3. DRUG DISCOVERY & DEVELOPMENT
Identify disease
Isolate protein
involved in
disease (2-5 years)
Preclinical testing
(1-3 years)
Formulation
Human clinical trials
(2-10 years)
Find a drug effective
against disease protein
(2-5 years)
Scale-up
FDA approval
(2-3 years)
Drug Design
- Molecular Modeling
- Virtual Screening
4. TECHNOLOGY IS IMPACTING THIS PROCESS
Identify disease
Isolate protein
Find drug
Preclinical testing
GENOMICS, PROTEOMICS & BIOPHARM.
Potentially producing many more targets
and “personalized” targets
HIGH THROUGHPUT SCREENING
Screening up to 100,000 compounds a
day for activity against a target protein
VIRTUAL SCREENING
Using a computer to
predict activity
COMBINATORIAL CHEMISTRY
Rapidly producing vast numbers
of compounds
MOLECULAR MODELING
Computer graphics & models help improve activity
IN VITRO & IN SILICO ADME MODELS
Tissue and computer models begin to replace animal testing
5. MODERN DRUG DISCOVERY PROCESS
Target
identification
Target
validation
Lead
identification
Lead
optimization
Preclinical
phase
Drug
discovery
2-5 years
•Drug discovery is an expensive process involving high R & D cost
and extensive clinical testing. A typical development time is estimated
to be 10-15 years.
6-9 years
6. Target
Selection
• Cellular
and
Genetic
Targets
• Genomics
• Proteomics
• Bioinformat
ics
Lead
Discovery
• Synthesis
and
Isolation
• Combinator
ial
Chemistry
• Assay
developme
nt
• High-
Throughput
Screening
Medicinal
Chemistry
• Library
Developme
nt
• SAR
Studies
• In Silico
Screening
• Chemical
Synthesis
In Vitro
Studies
• Drug
Affinity and
Selectivity
• Cell
Disease
Models
• MOA
• Lead
Candidate
Refinement
In Vivo
Studies
• Animal
models of
Disease
States
• Behavioura
l Studies
• Functional
Imaging
• Ex-Vivo
Studies
Clinical
Trials and
Therapeutic
s
7. TARGET SELECTION
• Target selection in drug discovery is defined as the decision
to focus on finding an agent with a particular biological
action that is anticipated to have therapeutic utility
• Target identification: to identify molecular targets that are
involved in disease progression.
to prove that manipulating the
can provide therapeutic benefit for
• Target validation:
molecular target
patients.
8. TARGET SELECTION
Biochemical Classes of Drug Targets
G-protein coupled receptors - 45%
enzymes - 28%
hormones and factors - 11%
ion channels - 5%
nuclear receptors - 2%
12. MOLECULAR MODELING
Model construction
a
QSAR/3D QSAR
Structure-based drug design
Rational drug design
NMR and X-ray
structure determination
Combinatorial chemistry
Chemical similarity
Chemical diversity
Homology modeling
Bioinformatics
Chemoinformatics
QM, MM methods
13. COMPUTATIONAL TOOLS
Quantum Mechanics (QM)
• Considers electronic effect & electronic structure of
the molecule
• Calculates charge distribution orbital energies
• Can simulate bond breaking and formation
• Upper atom limit of about 100-120 atoms
14. Molecular Mechanics (MM)
Totally empirical technique applicable to both small and
macromolecular systems
A molecule is described as a series of charged points
(atoms) linked by springs (bonds)
The potential energy of molecule is described by a
mathematical function called a FORCE FIELD
15. Unknown Known
Unknown
de NOVO design,
HTS, Comb. Chemistry
(Build the lock, then find the key)
Indirect DD Rational Drug Design
Known
Molecular Docking
2D/3D QSAR &
Pharmacophore
BASIC MODELING STRATEGIES
17. Drugs or ligand binds with receptor and
mediate their Pharmacological action
Drug
Receptor
Pharmacological
action
11/29/2019
Stucture-activity relationship 19
18. Docking attempts to find the “best” matching
between two molecules
MOLECULAR D
O
D
C
o
c
K
k
i
I
n
N
g
G
19. MOLECULAR DOCKING
It is a method which predicts the preferred orientation of one
ligand when bound in an active site to form a stable complex.
