Drug design

DRUG DESIGN:
A MODERN PERSPECTIVE
S H I K H A D . P O P A L I
H A R S H P A L S I N G H W A H I
D E P A R T M E N T O F P H A R M A C E U T I C A L
C H E M I S T R Y
G U R U N A N A K C O L L E G E O F P H A R M A C Y
N A G P U R
1

Drug Design & Discovery: Introduction
Drugs:
Natural sources Synthetic sources
Targets:

Discovering and Developing the
‘One Drug’

Profile of Today’s Pharmaceutical Business
•Time to market: 10-12 years. By contrast, a chemist develops a
new adhesive in 3 months!
• Why? (Biochemical, animal, human trials; scaleup; approvals
from FDA, EPA, OSHA)

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Over 100
Different
Disciplines
Working Together
PHARMACEUTICAL R&D
A MULTI-DISCIPLINARY TEAM

• Medicinal chemists today are facing a serious challenge because
of the increased cost and enormous amount of time taken to
discover a new drug, and also because of fierce competition
amongst different drug companies

DRUG DISCOVERY & DEVELOPMENT
Identify disease
Isolate protein
involved in
disease (2-5 years)
Find a drug effective
against disease protein
(2-5 years)
Preclinical testing
(1-3 years)
Formulation
Human clinical trials
(2-10 years)
Scale-up
FDA approval
(2-3 years)
Drug Design
- Molecular Modeling
- Virtual Screening

TECHNOLOGY IS IMPACTING THIS PROCESS
Identify disease
Isolate protein
Find drug
Preclinical testing
GENOMICS, PROTEOMICS & BIOPHARM.
HIGH THROUGHPUT SCREENING
MOLECULAR MODELING
VIRTUAL SCREENING
COMBINATORIAL CHEMISTRY
IN VITRO & IN SILICO ADME MODELS
Potentially producing many more targets
and “personalized” targets
Screening up to 100,000 compounds a
day for activity against a target protein
Using a computer to
predict activity
Rapidly producing vast numbers
of compounds
Computer graphics & models help improve activity
Tissue and computer models begin to replace animal testing

CONTENT
• QSAR
• Molecular Docking
• Pharmacophore Modelling
9

• Introduction to QSAR
• 2D and 3D QSAR
• Different models of QSAR
• Different methodologies for developing QSAR
• Advanced QSAR methods (GQSAR, 4D QSAR)
• Advantages and Disadvantages of QSAR.
• Errors encountered in QSAR.
11

QSAR
• The structure of a chemical influences its
properties and biological activity”
• “Similar compounds behave similarly”
• Hansch 1964
12

The physical properties of drugs, in part, dictate their
biological activity.
QSAR is an statistical approach to use these properties in
the development of mathematical models that relate the
physical properties to biological activity, and shows how
those mathematical models may be used to understand
drug action and drug designing.
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.
It establishes an equation of relationship models of
properties and activity.
13
Quantitative Structure Activity Relationship (QSAR)

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)
14

BRIEF HISTORY OF QSAR:
• Galileo Galilei (1564-1642) to Overton and Meyer
(1890’s).
• Hammett Equation of electronic parameter or substituent
constant, s.
• Hansch Analysis for Lead Compound Optimization.
• Combine QSAR and Free and Wilson Model.
• 2D QSAR- HQSAR, craig plot for Drug design.
• 3D QSAR or Comparative Molecular Field Analysis
(CoMFA) and CoMSIA, contour map etc. for
Pharmacophore mapping.
• Computer-assisted drug design (CADD).
16 1
6

QSAR MODEL
The problem of QSAR is to find coefficients C0,C1,...Cn
such that:
Biological activity = C0+(C1*P1)+...+(Cn*Pn)
and the prediction error is minimized for a list of given m
compounds.
8

NEED OF QSAR
• The number of compounds required for
synthesis in order to place 10 different groups in
4 positions of benzene ring is 104
• Synthesize a small number of compounds and
from their data derive rules to predict the
biological activity of other compounds.
18

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).
19

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.
20

21
Data
Selection
Descriptor
Evaluation
Training and
Test set
selection
Variable
selection
Statistical
Evaluation
Model
Evaluation
Model
Interpretation
LEAD IDENTIFICATION
AND
MODIFICATION
Typical Work flow of QSAR Studies
21

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.
• 5D-QSAR explicitly representing different induced-fit
models in 4D-QSAR.
• 6D-QSAR further incorporating different solvation
models in 5D-QSAR.
22

