CADD and Molecular
Modeling : Importance in
Pharmaceutical Development
Dr. Sanjeev Kumar Singh
Department of Bioinformatics
Alagappa University
e-mail- skysanjeev@gmail.com
Working at the Intersection
 Structural Biology
 Biochemistry
 Medicinal Chemistry
 Toxicology
 Pharmacology
 Biophysical Chemistry
 Information Technology
Structural Biology
 Fastest growing
area of biology
 Protein and nucleic
acid structure and
function
 How proteins
control living
processes
Medicinal Chemistry
 Organic Chemistry
 Applied to disease
 Example: design
new enzyme
inhibitor drugs
doxorubicin (anti-cancer)
Pharmacology
 Biochemistry of Human Disease
 Different from Pharmacy: distribution
of pharmaceuticals, drug delivery
systems
New Ideas From Nature
 Natural Products
Chemistry
 Chemical Ecology
 During the next two
decades: the major
activity in organismal
biology
 Examples: penicillin,
taxol (anti-cancer)
Bio/Chem-informatics

The collection, representation and organisation of
chemical data to create chemical information, to which
theories can be applied to create chemical knowledge.
Aim

To examine how computational techniques can be used
to assist in the design of novel bioactive compounds.

To give an idea of how computational techniques can
similarly be applied to other emerging areas such as Bioinformatics, Cheminformatics &
Pharmainformatics.
Overview
 Drug discovery process
 How do drugs work?
 Overview of Computer-Aided Drug
Design
Pharmaceutical/Agrochemical
Industry
 Identification of novel compounds with useful and
commercially valuable biological properties.
 vastly complex,
 multi-disciplinary task
 many stages over extended periods of time
 Risk
 most novel compounds do not result in a drug.
 those that do may cause unexpected, long-term
side-effects.
Why CADD…?
 Drug Discovery 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 rigorous
competition
amongst
different
pharmaceutical companies.
Drug Discovery & Development
Identify disease
Isolate protein
involved in
disease (2-5 years)
Find a drug effective
against disease protein
(2-5 years)
Scale-up
Preclinical testing
(1-3 years)
Human clinical trials
(2-10 years)
Formulation
FDA approval
(2-3 years)
Drug Development Process
10,000’s
compounds
develop
assay
lead
identification
lead
optimisation
1 drug
clinical
trials
to market
On average it takes 12 -15
years and costs ~$500 -800
million to bring a drug to
market
Cont…
Technology is impacting this process
GENOMICS, PROTEOMICS & BIOPHARM.
Potentially producing many more targets
and “personalized” targets
HIGH THROUGHPUT SCREENING
Identify disease
Screening up to 100,000 compounds a
day for activity against a target protein
VIRTUAL SCREENING
Using a computer to
predict activity
Isolate protein
COMBINATORIAL CHEMISTRY
Rapidly producing vast numbers
of compounds
Find drug
MOLECULAR MODELING
Computer graphics & models help improve activity
IN VITRO & IN SILICO ADME MODELS
Preclinical testing
Tissue and computer models begin to replace animal testing
Automating the CADD Process
X-ray or
Homology
Med Chem/Combichem
Gene sequence data
LibmakerTM
Designed libraries
Skelgen™
S
N
NH3+
N
H
HN
HN
N
HN
N
HN
N
N
I
O N
4
pA2=7.6
+
N
H H
N
H
N
H
O
HN
N
N
9
pA2=8.4
Cl
O
H
HN
N
10
pA2=7.3
N
H
N
H
O
N
11
Ki=12 nM
N
H
I
H
N+
N
H
O
12
Ki=14 nM
Library synthesis
O
HN
HN
Designed Templates
Cl
7
pA2=8.9
H
N +H
CH3
8
pA2=8.0
N
N
N
Cl
6
pA2=8.1
S
HN
HN
Cl
HN
iodoproxifan (5)
pA2=8.3
N
N
H
clobenpropit (3)
pA2=9.8
N
O
HN
H
N+
N
gt2331 (2)
Ki=0.4 nM
thioperamide (1)
pA2=8.9
HN
H
S
HN
Cl
13
Ki=7 nM
Ligand binding data
N
14
Ki=0.18 nM
Screening
Pharmacophore
Model
Phases of CADD
Target discovery
Target
Identification
Target
Validation
Database
filtering
Similarity
analysis
VHTS
Lead discovery
Lead
Identification
Alignment
Biophores
Lead
Optimization
QSAR
ADMET
diversity Combinatorial de novo
selection
libraries
design
Computer Aided
Drug Design
(CADD)
SAVING 12 – 15 years, Costs: 500 - 800 million US $
How Drugs Work
+
Enzyme
Substrate
Lock-and-key model
Enzyme-substrate
complex
Methodologies and strategies of
CADD:
 Structure based drug design (SBDD)
“DIRECT
DESIGN”
 Followed when the spatial structure of the
target is known.
 Ligand based drug design (LBDD)
“INDIRECT
DESIGN”
 Followed when the structure of the target is
unknown.
Computer-Aided Drug Design
 3-D target structure unknown (LBDD)