Docking is used for finding binding modes of protein with
ligands or inhibitors. They are able to generate a large number
of possible structures.
In molecular docking, we attempt to predict the structure of
the intermolecular complex formed between two or more
molecules.
21
20. TYPES OF DOCKING :-
• There are to types of docking that are :-
1. Rigid docking : In rigid docking the molecules are rigid, in 3D
space of one of the molecule which brings it to an optimal fit
with other molecule in terms of scoring function. Also the
internal geometry of both the receptor and ligand are rigid.
2. Flexible docking : In this type of docking the molecules are
flexible, conformations of the receptor and ligand molecules as
they appear in complex.
22
21.
22. TYPES OF DOCKING STUDIES :-
1. Protein-Protein docking : These interactions occur between
two proteins that are similar in size. Conformational
changes are limited by steric constraints and thus are said
to be rigid.
24
23. 2. Protein Receptor - Ligand docking : protein receptor -ligand
docking is used to check the structure, position and
orientation of a protein when it interacts with small
molecules like ligands. Protein receptor-ligand motifs fit
together tightly, and are often referred to as a lock and
key mechanism.
25
24. • Protein - Ligand Protein - Protein
•
•
Protein - Nucleotide
•
•
•
26
25. TYPES OF INTERACTIONS :-
•Interactions between particles can be defined as a consequence of forces between the
molecules contained by the particles. These forces are divided into four categories :-
1. Electrostatic forces - Forces with electrostatic origin due to the charges residing in
the matter. The most common interactions are charge-charge, charge dipole and
dipole-dipole.
2. Electrodynamics forces - The most widely known is the V
an der Waals
interactions.
3. Steric forces - Steric forces are generated when atoms in different molecules come
into very close contact with one another and start affecting the reactivity of each
other. The resulting forces can affect chemical reactions and the free energy of a
system.
4. Solvent-related forces - These are forces generated due to chemical reactions
between the solvent and the protein or ligand. Examples are Hydrogen bonds
(hydrophilic interactions) and hydrophobic interactions.
27
26. FACTORS AFFECTING DOCKING :-
The factors affecting docking are of two different forces that are as follows :-
1. Intra-molecular forces :-
a. Bond length
b. Bond angle
c. Dihedral angle
2. Inter-molecular forces :-
a. Electrostatic
b. Dipolar
c. H-bonding
d. Hydrophobicity
e. Van der Waal’s forces
28
27. STAGES OF DOCKING :-
1.Target / Receptor selection and preparation
2.Ligand selection and preparation
3.Docking
4. Evaluating docking results
29
28. Target structure
A target 3D structure is required!
The PDB (protein databank)
➔ Xray diffraction
● No size limit
● More accurate
● Unique structure (of the crystal)
● Crystallization problems
● Hydrogen are missed
➔ NMR
● Lowest accuracy
● Solution structure
residues (for a
● Size limit around
protein)
➔ Homology modelling
● Free and quick
● No experimental
● Low precision of sidechains
● Sequence similarity or
identity?
30. 2. Target structure treatment
Experimental structures are far from being perfect!
You can find in them:
o Ions
o Water
o Soap
o Glycosyl
o Antibody
o Chaperon proteins
o Missing atoms…
You must clean the pdb file
31. to do
Where is the interacting site on the protein?
Three major methods:
Experimental complex
Safer method
We need an identical mechanism for ligands
Analysis of structural properties
Cavity detection is complex
More an art than a definite method
Molecular docking of the whole protein
Time consuming and boring
Needs a lot of docking poses (~ 1000)
statistics
Generally we have “surprising” results
Interacting site:
34. STEPS INVOLVED IN DOCKING PROGRAM :-
1. Get the complex from protein data bank
2. Clean the complex
3. Add the missing hydrogen / side chain atoms and minimize the complex
4. Clean the minimized complex
5. Separate the minimized complex in macromolecule (lock) and ligand (key)
6. Prepare the docking suitable files for lock and key
7. Prepare all the needing files for docking
8. Run the docking
9. Analyze the docking results
36
35. New compounds with
improved biological activity
QSAR
Correlate chemical structure with activity using statistical approach
Compounds + biological activity
QSAR and Drug Design
36. QSAR?
A QSAR is a mathematical relationship between a
biological activity of a molecular system and its
geometric and chemical characteristics.
