Computer aided drug design

1. Computer Aided Drug Design
By- SANJU SAH
St. xavier’s college, Maitighar
Department of Microbiology

2. What is a drug?
• Defined composition with a pharmacological
effect
• Regulated by the Food and Drug
Administration (FDA)
• What is the process of Drug Discovery and
Development?

3. Drugs and the Discovery Process
• Small Molecules
– Natural products
• fermentation broths
• plant extracts
• animal fluids (e.g., snake venoms)
– Synthetic Medicinal Chemicals
• Project medicinal chemistry derived
• Combinatorial chemistry derived
• Biologicals
– Natural products (isolation)
– Recombinant products
– Chimeric or novel recombinant products

4. Discovery vs. Development
• Discovery includes: Concept, mechanism, assay,
screening, hit identification, lead demonstration,lead
optimization
• Discovery also includes In Vivo proof of conceptin
animals and concomitant demonstration of a
therapeutic index
• Development begins when the decision is made to put a
molecule into phase I clinical trials
• The time from conception to approval of a new drug is
typically 10-15 years
• The vast majority of molecules fail along the way
• The estimated cost to bring to market a successfuldrug
is now $800 million!! (Dimasi, 2000)

5. Molecular
Biological
Hypothesis
(Genomics)
Chemical
Hypothesis
Primary Assays
Biochemical
Cellular
Pharmacological
Physiological
Sources of
Molecules
Natural Products
Synthetic
Chemicals
Combichem
Biologicals
Drug Discovery Processes Today
Screening
+
Initial Hit
Compounds
Physiological
Hypothesis

6. Drug Discovery Processes - II
Initial Hit
Compounds
Secondary
Evaluation
- Mechanism
Of Action
- Dose
Response
Hit to Lead
Chemistry
- physical
properties
-in vitro
metabolism
Initial Synthetic
Evaluation
- analytics
- first analogs
First In Vivo
Tests
- PK, efficacy,
toxicity

7. Drug Discovery Processes - III
Development
Candidate
(and Backups)
Lead Optimization
Potency
Selectivity
Physical
Properties
PK
Metabolism
Oral Bioavailability
Synthetic Ease
Scalability
Pharmacology
Multiple In Vivo
Models
Chronic Dosing
Preliminary Tox

8. Drug Discovery Disciplines
• Medicine
• Physiology/pathology
• Pharmacology
• Molecular/cellular biology
• Automation/robotics
• Medicinal, analytical,and combinatorial
chemistry
• Structural and computationalchemistries
• Bioinformatics

9. Drug Discovery Program Rationales
• Unmet Medical Need
• Me Too! - Market - ($$$s)
• Drugs in search of indications
– Side-effects often lead to new indications
• Indications in search of drugs
– Mechanism based, hypothesis driven,
reductionism

10. Serendipity and Drug Discovery
• Often molecules are discovered/synthesized
for one indication and then turn out to be
useful for others
– Tamoxifen (birth control and cancer)
– Viagra (hypertension and erectiledysfunction)
– Salvarsan (Sleeping sickness and syphilis)
– Interferon- (hairy cell leukemia and Hepatitis C)

11. Issues in Drug Discovery
• Hits and Leads - Is it a “Druggable” target?
• Resistance
• Pharmacodynamics
• Delivery - oral and otherwise
• Metabolism
• Solubility, toxicity
• Patentability

12. A Little History of Computer Aided
Drug Design
• 1960’s - Viz - review the target - drug interaction
• 1980’s- Automation - high trhoughput target/drug selection
• 1980’s- Databases (information technology) - combinatorial
libraries
• 1980’s- Fast computers - docking
• 1990’s- Fast computers - genome assembly - genomic based
target selection
• 2000’s- Vast information handling - pharmacogenomics

13. The Structural Genomics Pipeline
(X-ray Crystallography)
Basic Steps
Target
Selection
Crystallomics
•Isolation,
•Expression,
•Purification,
•Crystallization
Data
Collection
Structure
Solution
Structure
Refinement
Functional
Annotation Publish
Bioinformatics
•Distant
homologs
•Domain
recognition
Automation
Bioinformatics
•Empirical
rules
Automation
Better
sources
Software integration
Decision Support
MAD Phasing Automated
fitting
Bioinformatics
•Alignments
•Protein-protein
interactions
•Protein-ligand
interactions
No?
Anticipated Developments • Motif recognition