Random screening if no actives are known
Similarity searching
Pharmacophore mapping
QSAR (2D & 3D) etc.
Combinatorial library design etc.
 Structure-based drug design (SBDD)
 Molecular Docking
 De novo design
In Pharmacophore…
 Pharmacoporic Studies on ACE
inhibitors
 Pharmacological Studies on HIV-1RT
 Nucleosidic Inhibitors
 Non-Nucleosidic Inhibitors
 Interaction Energy – Potency Correlation
What is Pharmacophore…?
 Pharmacophore model
 Set of points in space defining the binding of ligands
with target.
 Key factors in developing such a model are the
determination of functional groups essential for
binding, their correspondence from one ligand to
another, and the common spatial arrangement of these
groups when bound to the receptor
The pharmacophore model of HIV protease.
Pharmacophore…..?
 “a molecular framework that carries (phoros) the
essential features responsible for a drug’s
(pharmacon) biological activity” Paul Erlich, early
1990
 “a set of structural features in a molecule that is
recognized at a receptor site and is responsible for
that molecule’s activity” Peter Gund, 1977
Basic Features
 A set of features common to a series of
active molecules
 What are the features…?




HBD
HBA
+ve &-ve charged groups and
Hydrophobic regions
 Functional groups or molecules with
similar physical and chemical properties
 Bioisosteres - substituents or groups that
have chemical or physical similarities and
which produce broadly similar biological
properties
Pharmacophore model
 Set of points in space defining the binding of ligands
with target.
 Key factors in developing such a model are the
determination of functional groups essential for
binding, their correspondence from one ligand to
another, and the common spatial arrangement of these
groups when bound to the receptor.
ACE
 Angiotension
converting enzyme
 Converts
angiotensinI to
angiotension II
 Inhibits bradykinin
(vasodilator)
 Vasoconstriction
ACE-inhibitor
 Orally
available
& potent drug
ACE distance map
4 points defined
Five distances
defined
Acceptor
Charged negative
Donor
Hydrophobic core
Pharmacophoric Features of Nucleosidic
HIV-1RT Inhibitors
3'-azido thymidine (AZT)
deoxy nucleoside
triphosphate (dNTP)
3'-nitro nucleoside
2',3' dideoxy nucleoside
2',3'- didehydro dideoxy nucleoside
MESP contours for nucleosidic drugs. Red coloured contours indicate a value of -.01 for
electrostatic potential and yellow contours indicate a value of -0.05
Concluding remarks on Nucleosidic inhibitors
 Different substituents at the 3 position show similar sugar ring puckering
and only slight differences in nucleosidic base disposition and interactions
protein.
 MESP plots have clearly indicated that the charge environment of the
drugs is complementary to the receptor charge environment. Positive
potential areas have been observed in the active site of HIV-1RT where
DNA binding occurs.
 Pharmacophoric Features of Nucleosidic HIV-1RT Inhibitors.
Arpita Yadav* and Sanjeev Kumar Singh Bioorg. & Med. Chem. 11, 2003, 1801.
Threshold interaction energy of NRTI’s (nucleosidic
inhibitors for Reverse transcriptase) to undergo competitive
inhibition
3.58 Å
-14.13 kcal/mol
2'3' dideoxy thymidine
-13.33 kcal/mol
AZT -16.71 kcal/mol
-12
Interaction Energy
(Kcal/mol)
-14
3’-Nitro nucleoside
-21.30 kcal/mol
2'3'-didehydro 2'3'-dideoxy thymidine
-12.39 kcal/mol
-16
-18
-20
-22
0
2
4
6
8
10
12
IC50 (M)
Correlation of interaction energy with potency
Concluding remarks on interaction energy studies
 Correlation graph indicates the requirement of a threshold binding
energy ~12 kcal/mol for the drug to be able to undergo competitive