QSAR attempts to find consistent relationship
between biological activity and molecular properties,
so that these “rules” can be used to evaluate the
activity of new compounds.
37. The number of compounds required for synthesis
in order to place 10 different groups in 4 positions
of benzene ring is 104
Solution: Synthesize a small number of compounds
and from their data derive rules to predict the
biological activity of other compounds.
Why QSAR ?
39. Molecular Structure ACTIVITIES
Representation Feature Selection & Mapping
Descriptors
Quantitative structure-activity relationships correlate, within congeneric
series of compounds, their chemical or biological activities, either with
certain structural features or with atomic, group or molecular
descriptors.
Quantitative Structure Activity Relationship (QSAR)
Katiritzky, A. R. ; Lovanov, V
.S.; Karelson, M. Chem. Soc. Rev. 1995, 24, 279-287
40. Rationale for QSAR studies
• In drug design, in-vitro potency addresses only part of the
need; a successful drug must also be able to reach its target in
the body while still in its active form.
• The in-vivo activity of a substance is a composite of many
factors, including the intrinsic reactivity of the drug, its
solubility in water, its ability to pass the blood-brain barrier,
its non- reactivity with non-target molecules that it encounters
on its way to the target, and others.
• A quantitative structure-activity relationship (QSAR) correlates
measurable or calculable physical or molecular properties to
some specific biological activity in terms of an equation.
• Once a valid QSAR has been determined, it should be possible
to predict the biological activity of related drug candidates
before they are put through expensive and time-consuming
biological testing. In some cases, only computed values need
to be known to make an assessment.
41. Advantages of QSAR:
• Quantifying the relationship between structure and activity
provides an understanding of the effect of structure on
activity, which may not be straightforward when large
amounts of data are generated.
• There is also the potential to make predictions leading to the
synthesis of novel analogues. Interpolation is readily justified,
but great care must be taken not to use extrapolation outside
the range of the data set.
• The results can be used to help understand interactions
between functional groups in the molecules of greatest
activity, with those of their target
42. DATA FOR QSAR
• All analogs belong to congeneric series.
• All analogs have the same mechanism of action.
• All analogs bind in a similar fashion.
• The effect of isosteric replacement can be predicted.
• Binding affinity is correlated with interaction energy (e.g.,
ionic effects are approx. const.)
• Biological activity is correlated with binding affinity (e.g.,
not with transport properties).
44
43. WHY DO WE NEED DESCRIPTORS?
• Relate structure to activity (QSAR).
• Descriptors act as independent variable.
• Describe different aspects of molecules.
• Compare different molecular structures.
• Compare different conformation of same molecule.
45
45. TYPES OF QSAR
• 1D-QSAR correlating activity with global molecular properties like pKa,
log P, etc.
• 2D-QSAR correlating activity with structural patterns like connectivity
indices, 2D-pharmacophores, without taking into account the 3D-
representation of these properties.
• 3D-QSAR correlating activity with non-covalent interaction fields
surrounding the molecules.
• 4D-QSAR additionally including ensemble of ligand configurations in
3D-QSAR.
models in 4D-
• GQSAR further incorporating different fragments of molecules
• 5D-QSAR explicitly representing different induced-fit
QSAR.
• 6D-QSAR further incorporating different solvation models in 5D-QS47AR.
46. 2D QSAR
• Correlation of physicochemical descriptors with biological
activity.
• Typical QSAR methodology.
• Alignment independent
• Can not predict the interaction potential of molecules
under study.
•Example of 2DQSAR
• pIC50 = 0.0215+ 0.1743(±0.0911) SaasCcount
•
•
•
-0.0084(±0.0002) XAHydrophilicArea
+ 0.0590(±0.0269) SsOHE-index
-0.1742(±0.1000) SaaNE-index
48
47. METHODS:
49
• Quantitative regression techniques
• Qualitative pattern recognition techniques
• Hammet relationships as linear free energy relationship (LFER).
• Statistical parameters: Craig plot
• Simple linear regression
• Multiple Linear Regression(MLR), also termed as Ordinary Least
Squares (OLS)
• PLS- Partial Least Square fitting
• Adaptive Least Squares (ALS)
• PCA- Principal Component Analysis
BA = Iij Fij + k
48. 3D QSAR
• 3D-QSAR refers to the application of force field calculations requiring three-dimensional
structures, e.g. based on protein crystallography or molecule superimposition.
