Structure based drug design involves identifying lead compounds through virtual screening and then optimizing these leads through medicinal chemistry. Key steps include target selection, virtual screening to identify initial hits, lead optimization through structure-activity relationship studies and molecular docking to refine binding, and progression through preclinical and clinical testing. Several successful drugs have been developed using this approach, starting from virtual screening hits that were subsequently optimized into drug candidates.

1. Structure Based Drug Design and
Drug Discovery
Mr. Prashant Joshi

2. “ The new paradigm, now emerging is that all
the 'genes' will be known (in the sense of
being resident in databases available
electronically), and that the starting "point of a
biological investigation will be theoretical.”
Sir Walter Gilbert

3. Drug Discovery Process
Science 2004, 303, 1813-8
Target
selection
Proof of
efficacy
Clinical trials &
Therapeutics
 Genomics
 Proteomics
 Bioinformatics
 Target validation
 Assay developments.
 Lead identification
 HTS and vHTS
 In-silico studies
Lead
Discovery
Medicinal
Chemistry
 Lead optimization
 SAR generation
 X-ray Crystallography
 Formulation development
 In-vitro studies
 In-vivo studies
 PK/PD refinement
 Biomarker identification
 Safety studies
 Pharmacokinetics
 Clinical efficacy
 Marketing

4. What is structure based Drug Design ?
Science 2004, 303, 1813-8

5. Structure based drug design involved two steps:
 Lead identification: Identification of initial chemical core structure,
which have desired biological effect in moderate potency can be
optimized to superior drug candidate.
 Lead optimization: Chemical modification in lead structure by
medicinal chemist in order to improve the biological activity or the
physiochemical properties.
 e.g. taxol and optimized drug is paclitaxel.
 Similarly, nalidaxic acid is lead candidate and optimized drug is
ciprofloxacin.
 Similarly, Morphine is lead candidate and sufentanyl is the superior
anti-nociceptive drug.

6. What is Docking?
Docking attempts to find the “best” matching between two
molecules. i.e. host and guest molecule. Where host is protein and
guest is both ligand and protein.

7. The interaction of a drug with its
receptor is a complex process. Many
factors are involved in the
intermolecular association such as
hydrophobic; Van der Waal’s, hydrogen
bonding and electrostatic forces.
Ligand docking to receptor
Ligand database Target Protein
Molecular docking
Ligand docked into protein
•The process of “DOCKING” can be defined as a fitting of a ligand to
binding sites which tries to mimic the natural course of interaction of
the ligand and its receptor via a lowest energy pathway. Usually the
receptor is kept rigid while the conformation of the drug molecule is
allowed to change. The molecules are physically moved closer to none
another and the preferred docked conformation is minimized

8. Protein-ligand docking
• It Predicts...
• The pose of the molecule in the
binding site
• The binding affinity or a score
representing the strength of
binding

9. Pose vs. binding site
• Binding site (or “active site”)
• the part of the protein where the ligand binds
• generally a cavity on the protein surface
• can be identified by looking at the crystal
structure of the protein bound with a known
inhibitor
• Pose (or “binding mode”)
• The geometry of the ligand in the binding site
• Geometry = location, orientation and
conformation

10. Steps in ligand-protein docking
• Protein preparation:
• Ligand Preparation:
• Definition of binding site and docking methodology:
• Evaluation of binding poses:
• Scoring of poses:

11. 1. Preparation of protein structure
• PDB structures often contain water molecules: In general, all water
molecules are removed except where it is known that they play an important
role in coordinating to the ligand
• PDB structures are missing all hydrogen atoms: So incorporates
H atom.
• An incorrect assignment of protonation states
in the active site will give poor results
• Glutamate, Aspartate have COO- or COOH
where OH is hydrogen bond donor, O- is not
• Histidine is a base and its neutral form has
two tautomers
H
N
H N
R
+
N
H N
R
R
NH
N

12. Preparation of protein structure
• Crystallography gives electron density, not molecular structure
• In poorly resolved crystal structures of proteins, isoelectronic groups can
give make it difficult to deduce the correct structure
• Affects asparagine, glutamine, histidine and affects hydrogen
bonding pattern.
R
O
NH2
R
NH
2
O
R
N
N
N
N
R

