This document provides an introduction to the process of drug design given by Subhasis Banerjee. It discusses key areas of drug design including target identification and validation, lead finding and optimization, and ligand-based and structure-based drug design. It also describes the application of cheminformatics in drug design for data mining large databases of small molecules and proteins. The goal of drug design is to progress from initial hits identified through screening to optimized lead compounds through iterative chemical modifications and testing.

1. Subhasis Banerjee (Asst. Professor)
(Gupta College of Technological Sciences, West Bengal.
INTRODUCTION TO DRUG DESIGN
From serendipity to rationality

2. Areas to be touched
Introduction to Drug Discovery Process
Target Identification and Validation
Lead Finding and Optimization
Ligand-Based and Structure Based Drug design
Application of Cheminformatics in Drug Design

3. We don’t know how good a compound
is until we make it. MedChem is a
voyage of discovery.
We can predict enough data to ensure we
make better compounds and succeed
sooner. We know so much already.
Two MedChem Worlds

5. Cheminformatics Bioinformatics
Small molecule
drug Protein
Large databases Large databases
Not all can be drugs Not all can be drug targets
Opportunity for data mining techniques
Data Mining: The practice of examining large pre-existing databases in order to
generate new information

6. Target Identification and Validation

7. ~375 GPCRs375 GPCRs
168 GPCRs bind
to 97 known ligands
207 orphan GPCRs207 orphan GPCRs
86 deorphanized
121 remain to be characterised
Or p ha n GPCRsOr p ha n GPCRs
- Since the end of the 1980s by
molecular cloning and genome
sequencing projects were identified
genes coding unknown GPCRs
- These receptors are termed “orphan”
since their corresponding ligand(s)
remains to be identified
- oGPCR could be new targets
Target identification

9. A drug target can be classified into two classes:
Established drug targets: Deals with good scientific understanding, supported by a
lengthy publication history regarding both how the target functions in normal physiology
and how it is involved in human pathology
Potential drug targets: Functions are not fully understood and which lack drugs
targeting them. Potential targets suggest directions for completely new drug research

10. Target Validation: The target
validation process is aimed at
gathering convincing proofs that
the target under study is a key
player in the development and/or
progression of a disease.

11. Biology of the
target
Expression
Profile
Expression in relevant
areas
Expression in
pathologic state
Expression consistent
between human and
animal model
Functional
role
Role in
human
disease
In vivo studies
In vitro studies
Scientific rationale
definition
Predicted
side effect
profile
Predicted
additional
indications
Target validation flow-chart

12. Lead finding and optimization
A lead compound (i.e. a "leading" compound,
not lead metal) in drug discovery is a chemical
compound that has pharmacological or biological
activity likely to be therapeutically useful, but may still have
suboptimal structure that requires modification to fit better
to the target.
A lead compound (i.e. a "leading" compound,
not lead metal) in drug discovery is a chemical
compound that has pharmacological or biological
activity likely to be therapeutically useful, but may still have
suboptimal structure that requires modification to fit better
to the target.

13. Finding a lead compound
Screening of natural products
The plant kingdom: rich source of lead compounds (e.g. morphine, cocaine, digitalis, quinine,
tubocurarine, nicotine and muscarine, paclitaxel
The microbial world: microorganisms such as bacteria and fungi are rich for lead compounds
(e.g. Antimicrobial Drugs: pencillins, cephalosporines, tetracyclines, aminoglycosides, chloramphenicol, rifamycins)
The marine world: coral, sponges, fish and marine microorganisms have biological potent chemicals, with interesting,
anti-inflammatory, antiviral, and anticancer activity. Eg: Curacin A (anti-tumour, from marine cyanobacterium)
Animal sources: antibiotic peptides were extracted from the skin of African clawed frog.
Epibatidine (potent Analgasic) was also obtained from Ecuadorian frog.
Teprotide (from venom of viper) was the lead compound for the development of antihypertensive agents, Cilazapril &
Captopril

14. Medical folklore
(Berries, leaves and roots used by local healer or shaman as medicines. Many are useless or dangerous
and if they work this may be due to Placebo Effect.
Some of these extracts indeed have a real effect. (e.g. quinine (cinchona), reserpine (Rauwolfia), atropine
(atropa beladona), morphine (opium poppy), digitalis (foxglove), emetine (ipeca), cocaine (coca).
Screening synthetic compound “ libraries”
Me too drugs
Many companies use established drugs from their competitors as a lead compound in order to design a drug.
Modification done in such way that avoids the patent restrictions, retain the activity, and improved the
therapeutic properties.
Eg: Captopril (Anti-hypertension) used as lead compound by different companies to produce their own anti-
hypertension drugs.