• It examines the steric fields (shape of the molecule), the hydrophobic regions (water-
soluble surfaces), and the electrostatic fields.
• Alignment dependent.
• Can predict the interaction potential of molecules understudy.
• pIC50 = 4.1638+ 0.0324 S_989 + 0.3716 S_141 + 0.2655 E_902 +0.1045 E_709 50
49. DESCRIPTORS FOR 3D QSAR
• Descriptors are calculated as hydrophilic, steric and electrostatic
interaction energies at the lattice points of the grid using a
methyl probe of charge +1.
• This field provides a description of how each molecule will tend
to bind in the active site.
• Field descriptors typically consist of a sum of one or more spatial
properties, such as steric factors or the electrostatic potential.
O
N
O
N
51
50. G QSAR
• GQSAR is a breakthrough patent pending methodology that significantly enhances the
use of QSAR as an approach for new molecule design. As a predictive tool for activity,
this method is significantly superior to conventional 3D and 2D QSAR.
• In this method, every molecule of the data set is considered as a set of fragments, the
fragmentation scheme being either template based or user defined.
• The descriptors are evaluated for each fragment and a relationship between these
fragment descriptors is formed with the activity of the whole molecule.
• Unlike conventional QSAR, with the GQSAR, researchers get critically important site
specific clues within a molecule where a particular descriptor needs to be modified.
• GQSAR approach builds upon the basic focus of QSAR by applying the knowledge
gained in the field over the past four decades in terms of molecular descriptors,
statistical modeling etc.
52
52. VALIDATION OF QSAR MODELS
• Statistical quality
• Fitting R2
• Predictability Q2
• Outliers
• Prediction reliability for external set
54
53. ADVANTAGES OF QSAR:
• Quantifying the relationship between structure and activity provides an
understanding of the effect of structure on activity, which may not be
straightforward when large amounts of data are generated.
• There is also the potential to make predictions leading to the synthesis of
novel analogues. Interpolation is readily justified, but great care must be
taken not to use extrapolation outside the range of the data set.
• The results can be used to help understand interactions between
functional groups in the molecules of greatest activity, with those of their
target. To do this it is important to interpret any derived QSAR in terms of
the fundamental chemistry of the set of analogues, including any ou5t5liers.
54. DISADVANTAGES OF QSAR:
• False correlations may arise through too heavy a reliance being
placed on biological data, which, by its nature, is subject to
considerable experimental error.
• Frequently, experiments upon which QSAR analyses depend, lack
design in the strict sense of experimental design. Therefore the data
collected may not reflect the complete property space. Consequently,
many QSAR results cannot be used to confidently predict the most
likely compounds of best activity.
• Various physicochemical parameters are known to be cross-
correlated. Therefore only variables or their combinations that have
little covariance should be used in a QSAR analysis; similar
considerations apply when correlations are sought for different sets
of biological data
56
55. Molecular descriptors used in QSAR
Type Descriptors
Hydrophobic Parameters Partition coefficient ; log P
Hansch’s substitution constant; π
Hydrophobic fragmental constant; f, f’
Distribution coefficient; log D
Apparent log P
Capacity factor in HPLC; log k’ , log k’W
Solubility parameter; log S
56. Molecular descriptors used in QSAR
Type Descriptors
Electronic Parameters Hammett constant; σ, σ +, σ -
Taft’s inductive (polar) constant; σ*
Swain and Lupton field parameter
Ionization constant; pKa , ΔpKa
Chemical shifts: IR, NMR
57. Molecular descriptors used in QSAR
Type Descriptors
Steric Parameters Taft’s steric parameter; Es
Molar volume; MV
Van der waals radius
Van der waals volume
Molar refractivity; MR
Parachor
Sterimol
58. Molecular descriptors used in QSAR
Type Descriptors
Quantum chemical descriptors Atomic net charge; Qσ, Qπ
Superdelocalizability
Energy of highest occupied molecular orbital;
EHOMO
Energy of lowest unoccupied molecular orbital;
ELUMO
59. Molecular descriptors used in QSAR
Type Descriptors
Spatial Descriptor Jurs descriptors,
Shadow indices,
Radius of Gyration
Principle moment of inertia
60. Classification of Descriptors Based on the
Dimensionality of their Molecular Representation
Molecular
representation
Descriptor Example
0D Atom count, bond
counts, molecular
Molecular weight, average
molecular weight number of:
weight, sum of atoms, hydrogen atoms carbon
atomic properties atoms, hetero-atoms, non-
hydrogen atoms, double bonds,
triple bonds, aromatic bonds,
rotatable bonds, rings, 3-
membered ring, 4- membered
ring, 5-membered ring, 6-
membered ring
61. Classification of Descriptors Based on the
Dimensionality of their Molecular Representation
Molecular
representation
Descriptor Example
1D Fragments
counts
Number of: primary C, secondary C,
tertiary C, quaternary C, secondary
carbon in ring, tertiary carbon in
ring, quaternary carbon in ring,
unsubstituted aromatic carbon,
substituted carbon, number of H-
bond donar atoms, number of H-
bond acceptor atoms, unsaturation
index,
hydrophilic factor, molecular
refractivity.
