This presentation provides an insight about Drug Discovery process using Computer Aided Drug Design (CADD) tools.

1. DRUG DISCOVERY
&
COMPUTER-AIDED
DRUG DESIGN
Prof. (Dr.) Ashwani K. Dhingra
Guru Gobind Singh College of Pharmacy,
Yamuna Nagar, Haryana.
E-mail: ashwani1683@gmail.com
Contact No.: +91-9996230055

2. Contents
What is a “drug”?
Sources of drugs
Drug discovery and development
Approaches to drug discovery
Drug design
Computer-aided drug design
Types of drug design
QSAR
Pharmacophore mapping
Docking
De novo design
Drug design in drug discovery
Drug design: success
In silico ADMET predictions

3. Any particles intended
for use in the
diagnosis, mitigation,
treatment, prevention
of disease in man or
other animals. It can
be any chemical or
biological substance,
synthetic or non-
synthetic.
Drug

4. Sources
of Drugs
Animal
 Insulin (pig, cow)
 Growth hormone (man)
Plant
 Digitalis (Digitalis purpurea)
 Morphine (Papaver somniferum)
Inorganic
 Arsenic
 Mercury
 Lithium
Synthetic
 Chemical (propranolol)
 Biological (penicillin)
 Biotechnology (human insulin)

5. Drug Discovery Roadmap

6. Drug Discovery: Timeline

7. 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

8. Approaches
to Drug
Discovery
Serendipity (luck)
Chemical modification
Random screening
Rational drug design

9. Serendipity
In 1928 Fleming studied
Staph, but contamination of
plates with airborne mold.
Noticed bacteria were lysed in
the area of mold. A mold
product inhibited the growth
of bacteria: the antibiotic
penicillin

10. R-(CH2)n-N Homologs (n=2,3,4,5.....)
(A) Homolog approach: Homologs of a lead prepared
(B) Molecular disconnection /simplification
O
N CH3
O
H
NCH2CH=C C
CH2OCONH2
CH2OCONH2
C
H3
C3H7
MORPHINE PENTAZOCINE MEPROBAMATE
(C) Molecular addition
O
N-CH2-CH=CH2
O
H
NALORPHINE
(D) Isosteric replacements
N
H2 COOH N
H2 SO2NH2
P.A.B.A. SULFANILAMIDE
Chemical Modifications

11. Random
Screening
Testing a random and large
number of different molecules
for biological activity reveals
leads. Innovations have led to
the automation of synthesis
(combinatorial synthesis) and
testing (high-throughput
screening).
Example: Prontosil is derived
from a dye that exhibited
antibacterial properties.

12. Rational Drug Design
Starts with a validated biological target
and ends up with a drug that optimally
interacts with the target and triggers the
desired biological action.
Problem: Histamine triggers release of
gastric acid in stomach. Want a histamine
antagonist to prevent stomach acid release
by histamine => validated biological
target.
Histamine analogs were synthesized
(chemical modification) and screened. N-
guanyl-histamine showed some
antagonist properties = LEAD compound.

13. Drug Design
• Drug design is the inventive
process of finding new drugs
based on the knowledge of a
biological target (protein/
receptor/ enzyme).
• Drug design involves the design
of molecules that are
complementary in shape and
charge to the biomolecular
target (protein/receptor/enzyme)
with which they interact and
therefore will bind to it.

14. Computer-Aided Drug
Design
• Drug design frequently but not necessarily
relies on computer modelling techniques.
• This type of modelling is sometimes
referred to as computer-aided drug
design.
• CADD represents computational methods
and resources that are used to facilitate the
design and discovery of new
therapeutic solutions.

15. Types of Drug
Design
Ligand-based drug design
It relies on knowledge of other molecules
(ligands) that bind to the biological target of
interest.
• QSAR
• Pharmacophore mapping
2) Structure-based drug design
It relies on knowledge of the 3-D structure of the
biological target obtained through X-ray
crystallography, NMR spectroscopy or
homology modelling.
• Docking
• De novo design

16. Quantitative Structure-Activity Relationships
(QSAR)
QSARs are the mathematical relationships linking chemical
structures with a biological activity using physicochemical or any
other derived property.
Mathematical methods used in QSAR include various regression
and pattern recognition techniques.
Physicochemical or any other properties used to generate QSARs
are termed as Descriptors and treated as the independent
variables.
The biological/pharmacological activity is treated as the dependent
variable.
Biological Activity = f (Physico-chemical properties)

17. Typical QSAR Work Flow

18. Pharmacophore
Mapping
The spatial orientation
of various functional
groups or features in
three dimensions
necessary to show
biological activity.