13. 2. Ligand Preparation
• A reasonable 3D structure is required as starting point
• During docking, the bond lengths and angles in ligands are held fixed;
only the torsion angles are changed
• The protonation state and tautomeric form of a particular ligand
could influence its hydrogen bonding ability: Either protonate as
expected for physiological pH and use a single tautomer Or generate and dock
all possible protonation states and tautomers, and retain the one with the
highest score OH O
H+
Enol Ketone

14. Docking algorithms: Rigid Docking
• We can classify the various search algorithms for docking based on the type of
bonding such as (covalent docking and non-covalent docking). Further non-covalent
docking could be broadly categorized based on the degrees of freedom of chemical
bonds that they consider in ligand structures and protein.
• Rigid docking: The ligand as well as protein is treated as a rigid structure during the
docking. Only the translational and rotational degrees of freedom are considered.
• To deal with the problem of ligand conformations, a large number of conformations
of each ligand are generated in advance and each is docked separately e.g. FRED
(Fast Rigid Exhaustive Docking), and DOCK
3. Definition of binding site and docking methodology:

15. The DOCK algorithm – Rigid ligand docking
AR Leach, VJ Gillet, An Introduction to Cheminformatics

16. Flexible ligand docking:
• Flexible docking: Conformations of each molecule are generated on-the-fly by the search
algorithm during the docking process and The algorithm can avoid considering
conformations that do not fit. Sometimes they also uses Stochastic search methods:
Stochastic means that they incorporate a incremental degree of randomness to whole
molecule. E.g. Genetic algorithms (GOLD), and Simulated annealing (AutoDock).
• Incremental construction methods:
• These construct conformations of the ligand within the binding site in a series of stages
• First one or more “base fragments” are identified which are docked into the binding site
• The orientations of the base fragment then act as anchors for a systematic
conformational analysis of the remainder of the ligand e.g. : FlexX and GLIDE.

17. Incremental construction methods

18. Incremental construction methods
Base fragment Chain lengthening Building molecule

19. Handling protein conformations and flexibility
• Most docking software treats the protein as rigid: Rigid Receptor Approximation.
This approximation may be invalid for a particular protein-ligand complex as...
• the protein may deform slightly to accommodate different ligands (ligand-induced fit)
• protein side chains in the active site may adopt different conformations.
• Some docking programs allow protein side-
chain flexibility: For example, selected side
chains are allowed to undergo torsional rotation
around acyclic bonds.
• Larger protein movements can only be handled
by separate dockings to different protein
conformations e.g. Ensemble docking (e.g.
GOLD 5.0)

20. 4. Evaluation of binding poses
• Typically, protein-ligand docking software consist of two main
components which work together:
1. Search algorithm: Generates a large number of poses of a molecule in the
binding site
2. Scoring function: Calculates a score or binding affinity
for a particular pose
To give:
• The pose of the molecule in the
binding site
• The binding affinity or a score of pose
representing the strength of binding

21. An Effective Binding Free Energy Function in GLIDE
vdW desol elec const
vdW
desol
elec
const
ΔG=ΔE +ΔG +ΔE +ΔG
ΔE :
ΔG :
ΔE :
ΔG :
Van der Waals energy; i.e. Shape complementarity
Desolvation energy; i.e. Hydrophobicity
Electrostatic interaction energy and
Translational, rotational and vibrational free energy changes
desol
ΔG = N ΔG
N :
ΔG :
i i
i
i
i

Number of atoms of type i
Desolvation energy for an atom of type i
5. scoring of binding poses

22. The perfect scoring function will…
• Accurately calculate the binding affinity
– Will allow actives to be identified in a virtual screen
– Be able to rank actives in terms of affinity
• Score the poses of an active higher than poses of an inactive
– Will rank actives higher than inactive in a virtual screen
• Score the correct pose of the active higher than an incorrect pose of
the active
– Will allow the correct pose of the active to be identified
Where “actives” = molecules with biological activity

23. The Drug discovery schema today and Virtual
screening
 Virtual Screening: Virtual screening is a collection of
tools to search libraries of small molecules in order to
identify those structures which are most likely to match to
a query (being a protein or a small molecule ligand).