inhibition efficiently. Less than this binding energy/ interaction energy will
make the drug ineffective or very high concentrations will be required for
inhibition of enzyme. Which may lead to cytotoxicity.
v Threshold interaction energy of NRTI’s (nucleosidic inhibitors for Reverse
transcriptase) to undergo competitive inhibition
Arpita Yadav* and Sanjeev Kumar Singh Bioorg. & Med. Chem. letts. 14, 2004,
2677-2680
Common binding mode for structurally and chemically
diverse non- nucleosidic HIV-1RT inhibitors
2.514 Å
4.0 Å
2.514 Å
2.021 Å
2.785 Å
lys101
Pyrrolyl hetro aryl sulfone with lysine
Pyrrolyl hetro aryl sul
Concluding remarks of Non nucleosidic inhibitors
 Conformational study of non-nucleosidic drugs indicated that each
drug has a ‘V’- shaped conformation.
 Each drug has a -NH group in a position that it can make H- bond with
the carbonyl group of lysine 101 in conformity with earlier studies on
pyrrolyl hetero aryl sulfone. This indicates the importance of lysine 101
in binding NNRTI’s.
 Common binding mode for structurally and chemically diverse non- nucleosidic
HIV-1RT inhibitors"
Arpita Yadav* and Sanjeev Kumar Singh, THEOCHEM, 723, 2005, 205-209.
DISCO: DIStance COmparisons
 Generate some number of low-energy conformations
for each active compound
 The resulting conformations are represented by the
positions of potential pharmacophore points.
 Hydrogen-bond donors and acceptors; charged
atoms; ring centroids; and centres of hydrophobic
regions.
Quantitative Structure-Activity
Relationships (QSAR)



A QSAR relates a numerical description of molecular structure
or properties to known biological activity

Activity = f (molecular descriptors)

Success of QSAR: right descriptors + right method (form of
f)
A QSAR should be

explanatory (for structures with activity data)

predictive (for structures without activity data)
A QSAR can be used to explain or optimise:

localised properties of molecules such as binding
properties

whole molecule properties such as uptake and distribution
3D QSAR
 CoMFA and CoMSIA
 Molecules are described by the values of
molecular fields calculated at points in a 3D
grid
 The molecular fields are usually steric and
electrostatic
 Partial least squares (PLS) analysis used to
correlate the field values with biological
activity
 A common pharmacophore is required.
Using the Model
 The PLS results are
presented as contour
plots
 Steric Bulk:
 Green = Steric
Favourable
 Yellow = Steric
Unfavourable
 Electrostatics:
 Red = Electronegative
Favourable
 Blue = Electronegative
Unfavourable
3D-QSAR CoMFA Study on Aminothiazole
Derivatives as Cyclin Dependent Kinase 2
Inhibitors
• In this work we performed CoMFA study carried out on 47
aminothiazole derivatives as inhibitors of this protein kinase.
• The models could be usefully employed to design selective CDK2
inhibitors and to find novel scaffolds through screening of
chemical databases.
Allignment
CoMFA Steric Contours
CoMFA Electrostatic Contours
• Green contours stand for points where sterically bulkier groups are
anticipated to increase the biological activity.
• The yellow contours are used to underscore the points where bulkier
groups could lower the biological property.
• The electrostatic red plots show where the presence of a negative
charge is expected to enhance the activity.
• The blue contours indicate where introducing or keeping positive
charges are expected to better the observed activity.
• 3D-QSAR CoMFA Study on Aminothiazole Derivatives as Cyclin Dependent Kinase 2
Inhibitors. Nigus Dessalew, Sanjeev Kumar Singh* and P.V. Bharatam QSAR Comb.
Sci., 26(1), 2007, 85-91.
QSAR WORK…