14. structure info sequence info
NR, PFAM
Building FOLDLIB:
------------------------------------
PDB chains
SCOP domains
PDP domains
CE matches PDB vs.
SCOP
-----------------------------------
90% sequence non-identical
Prediction of : signal peptides
(SignalP, PSORT)
transmembrane (TMHMM,
PSORT) coiled coils (COILS)
low complexity regions (SEG)
Structural assignment of domains
by PSI-BLAST on FOLDLIB-PRF
minimum size 25 aa
coverage (90%, gaps <30,
ends<30)
Create PSI-BLAST
profiles for FOLDLIB vs.
NR
Only sequences w/out A-
prediction
Structural assignment of domains by
123D on FOLDLIB-PRF
Only sequences w/out A-
prediction
Functional assignment by PFAM, NR,
PSIPred assignments
FOLDLIB-PRF
Domain location prediction by sequence
The Genome Annotation Pipeline
Protein sequences
Store assigned
regions in the DB
SCOP,
PDB

15. Combinatorics
Combinatorics is a branch of mathematics concerning the
study of finite or countable discrete structures.
Aspects of combinatorics include counting the structures of
a given kind and size (enumerative combinatorics), deciding
when certain criteria can be met, and constructing and
analyzing objects meeting the criteria (as in combinatorial
designs), finding "largest", "smallest", or "optimal" objects
(extremal combinatorics and combinatorial optimization),
and studying combinatorial structures arising in an
algebraic context, or applying algebraic techniques to
combinatorial problems (algebraic combinatorics).

16. Combinatorial Chemistry
Combinatorial chemistry involves the rapid synthesis or
the computer simulation of a large number of different but
often structurally related molecules or materials.
In a combinatorial synthesis, the number of compounds
made increases exponentially with the number of chemical
steps.
In a binary light-directed synthesis, 2n compounds can be
made in n chemical steps.
Combinatorial chemistry is especially common in CADD
(Computer aided drug design) and can be done online with
web based software, such as Molinspiration.

17. Combinatorial Libraries
•Thousands of variations to a fixed template
•Good libraries span large areas of chemical and
conformational space - molecular diversity
•Diversity in - steric, electrostatic, hydrophobic
interactions...
• Desire to be as broad as “Merck” compounds from
random screening
•Computer aided library design is in its infancy
Blaney and Martin - Curr. Op. In Chem. Biol. (1997) 1:54-59

18. Statement of the Director, NIGMS, before the House Appropriations
Subcommittee on Labor, HHS, Education Thursday, February 25, 1999

19. CADD ?
• Specialized discipline for design of new molecule
or an existing drug with improved activity with
the help of computer generated results.
• Use of various structural features to identify the
nature of the molecule.
• Identification of structure of receptor and its
interaction with the ligand.
• Design of various possible models for calculation
of most suitable conformer.

20. Methods employed
• Different bioinformatics tools.
• Molecular docking
• QSAR

21. What is Docking?
• Docking attempts to find the “best” matching between two molecules

22. … a more serious definition…
• Given two biological molecules determine:
- Whether the two molecules “interact”
- If so, what is the orientation that maximizes the
“interaction” while minimizing the total
“energy” of the complex
• Goal: Tobe able to search a database of
molecular structures and retrieve all molecules
that can interact with the query structure

23. Why is docking important?
• It is of extreme relevance in cellular biology,
where function is accomplished by proteins
interacting with themselves and with other
molecular components
• It is the key to rational drug design: The results of
docking can be used to find inhibitors for specific
target proteins and thus to design new drugs. It is
gaining importance as the number of proteins
whose structure is known increases

24. Why is this difficult?
• Both molecules are flexible and may alter each other’s
structure as they interact:
• Hundreds to thousands of degrees of freedom (DOF)
• Total possible conformations are astronomical

25. Method/Process
1. Identification of structure of the receptor
2. Drawing the 2D structure
3. Conversion of 2D structure to 3D
4. Calculation of energy and energy minimization
5. Conformational search
6. Superimposition of ligands to check flexibility
7. Docking of low energy conformer with receptor

