligning the multiple ligands in the training set and determining the essential common
chemical features to construct pharmacophore models.

66. • Handling conformational flexibility of ligands and conducting molecular alignment
represent the key techniques and also the main difficulties in ligand-based pharmacophore
modeling. Currently, various automated pharmacophore generators have been developed,
including commercially available software – such as HipHop, HypoGen DISCO,GASP,
GALAHAD,PHASE.
 Challenges in ligand based pharmacophore modeling:
• The first challenging problem is the modeling of ligand flexibility.
Two strategies have been used to deal with this problem:
pre-enumerating method
on-the-fly method
• pre – enumerating method : In which multiple conformations for each molecule are
precomputed and saved in a database.
• on-the-fly method: In which the conformation analysis is carried out in the
pharmacophore modeling process.

67. The Molecular alignment is the second challenging issue in ligand based pharmacophore
modeling.
The alignment methods can be classified into two categories in terms of their fundamental
nature:
point-based
property-based approaches
• The points (in the point-based method) can be further differentiated as atoms, fragments
or chemical features. In point-based algorithms, pairs of atoms, fragments or chemical
feature points are usually superimposed using a least-squares fitting.
• The property-based algorithms make use of molecular field descriptors, usually
represented by sets of Gaussian functions, to generate alignments. The alignment
optimization is carried out with some variant of similarity measure of the intermolecular
overlap of the Gaussians as the objective function.

68. Structure-based pharmacophore
modeling
Structure-based pharmacophore modeling works directly with the 3D structure of a
macromolecular target or a macromolecule– ligand complex.
The protocol of structure-based pharmacophore modeling involves an analysis of the
complementary chemical features of the active site and their spatial relationships,
and a subsequent pharmacophore model assembly with selected features.
The structure-based pharmacophore modeling methods can be further classified into
two subcategories:
Macromolecule– ligand-complex based.
Macromolecule (without ligand)-based.

69. • The macromolecule–ligand-complex-based approach is
convenient in locating the ligand-binding site of the
macromolecular target and determining the key interaction
points between ligands and macromolecule.
• The structure-based pharmacophore (SBP) method
implemented in Discovery Studio is a typical example of a
macromolecule-based approach. SBP converts LUDI
interaction maps within the protein-binding site into Catalyst
pharmacophoric features: H-bond acceptor, H-bond donor
and hydrophobe.

70. Application of Pharmacophore in ADME-
TOX
Poor ADME-tox is a major contributing factor to failures during drug development and clinical
trial.
Pharmacophore modeling approaches are often used for such ADME-tox predictions.
The pharmacophore models can be used to identify possible interactions of drugs with drug
metabolizing enzymes by matching the equivalent chemical groups of test molecules to those of
drug molecules with a well-known ADME-tox profile.
The enzymes of major importance for observed ADME-tox profile are the cytochrome P450s
(CYP) that initiate drug breakdown. It has been estimated that only six CYP isoenzymes (1A2,
2C9, 2C19, 2D6, 2E1, and 3A4) are responsible for over 90% of drug metabolism.
Based on the observed interactions of known drugs with the CYP enzymes, receptor-based
pharmacophore models have been generated that are able to predict the binding of a drug-like
compound to a certain CYP and assess the possibility of degradation by this enzyme

71. Applications
Pharmacophore modeling is used in de novo design of ligands.
Its also has its role in virtual screening and docking. Compared with
pharmacophore-based VS, pharmacophore-based de novo design shows a
unique advantage in building completely novel hit compounds.
Applications of pharmacophore have also been extended to lead optimization,
multitarget drug design, activity profiling and target identification.

72. REFERENCE
Rational drug design novel methodology and practical application by AbbyL. Parrill,
M.Rami Reddy
European Journal of Pharmacology, “Rational drug design”, Soma Mandal, 625(2009)
90-100.
Drug Discovery Today, “pharmacophore modeling and applications in drug
discovery”,15(2010)11-12.
Current computer-Aided Drug Design, “pharmacophore based drug design approach
as a practical process in drug discovery”, 6(2010) 37-49.
Journal of receptor, ligand and channel research, “pharmacophore modeling:
advances, limitations and current utility in drug discovery” 7(2014) 81-92.