drug sesign

1. Computer aided
drug design
By Dr Sameh Ahmad M- Abdelghany

2. Basic Drug designing
Section 01

3. Drug Design SLIDE 3
 is the inventive process of finding new
medications based on the knowledge of a
biological target.
 It involves the design of molecules that are
complementary in shape and charge to the
biomolecular target with which they interact and
therefore will bind to it.

4. Life Cylce of Drug Design
SLIDE 4
Synthetic or
Natural
Compounds
Preclinical Trails Clinical Trails
1st step 2nd step Drug
3rd step
 Traditional Life Cycle

5. Modern Drug Design SLIDE 5
Target
 I
d
e
n
st
i
ef
i
c
lea
t
ci
o
tn
ioo
nf
Target
 Verification of
target
 Target Selection
Lead
Identificatio
 Screen Dnevelopment
 High throughput
screening
 Secondary assay
Lead
Optimization
 Lead explosion
 Potency in disease
 Pharmacokinetics
Final
1st step 2nd step Drug
3rd step

6. Drug Designing… SLIDE 6
 Selected/designed molecule
should be:
 Organic small molecule.
 Complementary in shape to the
target.
 Oppositely charge to the
biomolecular target .

7. Drug Designing… SLIDE 7
This molecule will:
 interact with target
 bind to the target
 activates or inhibits the function
of a biomolecule such as a protein

8. Drug Designing… SLIDE 8
 Drug design frequently but not
necessarily relies on computer
modeling techniques.
 This type of modeling is sometimes
referred to as computer-aided drug
design.

9. Mechanism based drug
design SLIDE 9
 When the disease process is understood
at the molecular level and the target
molecule(s) are defined, drugs can be
designed specifically to interact with
the target molecule in such a way as to
disrupt the disease.

10. Computer-aided drug
design(CADD) SLIDE 10
 CADD represents computational
methods and resources that are used to
facilitate the design and discovery of
new therapeutic solutions.

11. Introduction to CADD
SLIDE 11
 Drug design with the help of computers may be used at any of the following
stages of drug discovery:
 hit identification using virtual screening (structure- or ligand-based design)
 hit-to-lead optimization of affinity and selectivity (structure-based
design, QSAR, etc.)
 lead optimization: optimization of other pharmaceutical properties while
maintaining affinity.

12. Objective of CADD
SLIDE 12
 To change from:
 Random screening against disease assays
 Natural products, synthetic chemicals
 To:
 Rational drug design and testing
 Speed-up screening process
 Efficient screening (focused, target directed)
 De novo design (target directed)
 Integration of testing into design process
 Fail drugs fast (remove hopeless ones as early as
possible)

13. Types of drug design
1) Ligand based drug design
SLIDE 13
2)Structure based drug design

14. Ligand-based drug design SLIDE 14
 relies on knowledge of other
molecules that bind to the biological
target of interest.
 used to derive a pharmacophore
model that defines the minimum
necessary structural characteristics a
molecule must possess in order to
bind to the target.

15. Ligand-based drug design
SLIDE 15
 a model of the biological target may be built based on the knowledge of what
binds to it, and this model in turn may be used to design new molecular
entities that interact with the target.
 Alternatively, a quantitative structure-activity relationship (QSAR), in which
a correlation between calculated properties of molecules and their
experimentally determined biological activity, may be derived. These QSAR
relationships in turn may be used to predict the activity of new analogs.

16. Structure-based drug design: SLIDE 16
 relies on knowledge of the three
dimensional structure of the
biological target obtained through :
1. x-ray crystallography
2. NuclearMagnetic Resonance
(NMR) spectroscopy.
NMR
spectroscopy
X-ray
crystallography

17. Structure-based drug design
SLIDE 17
 If an experimental structure of a target is not available,
it may be possible to create a homology model of the
target based on the experimental structure of a related
protein.
 Homology modeling, also known as comparative
modeling of protein, refers to constructing an atomic-
resolution model of the "target" and an experimental
three-dimensional structure of a related homologous
protein (the "template").

18. Structure-based drug design
SLIDE 18
 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
 Intelligence of a medicinal chemist.
 various automated computational procedures may be
used to suggest new drug candidates.

19. Methods SLIDE 19
1) Virtual screening:
 The first method is identification of new ligands for a given receptor by searching large
databases of 3D structures of small molecules to find those fitting the binding pocket of
the receptor using fast approximate docking programs.
2) de novo design of newligands:
 In this method, ligand molecules are built up within the constraints of the binding pocket
by assembling small pieces in a stepwise manner. These pieces can be either individual
atoms or molecular fragments. The key advantage of such a method is that novel structures
can be suggested.
3) optimization of known ligands by evaluating proposed analogs within the binding cavity.