26. 1.Identification of structure of the receptor
• May be identified by various literatures
• Structure may be obtained form PDB
• Study of the nature of the receptor
• Finding the binding site for ligand
Crystal structure of tyrosine kinase(PDB ID1Y6A)

27. 2. Drawing and
Conversion of 2D structure to 3D
• Drawing softwares include
– ACD labs
– Chemdraw
– Chem ultra
– Chem office
– QSAR+
Ball and stick model of a ligand

28. 3. Calculation of energy and energy minimization
• Low energy conformers bind more effectively to
receptor
• Optimization and Calculation of current energy
of molecule
• Minimization of energy
High energy conformer low energy conformer

29. 4. Superimposition of ligands

30. 5. Ligand Docking
• Performed to obtain various possible
interactions and binding modes of inhibitor to
the receptor
• Various contact between amino acid chain of
receptor and ligand
• Possible hydrogen bonds between receptor
and ligand
• Suggests best fit model

31. Docking Programs
 DOCK (I. D. Kuntz, UCSF)
 AutoDOCK (Arthur Olson, The ScrippsResearch
Institute)
 RosettaDOCK (Baker, Washington Univ., Gray, Johns
Hopkins Univ.)
 FlexX
 ArgusLabs
 Schrodinger
 Hex
 GOLD

32. • Quantative Structure-Activity Relationships
What is QSAR?
• A QSAR is a mathematical relationshipbetween
a biological activity of a molecular system and
its geometric and chemicalcharacteristics.
• 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.

33. • Quantitative structure–activity relationship models
(QSAR models) are regression models used in the chemical
and biological sciences and engineering.
• QSAR models relate measurements on a set of "predictor"
variables to the behavior of the response variable.
• In QSAR modeling, the predictors consist of properties of
chemicals; the QSAR response-variable is a the biological
activity of the chemicals.
• QSAR models first summarize a supposed relationship
between chemical structures and biological activity in a data-
set of chemicals.
• Second QSAR models predict the activities of newchemicals.
• Related terms include quantitative structure–property
relationships(QSPR).

34. QSAR and Drug Design
Compounds + biological activity
QSAR
New compounds with improved
biological activity

35. Why QSAR?
The number of compounds required for
synthesis in order to place 10 different groups
in 4 positions of benzene ring is 104
Solution: synthesize a small number of
compounds and from their data derive rules
to predict the biological activity of other
compounds.

36. 3D-QSAR
Structural descriptors are of immense
importance in every QSAR model.
Common structural descriptors are
pharmacophores and molecular fields.
Superimposition of the molecules is necessary.
3D data has to be converted to 1D in order to
use PLS.

37. 3D-QSAR Assumptions
 The effect is produced by modeled compound andnot
it’s metabolites.
 The proposed conformation is the bioactive one.
 The binding site is the same for all modeled
compounds.
 The biological activity is largely explained byenthalpic
processes.
 Entropic terms are similar for all the compounds.
 The system is considered to be at equilibrium, and
kinetics aspects are usually not considered.
 Pharmacokinetics: solvent effects, diffusion,transport
are not included.

38. General Procedure of QSAR
• Select a set of molecules interacting with thesame
receptor with known activities.
• Calculate features (e.g. physicalchemicalproperties,
etc., 2D, 3D)
• Divide the set to two subgroups: one for training and
one for testing.
• Build a model: find the relations between the activities
and properties (regression problem, statistic methods,
machine learning approaches, etc).
• Test the model on the testing dataset.
• Publish a paper if your results are good!
• You can also develop new descriptors,new
methodologies, algorithms, etc.

39. Advantages of QSAR
• Quantifying the relationship between structure
and activity provides an understanding of the
effect of structure on activity.
• It is also possible to make predictions leading to
the synthesis of novel analogues.
• The results can be used to help understand
interactions between functional groups in the
molecules of greatest activity, with those of their
target

40. QSAR and 3D-QSAR Software
Tripos – CoMFA
VolSurf
Catalyst
Serius
QSAR+
Schrodinger
DISCOVER