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
23

2D-DISCRIPTORS:
• Physicochemical descriptors.
• Parameters for Hydrophobicity, Electronic properties, and
Steric effects.
• Topological, Wiener index, Constitutional,
• Geometrical, Charge, Information, WHIM, GETAWAY,
(GEometry, Topology, and Atom-Weights AssemblY)
descriptors
• Functional group, Eigen value, Connectivity and Edge
adjacency indices etc.
• pKa (limited to ionizable compounds) , chemical shifts from
NMR
• redox potentials, dipole moments, quantum mechanical
derived properties
• Atomic charges , HOMO and LUMO orbital energies 15

METHODS:
• 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
25
BA k1 2
 k2  k3s  k4ES  k5
BA = S Iij Fij + k
• Hansch analysis and
equation:
• Free and Wilson Model
:

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 under study.
pIC50 = 4.1638+ 0.0324 S_989 + 0.3716 S_141 + 0.2655 E_902
+0.1045 E_709
26

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.
27
O
N
O
N

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 modeling28

pKi= 0.3260+0.0088(±0.0004)R7-Volume + 0.1144(±0.0415)R2-
slogp +0.2357(±0.1118)R6-H-AcceptorCount
29

VALIDATION OF QSAR MODELS
• Statistical quality
– Fitting R2
– Predictability Q2
• Outliers
• Prediction reliability for external set
30

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 outliers.
31

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
32

WHY QSAR FAILS
• False correlations
• May not reflect the complete property space.
• Bottom-line: overfitting
• Inability to interpret QSAR model
33

INTRODUCTION TO RATIONAL DRUG DESIGN
Rational drug design is a process in which finding of new medication
is based on knowledge of biological target.
It involves design of small molecules that are complementary in
shape and charge to bimolecular target.
Drug design frequently but not necessarily relies on computer
modeling techniques . This type of modeling is sometimes referred to
as computer-aided drug design.
The therapeutic antibodies are an increasingly important class of
drugs and computational methods for improving the affinity,
selectivity, and stability of these protein-based therapeutics have also
been developed
36

METHODS OF RATIONAL DRUG DESIGNING :-
1. Structure Based Drug design
2. Ligand based drug design.
3. Fragment Based drug design
37

STRUCTURE BASED DRUG DESIGN
It relies on the knowledge of three dimensional structure of the
biological target obtained through methods such as X-ray
crystallography or NMR spectroscopy.
Using the structure of the biological target, candidate drugs that are
predicted to bind with high affinity and selectivity to the target may
be designed using interactive graphics.
The structure-based drug design may be dived into two categories :-
1. Ligand based drug design
2. Receptor based drug design
38

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.
39

40
Fig :- Mechanism of Docking program

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.
41

Rigid docking
Flexible docking
Induced fit docking
42

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.
43

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.
44

Protein - Ligand Protein - Protein
Protein - Nucleotide
45

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 Van 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.
46

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
47

STAGES OF DOCKING :-
1. Target / Receptor selection and preparation
2. Ligand selection and preparation
3. Docking
4. Evaluating docking results
48

Dr. Florent Barbault, ITODYS (CNRS UMR 7086)
PREPARATION STEPS OF MOLECULAR
DOCKING
3.2. Target structure
3.2.1. Sources
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
●Size limit around 150 residues (for a
protein)
●Average structure
➔ Homology modelling
● Free and quick
●No experimental
●Low precision of sidechains
●Sequence similarity or identity?

DOCKING
Accuracy is an important parameter: RX
3.2.2. Resolution
Here precision, accuracy is very good.

A protein alpha-helix with different resolution
Target structure
3.2.2. Resolution
Preparation steps of molecular docking

DOCKING
In NMR the resolution is hard to determine numerically:
Generally we look at the RMSD or the number of restraints by residue.
3.2.2. Resolution

3. PREPARATION STEPS OF MOLECULAR
DOCKING
3.2.2. Resolution
For homology modelling (comparative modelling) the resolution has no real
meaning.
In all cases, it is essential to have a feeling of the target structure resolution at the
itneracting site location. For enzyme, generally, this area is the best defined.
Beware: for Xray structures some protein parts or atoms may be missed. In this
case, we choose to add or not these parts depending of their location or influence
for the chemical association.
To sum-up, it is always required to gather as much as you can information about
the target.