62. Classification of Descriptors Based on the
Dimensionality of their Molecular Representation
Molecular
representation
Descriptor Example
2D Topological
descriptors
Zagreb index, Wiener index, Balaban
J index, connectivity indices chi (χ),
kappa (К) shape indices
3D Geometrical
descriptors
Radius of gyration, E-state
topological
parameters, 3D Wiener index, 3D
Balaban index
63. Pharmacophore
• A pharmacophore that indicates the key features of a seriesof
active molecules
• In drug design, the term 'pharmacophore‘ refers to a set of
features that is common to a series of active molecules
• Hydrogen-bond donors and acceptors, positively and
negatively charged groups, and hydrophobic regions are
typical features
• We will refer to such features as 'pharmacophoric groups'
64.
65. 3. PHARMACOPHORE
• Defines the important groups involved in binding
• Defines the relative positions of the binding groups
• Need to know Active Conformation
• Important to Drug Design
• Important to Drug Discovery
66. 3D-Pharmacophores
• A three-dimensional pharmacophore specifies the spatial relation-
ships between the groups
• Expressed as distance ranges, angles and planes
• A commonly used 3D pharmacophore for antihistamines contains
two aromatic rings and a tertiary nitrogen
73. 3D Pharmacophore
• Defines relative positions in space of important
binding groups
Example
N
HO
HO
x
x N
74. • Defines relative positions in space of the binding
interactions which are required for activity / binding
Generalised Bonding Type Pharmacophore
Ar
Ar
x
x
y
Base
HBA
HBD
HBA Base
HBA
HBD
HBA
y
75. Pharmacophores from Target Binding Sites
H-bond
donor or
acceptor
aromatic
center
basic or
positive
center
H-bond
donor or
acceptor
aromatic
center
basic or
positive
center
Pharmacophore
O
H
CO2
ASP
SER
PHE
Binding
site
78. INHOUSE DEVELOPED LEADS
80
Factor IXAInhibitor
(Med Chem Res ,2013, 22:976–985)
Calcium Channel Blocker
(J.Kor. Chem. Soc 57,2013)
N
N
S N
H
CH3
O N N
N
N
CN
O
S
H2NHN
O
79. DRUG DESIGN SUCCESSES
While we are still waiting for a drug totally designed from
scratch, many drugs have been developed with major
contributions from computational methods
norfloxacin (1983)
antibiotic
first of the 6-fluoroquinolones
QSAR studies
dorzolamide [Trusopt] (1994)
glaucoma treatment
carbonic anhydrase inhibitor
SBLD and ab initio calcs
donepezil (1996)
Alzheimer's treatment
acetylcholinesterase inhibitor
shape analysis and docking studies
losartan [Cozaar] (1995)
angiotensin II antagonist
anti-hypertensive
Modeling Angiotensin II octapeptide
zolmatriptan [Zomig] 1995
5-HT1D agonist
migraine treatment
Molecular modeling
80. SUMMARY
Drug Discovery is a multidisciplinary, complex,
costly and intellect intensive process.
Modern drug design techniques can make drug
discovery process more fruitful & rational.
Knowledge management and technique specific
expertise can save time & cost, which is a
paramount need of the hour.