20. Binding site identification
SLIDE 20
 It is the first step in structure based design.
 relies on identification of concave surfaces
on the protein that can accommodate drug
sized molecules that also possess appropriate
"hot spots" (hydrophobic surfaces, hydrogen
bonding sites, etc.) that drive ligand binding.

21. Docking & Scoring
SLIDE 21
 Docking attempts to find the “best”
matching between two molecules
 It includes finding the Right Key for
the Lock
 To place a ligand (small molecule) into
the binding site of a receptor in the
manners appropriate for optimal
interactions with a receptor.
 To evaluate the ligand-receptor
interactions in a way that may
discriminate the experimentally
observed mode from others and
estimate the binding affinity.

22. Components of Docking
SLIDE 22
I- pre- and/or during docking:
Representation of receptor binding site and ligand
II- during docking:
 Sampling of configuration space of the ligand-
receptor complex
III- during docking and scoring:
 Evaluation of ligand-receptor interactions

23. Advantages of CADD
SLIDE 23
 Time
 Cost
 Accuracy
 information about the disease
 screening is reduced
 Database screening
 less manpower is required

24. Success stories of CADD
SLIDE 24
 K+ ion channel blocker
 structural based discovery
 Ca2+ antagonist / T-channel blocker
 chemical descriptor based discovery

25. Success stories of CADD
SLIDE 25
Glyceraldehyde-phosphate DH inhibitors (anti-trypanosomatid drugs)
 combinatorial docking
 Thrombin inhibitor
 docking, de-novo design

26. Computational Tools
For Drug Designing
Section 02

27. 1
2
3
4
5
6
Categories of software
SLIDE 27
Databases & Draw Tools
Molecular Modeling & HomologyModeling
Binding site prediction &Docking
Ligand design Screening -QSAR
Binding free energy estimation
ADME Toxicity

28. Databases
SLIDE 28
 ZincDatabase, Zinc15Database
 ChEMBL
 JChemforExcel
 ProteinDataBank(PDB)
 BindingMOAD(MotherOfAllDatabase)
 PDBbind
 STITCH,SMPDB

29. Databases
SLIDE 29

30. Databases
SLIDE 30

31. Draw Tools SLIDE 31
 ChemDraw
 MarvinSketch
 ACD/ChemSketch
 Marvin molecule editor and viewer
 ChemWriter
 UCSFChimera
 Pymol

32. Medchem SLIDE 32

33. SLIDE 33
UCSF Chimera

34. SLIDE 34
Chem Office

35. Molecular Modeling SLIDE 35
 CHARMM
 GROMACS
 Amber
 SwissParam
 CHARMM-GUI
 CHARMMing.org
 SwissSideChain

36. Hyperchem SLIDE 36

37. Homology Modeling SLIDE 37
 Modeller
 I-TASSER
 LOMETS
 SWISS-MODEL
 SWISS-MODELRepository
 Robetta

38.  LOMETS SLIDE 38

39. Binding site prediction SLIDE 39
 MED-SuMo
 CAVER
 FINDSITE
 sc-PDB
 Pocketome
 PocketAnnotatedatabase
 3DLigandSite,
 metaPocket
 PocketAnnotate

40. CAVER SLIDE 40

41. Docking SLIDE 41
 Autodock
 DOCK
 GOLD
 SwissDock
 DockingServer
 1-ClickDocking
 iGemdock

42. iGemdock SLIDE 42

43. Screening SLIDE 43
 Pharmer
 Catalyst
 PharmaGist
 SwissSimilarity
 Blaster
 AnchorQuery
 ligandscout
 Discovery Studio

44. Discovery Studio SLIDE 44

45. Target prediction SLIDE 45
 MolScore-Antivirals
 MolScore-Antibiotics
 Swiss Target Prediction
 SEA
 ChemProt

46. Ligand design SLIDE 46
 GANDI
 LUDI
 AutoT&T2
 SwissBioisostere
 VAMMPIRE
 sc-PDB-Frag
 e-LEA3D
 eDesign
 iScreen

47. Binding free energy
estimation SLIDE 47
 Hyde, X-score
 NNScore
 DSXONLINE
 BAPPLserver
 BAPPL-Zserver,

48. QSAR SLIDE 48
 cQSAR
 clogP
 ClogP/CMR
 MOLEdb
 ChemDB/Datasets
 OCHEM
 E-Dragon
 PatternMatchCounter
 avogadro

49. Avogadro SLIDE 49

50. ADME Toxicity SLIDE 50
 VolSurf
 GastroPlus
 MedChemStudio
 ALOGPS
 OSIRISPropertyExplorer
 SwissADME
 Metrabase
 PACT-F, TOXNET

51. GastroPlus SLIDE 51

52. That’s all. Thank you very much! 
Any Questions?