19. Each feature consists of
four parts:
1. Chemical function
2. Location and orientation in
3D space
3. Tolerance in location
4. Weight
Pharmacophore
Features
 HB Acceptor & HB Donor
 Hydrophobic
 Hydrophobic aliphatic
 Hydrophobic aromatic
 Positive charge/Pos. Ionizable
 Negative charge/Neg. Ionizable
 Ring Aromatic

20. Pharmacophore Hypothesis Mapped on
Active Molecule

21. Molecular Docking
• Docking is a method which predicts the preferred orientation of one
ligand when bound in an active site of the target molecule to form a
stable complex.
• Docking is used for finding binding modes of the protein with ligands
or inhibitors. They are able to generate a large number of possible
structures.

22. In molecular docking, an attempt is made to predict the
structure of the intermolecular complex formed between two
or more molecules (e.g., a ligand and a protein, receptor or
enzyme).
Molecular Docking

23. Mechanism of Docking Program

24. Types of Docking Studies
Protein-ligand docking Protein-protein docking
Protein-nucleic acid docking

25. Approaches to Docking

26. Types of Docking Interactions
• 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.
• Electrodynamics forces: The most widely known is the Van der
Waals interactions.
• 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.
• Solvent-related forces: These are forces generated due to chemical
reactions between the solvent and the protein or ligand. Examples are H-
bonds (hydrophilic interactions) and hydrophobic interactions.

27. Docking Process (Workflow)

28. Protein Structure

29. Selection of Protein File

30. Preparation of Protein Files

31. Preparation of Ligand Files

32. Grid Box Parameters

33. Docking Run

34. Docking Run (User Interface)

35. Docking Analysis

36. Common Softwares Used for Docking

37. Applications of Docking

38. De Novo Drug Design
Build compounds that are complementary to a target binding site on
a protein via a “random” combination of small molecular fragments
to make the complete molecule with a better binding profile.

39. Drug Design
in Drug
Discovery
Lead discovery: Identification of a
compound that triggers specific biological
actions.
Lead optimization: Properties of the lead are
tested with biological assays; new molecules
are designed and synthesized to obtain the
desired properties.

41. Name of the drug discovered Biol. Activity
1. Erythromycin analogs Antibacterial
2. New Sulfonamide dervs. Antibacterial
3. Rifampicin dervs. Anti-T.B.
4. Napthoquinones Antimalerials
5. Mitomycins Antileukemia
6. Pyridine –2-methanol’s Spasmolytics
7. Cyclopropalamines MAO inhibitors
8. -Carbolines MAO Inhibitors
9. Phenyl oxazolidines Radioprotectives
10.Hydantoin dervs. Anti CNS-tumors
11.Quinolones Antibacterial
Drug Design Successes
(Fruits of QSAR)

42. Drug Design Successes
N
N
N
H
N
OH
Ph
O
OH
N
H
O
indinavir [Crixivan] (Merck, 1996)
X-ray data from enzyme and molecular mechanics
N
H
Me
HO
O
SPh
OH
O
H
N
H
H
nelfinivir [Viracept] (Agouron, 1996)
ritonavir [Norvir] (Abbott, 1995)
peptidomimetic strategy
saquinavir [Invirase, Fortovase] (Roche, 1990)
transition state mimic of enzyme substrate
N
H
O
Ph
OH
O
H
N
H
H
H
N
CONH 2
O
N
N
S
N
Me
N
H
H
N
N
H
O
O
O
Ph
OH
Ph
O
N
S
HIV-1 protease inhibitors

43. In-Silico ADMET Prediction
Computational methods can predict compound properties important to
ADMET and can be used to predict the drug-likeness of the compounds,
e.g., SwissADME, chemTree, Molinspiration, etc.
• Solubility
• Permeability
• Absorption
• Cytochrome p450 metabolism
• Toxicity
Estimates can be made for millions of compounds, helping reduce
“attrition” – the failure rate of compounds in the late stage.

44. Conclusions
Drug discovery is a multidisciplinary, complex, costly and
intellect-intensive process.
Modern drug design techniques can make the drug discovery
process more fruitful & rational.
Knowledge management and technique-specific expertise can
save time & cost, which is a paramount need of the hour.

45. Our CADD Lab
Molecular Modeling (CADD)
Laboratory at GGSCOP
includes tools for:
 Docking
 QSAR
 Pharmacophore Modeling
 Molecular Dynamics
 Chemo-informatics

46. Prof. (Dr.) Ashwani K. Dhingra
Guru Gobind Singh College of Pharmacy,
Yamuna Nagar, Haryana.
E-mail: ashwani1683@gmail.com
Contact No.: +91-9996230055