3. PREPARATION STEPS OF MOLECULAR
DOCKING
3.2.3. 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

DOCKING
Where is the interactingsite 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) to do statistics
 Generally we have “surprising” results
3.3. Interacting site:

TYPICAL DOCKING WORKFLOW
56
TARGET
SELECTION
LIGAND
SELECTION
TARGET
PREPARATION
EVALUATING
DOCKING RESULT
DOCKING
LIGAND
PREPARATION

Common Software's Used for Docking Purpose :-
Sr. No. Docking
Program
Year
Published
Docking Approach
1. DOCK 1988 Shape fitting
(sphere sets)
2. Auto Dock 1990 Genetic
Algorithm, Simulated
Annealing
3. Flex X 2001 Incremental
construction
4. FRED 2003 Shape fitting
5. VLifeMDS Protein-ligand based design
6. FLOG 1994 Rigid body docking program
7. HADDOCK 2003 Protein-Protein docking, Protein-
Ligand docking 57

VLifeMDS
 It has an powerful tools to conduct protein and ligand level studies
through molecular modeling and simulation.
 VLifeMDS is useful in identifying the key residues for protein – ligand
interactions leading in optimization of ligand.
58

DOCK
 DOCK is a fragment based method using shape and chemical
complementary methods for creating possible orientations for the
ligand.
 These orientations can be scored using scoring functions such as
solvation or hydrophobicity.
59

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
60

IMPORTANCE OF DOCKING :-
 Docking is frequently used to predict the binding orientation of
small molecule drug candidates to their protein targets in order to in
turn predict the affinity and activity of the small molecule.
Hence docking plays an important role in the rational design of
drugs.
 The associations between biologically relevant molecules such as
proteins, nucleic acid, carbohydrates and lipids play a central role in
signal transduction. Furthermore, the relative orientation of two
interacting partners may affect the type of signal produced (E.g.-
Agonism Vs Antagonism). Therefore docking is useful in predicting
both strength and type of signal produced. 61

APPLICATIONS OF MOLECULAR DOCKING :-
 It is used in determination of the lowest free energy structures for the
receptor-ligand complex.
 It is also used to calculate the differential binding of a ligand of two
different macro-molecular receptors.
 Study the geometry of a particular complex.
 It can also be used to predict the pollutants that can be degraded by
enzymes.
 De novo design for lead generation.
 To check the specificity of the potential drug against homologous
proteins through docking.
 Docking is widely used as a tool for predicting protein-protein
interaction. 62

Pharmacophore
A pharmacophore that indicates the key features of a series
of 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'
H HBD HBA R

Bioisosteres
Bioisosteres, which are atoms, functional groups or
molecules with similar physical and chemical properties
such that they produce generally similar biological
properties

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

Constrained Systematic Search
Deduce which features are required for activity
Angiotension-converting enzyme (ACE), which is
involved in regulating blood pressure
Four typical ACE inhibitors Captopril
Interacts with an
Arg residue of
enzyme
a zinc-binding group
H bonds to a hydrogen-bond donor in enzyme

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

3.1 Structural (2D) Pharmacophore
Defines minimum skeleton connecting important binding groups

O
NMe
HO
HO
MORPHINE
IMPORTANT GROUPS FOR ANALGESIC ACTIVITY

N
HO
ANALGESIC PHARMACOPHORE FOR OPIATES

MORPHINE
O
NMe
HO
HO
NMe
HO
LEVORPHANOL
NMe
HO
METAZOCINE
CH3
H3C

3D Pharmacophore
Defines relative positions in space of important binding groups
Example
N
HO
HO
N
x

x


O
NMe
HO
HO
MORPHINE
IMPORTANT GROUPS FOR ACTIVITY

O
N
Ar
11.3o
150o
18.5o
7.098 A
2.798 A
4.534 A

Defines relative positions in space of the binding interactions
which are required for activity / binding
3.3 Generalised Bonding Type Pharmacophore
Ar
Ar
x

x

y

Base
HBA
HBD
HBA Base
HBA
HBD
HBA
y

3.4 The Active Conformation
• Need to identify the active conformation in order to identify the 3D
pharmacophore
• Conformational analysis - identifies possible conformations and their
activities
• Conformational analysis is difficult for simple flexible molecules with
large numbers of conformations
• Compare activity of rigid analogues
NH2HO HO NH2 HO
NH2
HO HO HO
I II
rotatable bonds
Dopamine
Locked bonds

3.5 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

3.6 Pharmacophoric Triangles
HO
NH2
HO
Pharmacophore triangles for dopamine
HO
NH2
HO
HO
NH2
HO
Ar
Ar
Basic
HBD/HBA
HBD/HBA

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