15. Lead optimization: A Balancing Act

16. Rule of Five (Lipinski et al)
Poor absorption/permeation and solubility are likely when:
Number of H-bond donors (NH, OH) > 5; Number of H-bond acceptors >
10
MW > 500; clogP > 5
90 % of oral drugs adhere to this rule

17. Refining the chemical structure of a confirmed hit to improve its drug
characteristics
– Synthesis of analog series
– Testing the series to correlate changes in chemical structure to biological and pharmacological
data to establish structure-activity relationships (SAR)
•Potency
•Bioavailability
•Stability
•Selectivity
– Optimization cycle is repeated until the candidate molecule is selected

18. Lead Optimization – Top 10 Tactics
1.Start with a good lead Low MW and logP, potent, selective, novel and functionally active!
2. Look before you leap ‘Why waste 2 hours in the library when you could spend 2 weeks in the lab’
3. Chemistry should allow rapid diversification 39 Multiple sites of variation and chemistry suitable for
parallel follow-up
4. Optimise Lipophilic Interactions LogP/Potency plots & Ligand Efficiency– spot outliers
5. Optimise Polar interactions Look for specific H-bonds and meaningful loss (or gains) in potency
Contd…

19. 6. Hetero-atom Insertion Aryl/heterocycle switch or CH 2/O/N switch
7. Bioisosteres Amide reversal Isoelectronic and/or isosteric replacement
8. Optimise Dipole by F or CF3 su bstitution N/C-F switch
9. Conformational control If you see a ring break it. If you don’t then make it.
Preorganisation can be very beneficial to potency (If you get it right!)
10. Challenge your own hypotheses & invest in alternative templates/series
Get out of the box!

20. Ligand Based and Structure Based Drug design

22. Building Molecules at the Binding Site
Identify the binding regions Evaluate their disposition in space
Search for molecules in the library of ligands for
similarity

23. Structure Based Ligand Design
O
NH
O
H
O
NH
?
O
O
O
H
O
NH
N
SO
O
H
O
NH
O
H
O
NH
S?
?
O
H
O
NH
?
?
?
O
O
H
O
NH
Docking
Building
Linking

24. Homology modeling
Predicting the tertiary structure of an unknown protein using a known 3D
structure of a homologous protein(s) (i.e. same family)
Assumption that structure is more conserved than sequence
Can be used in understanding function, activity, specificity, etc

26. •Alignment
–Multiple possible alignments
•Build model
•Refine loops
–Database methods
–Random conformation
–Score: best using a real force field
•Refine sidechains
–Works best in core residues
Key step in Homology Modeling

27. Structure Prediction by Homology Modeling
Structural Databases
Reference Proteins
Conserved Regions Protein Sequence
Predicted Conserved Regions
Initial Model
Structure Analysis
Refined Model
SeqFold,Profiles-3D, PSI-BLAST, BLAST & FASTA
Cα Matrix Matching
Sequence Alignment
Coordinate Assignment
Loop Searching/generation
WHAT IF, PROCHECK, PROSAII,..
Sidechain Rotamers
and/or MM/MD
MODELER

28. Framework for just the target backbone is shown in
yellow against the template structures
Fragments which have the right conformation to
properly connect the stems without colliding with
anything else in the structure
Generating a framework

29. Molecular Docking
 The process of “docking” a ligand to a binding site mimics the natural course of
interaction of the ligand and its receptor via a lowest energy pathway
 Put a compound in the approximate area where binding occurs and evaluate the following:
 Do the molecules bind to each other?
 If yes, how strong is the binding?
 How does the molecule (or) the protein-ligand complex look like. (understand the intermolecular
interactions)
 Quantify the extent of binding
Contd…

30.  Computationally predict the structures of protein-ligand complexes from their
conformations and orientations.
 The orientation that maximizes the interaction reveals the most accurate structure of
the complex.
 The first approximation is to allow the substrate to do a random walk in the space
around the protein to find the lowest energy.

31. Algorithms used while docking
 Fast shape matching (e.g., DOCK and Eudock)
 Incremental construction (e.g., FlexX, Hammerhead, and SLIDE)
 Tabu search (e.g., PRO_LEADS and SFDock)
 Genetic algorithms (e.g., GOLD, AutoDock, and Gambler)
 Monte Carlo simulations (e.g., MCDock and QXP)

32. Some Available Programs to Perform Docking
 Affinity
 AutoDock
 BioMedCAChe
 CAChe for Medicinal
Chemists
 DOCK
 DockVision
 FlexX
 Glide
 GOLD
 Hammerhead
 PRO_LEADS
 SLIDE
 VRDD

34. Docked structure
HIV protease inhibitors COX2 inhibitors

35. Application of “CHEMINFORMATICS”

36. Chemical information: Storage and retrieval of chemical structures and associated data
to manage the flood of data by the softwares are available for drawing and databases.
All fields of chemistry: Prediction of the physical, chemical, or biological properties of
Compounds, Analytical Chemistry, Chemical(s) of concern, Chemical Specific data,
Structural analogue, Property analogue, Biological or mechanistic analogue, Data bases
Data mining, Analysis of data from analytical chemistry to make predictions on the
quality, origin, and age of the investigated objects, Elucidation of the structure of a compound
based on spectroscopic data.
Contd…..

37. Organic Chemistry: Prediction of the course and products of organic
reactions, design of organic syntheses
Drug Design as well as for bioactive molecules: Identification of new lead
structures, Optimization of lead structures, Establishment of quantitative
structure-activity relationships, Comparison of chemical libraries

38. "The simple act of paying positive attention to people has a
great deal to do with productivity"--Tom Peters
Thank you