The developed model showed a strong correlative and
predictive capability having a cross validated correlation
co-efficient of 0.747 for CDK4 and 0.755 for CDK2
inhibitions.
•
3D-QSAR CoMFA studies on Indenopyrazole as CDK2 Inhibitors.
Sanjeev Kumar Singh*, Nigus Dessalew, and P. V. Bharatam Eur.
J. of Med. Chem., 41, 2006, 1310-1319.

The conventional and predictive correlation coefficients
were found to be respectively 0.943 and 0.508 for CDK1
and 0.957 and 0.585 for CDK2.
•
3D-QSAR CoMFA Study on Oxindole Derivatives as Cyclin
Dependent
Kinase 1 (CDK1) and Cyclin Dependent Kinase 2
(CDK2) Inhibitors. Sanjeev Kumar Singh*, Nigus Dessalew, and P.
V. Bharatam, Med. Chem. 3(1), 2007, 75-84.
Structure Based Drug Design
Determine Protein Structure
Identify Interaction Sites
Discovery or design of
molecules that interact
with biochemical targets
of known 3D structure
De Novo Design
3D Database
Evaluate Structure
Synthesize Candidate
Test Candidate
Lead Compound
Structure based drug design
 Molecular database mining
 Compounds with best complementarity to
binding site are selected.
 DOCK, Autodock, Flex X etc.
 De novo drug designing
 Virtual
modeling
and
optimization
structure
 LUDI, CLIX, CAVEAT, LeapFrog etc.
of
Structural Targets
 3D structure of target receptors determined
by
 X-ray crystallography
 NMR
 Homology modeling
 Protein Data Bank
 Archive of experimentally determined 3D
structures of biological macromolecules
X-ray crystallography
NMR
Molecular docking
 Virtual screening approach to predict receptorligand binding modes
 Scoring method used
 to detect correct bound conformation during
docking process
 to estimate binding affinities of candidate
molecule after completion of docking
Docking algorithms
 Molecular flexibility
 both ligand and protein rigid
 flexible ligand and rigid protein
 both ligand and protein flexible
 search algorithm
 use to explore optimal positions of the ligand
within the active site
 scoring function
 value should correspond to preferred binding
mode
 efficiency very important for database searching
Scoring function
 Ligand-receptor binding is driven by
 Electrostatics (including h-bonding)
 Dispersion of vdw’s forces
 Hydrophobic interaction
 Desolvation of ligand and receptor
 Molecular mechanics
 Attempt to calculate interaction energy
directly
Docking
X-ray structure of complex
Ligand database
Target Protein
Molecular docking
Ligand docked into protein’s active site
How do my ligands dock into the
protein?
 Various approaches, including:
 Shape (DOCK program)
 incremental search methods (Flex X)
 Monte Carlo/Simulated annealing (AUTODOCK, FLO)
 Genetic algorithms (GOLD)
 Molecular dynamics
 Systematic search (Glide, Open Eye)
 Two key issues
 sampling
 scoring/evaluating possible configurations/poses
Collaboration with…
 Prof. Shandhar Ahamad, National Institute of
Biomedical Innovation, Japan
 Dr. Nigus Desselaw Addis Ababa University,
Ethiopia
 Prof. J. Kastner, University of Stuttgart, Germany
 Prof. K. Dharmalingam, Madurai Kamaraj Uni.,
Madurai
 Dr. Arpita Yadav, CSJM University Kanpur
THANK YOU