THE CADD IS FOR THE DRUG DEVELOPMENT THE DIFFERENT STRATEGIES ARE MENTIONED LIKE QSAR MOLECULAR DOCKING, THE DIFFERENT DIMNSIONAL FORMS OF QSAR , THE ADVANCE SAR of it.

1. CADD and Molecular Modeling :
Importance in Pharmaceutical
Development

2. TRADITIONAL DRUG DESIGN
Lead generation:
Natural ligand / Screening
Biological Testing
Synthesis of New Compounds
Drug Design Cycle
If promising
Pre-Clinical Studies

3. Structure-based Drug Design (SBDD)
Molecular Biology & Protein Chemistry
3D Structure Determination of Target
and Target-Ligand Complex
Modelling
Structure Analysis
and Compound Design
Biological Testing
Synthesis of New Compounds
If promising
Pre-Clinical
Studies
Drug Design Cycle
Natural ligand / Screening

4.  SBDD:
 drug targets (usually proteins)
 binding of ligands to the target (docking)
↓
“rational” drug design
(benefits = saved time and $$$)

5. Ligand database Target Protein
Molecular docking
Ligand docked into protein’s active site

6. 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)

7. 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 Bio-informatics,
Cheminformatics & Pharmainformatics.

8. 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.

9. 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)

10. Drug Development Process
On average it takes 12 -15
years and costs ~$500 -800
million to bring a drug to
market
develop
assay
lead
optimisation
lead
identification
clinical
trials
to market
10,000’s
compounds
1 drug

11. Cont…

12. 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

13. Automating the CADD Process
Gene sequence data
X-ray or
Homology
Screening
Library synthesis
Med Chem/Combichem
LibmakerTM
Designed libraries
Ligand binding data
Pharmacophore
Model
Skelgen™
Designed Templates

14. Target
Identification
Target
Validation
Lead
Identification
Lead
Optimization
Target discovery Lead discovery
Phases of CADD
SAVING 12 – 15 years, Costs: 500 - 800 million US $
VHTS
Similarity
analysis
Database
filtering
Computer Aided
Drug Design
(CADD)
de novo
design
diversity
selection
Biophores
Alignment
Combinatorial
libraries
ADMET
QSAR

15. How Drugs Work
SubstrateEnzyme
+
Enzyme-substrate
complex
Lock-and-key model

16. 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.

17. 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

18. 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.

19. Donor Hydrophobic core
Charged negative
Acceptor

20. 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.

21. 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

22. 3D QSAR
 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.

23. 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

24. Combinatorial Chemistry
• By combining molecular “building blocks”, we can create
very large numbers of different molecules very quickly.
• Usually involves a “scaffold” molecule, and sets of
compounds which can be reacted with the scaffold to place
different structures on “attachment points”.

25. Example Combinatorial Library
NH
R1
R2
R3
Scaffold “R”-groups
R1 = OH
OCH3
NH2
Cl
COOH
R2 = phenyl
OH
NH2
Br
F
CN
R3 = CF3
NO2
OCH3
OH
phenoxy
Examples
NH
OH
CN
OH
NH
OH
O
CH3
NH
C
OH
OHO
CF3
NH
C
OH
OHO
O
For this small library, the number
of possible compounds is
5 x 6 x 5 = 150

26. Combinatorial Chemistry Issues
• Which R-groups to choose
• Which libraries to make
– “Fill out” existing compound collection?
– Targeted to a particular protein?
– As many compounds as possible?
• Computational profiling of libraries can help
– “Virtual libraries” can be assessed on computer

27. 5. Molecular Modeling
• 3D Visualization of interactions between
compounds and proteins “Docking”
compounds into proteins computationally

28. 3D Visualization
• X-ray crystallography and NMR Spectroscopy
can reveal 3D structure of protein and bound
compounds
• Visualization of these “complexes” of proteins
and potential drugs can help scientists
understand the mechanism of action of the
drug and to improve the design of a drug
• Visualization uses computational “ball and
stick” model of atoms and bonds, as well as
surfaces
• Stereoscopic visualization available

29. “Docking” compounds into
proteins computationally

30. 6. In Vitro & In Silico ADME
models
• Traditionally, animals were used for pre-
human testing. However, animal tests are
expensive, time consuming and ethically
undesirable
• ADME (Absorbtion, Distribution, Metabolism,
Excretion) techniques help model how the
drug will likely act in the body
• These methods can be experemental (in vitro)
using cellular tissue, or in silico, using
computational models

31. In Silico ADME Models
• Computational methods can predict compound
properties important to ADME, e.g.
– LogP, a liphophilicity measure
– Solubility
– Permeability
– Cytochrome p450 metabolism
• Means estimates can be made for millions of
compouds, helping reduce “atrittion” – the
failure rate of compounds in late stage

32. Structure Based Drug Design
Determine Protein Structure
Identify Interaction Sites
De Novo Design 3D Database
Evaluate Structure
Synthesize Candidate
Test Candidate
Lead Compound
Discovery or design of
molecules that interact
with biochemical targets
of known 3D structure

33. 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 of structure
 LUDI, CLIX, CAVEAT, LeapFrog etc.

34. 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

35. X-ray crystallography

36. NMR

37. Molecular docking
 Virtual screening approach to predict receptor-ligand
binding modes
 Scoring method used
 to detect correct bound conformation during
docking process
 to estimate binding affinities of candidate molecule
after completion of docking

38. 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

39. 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

40. Docking
X-ray structure of complex

41. Ligand database Target Protein
Molecular docking
Ligand docked into protein’s active site

42. 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

43. THANK YOU