24. Challenges in drug discovery (Lead identification)

25. Virtual screening work flow

26. Achievements of virtual screening in drug discovery
1 (IC50 = 27 μM)
Hit identified in virtual screening
from Merck Sample collection
L-700, 462 (IC50 = 0.011 μM)
Known as Aggraset lunched in 1998
Structure based
Lead optimization
1. AGGRASET
2. PRX00023
PRX-93009 (Initial virtual hit)
Ki (5HT1A = 1 nM)
Ki (α1 = 6 nM)
Ki (α2 = 12 nM)
PRX-00023 (Optimized lead) Phase IIb
Ki (5HT1A = 5 nM)
Ki (α1 = > 10 μM)
Ki (α2 = >10 μ M)
Structure based
Lead optimization
5HT1A receptor agonist used in major depression patients
Fibrinogen receptor antagonist used as platelet aggregation inhibitor in myocardial infraction
Expert. Opin. Drug Discov. 2008, 3, 841-851. WO2006/081332

27. Achievements of virtual screening in drug discovery
PRX-93046 (Ki = 21 nM)
PRX-03140 (Ki = 1 nM)
Phase IIb alone and in combination with Aricept
Structure not disclosed
3. PRX-03140
4. PRX-08066
PRX-08066 (optimized lead) Phase IIa
Ki (5HT2B = 3.4 nM)
Structure based
Lead optimization
5HT2B antagonist useful in pulmonary arterial hypertension associated with COPD
Partial agonist of 5HT4 useful in alzheimer disease
Structure based
Lead optimization
(Ki = 570 nM)
EPIX pharmaceuticals virtual hit
EPIX pharmaceuticals virtual hit
Expert. Opin. Drug Discov. 2008, 3, 841-851. WO2006/081332

28. Achievements of virtual screening in drug discovery
Expert. Opin. Drug Discov. 2008, 3, 841-851. WO2006/081332
Cyclic aliphatic bicarboxylic
acid derivative in which one
carboxylic acid is amide
bound to biphenyl group
SC12267 (IC50 = 134 nM)
Phase II studies
5. Vidofludimas
6. Mozenavir
DMP-450 or mozenavir (Ki = 0.28 nM)
Reached upto Phase IIb trial
Structure based
Lead optimization
Hit from ligand based virtual screening
Ki = > 4 μM concentration
EPIX pharmaceuticals virtual hit
Initial Lead (IC50 = 410 nM)
Structure based
Lead optimization
Dihydroorotate dehydrogenase (DHODH) inhibitor for immunomodulation in rhemutoid arthritis
Protease inhibitor for AIDS failed due to bioavaibility issues

29. Summary of presentation
• The last 40 years exhaustive research led to the development of Structure based
drug design ass powerful tool currently employed in drug discovery.
• Virtual screening is an combination of methods used to improve the enrichment
of active lead candidate from large databases.
• Flexible docking methods are mostly used due to superior enrichment results
over rigid docking.
• Docking scoring function prioritizes the set of compound based on the plausible
binding affinity. However, still the selection of compound for biological
validation is still a require a human command.
•

30. Thank you

31. Combined ligand and structure based approaches for identification
and optimization of new CYP1B1 inhibitor leads
CYP1B1 is considered as one of the most appropriate molecular mechanism for
metabolism of cisplatin in cancer cells and therefore causes chemoresistance to cisplatin.
 Recently Walsh et al elucidated the crystal structure of CYP1B1 with potent inhibitor α-
napthoflavone. Which could be utilized to search novel CYP1B1 inhibitor scaffolds.
O
O
Alpha-Napthoflavone
CYP1B1 IC50= 4 nM
Mol. Cell Biol. 1990, 10, 5098.
Cancer Res. 1993, 53, 2279. J. Biol Chem. 2013, 288, 12932-12941.
Literature reports suggest that no such protocol is available for identification of new chemotypes
for CYP1 family inhibition.

32. α-Napthoflavone
 GLIDE Docking score = -11.07
 MMGB/SA binding affinity ΔG = -82.82
Therefore 50,000 in-house molecules were subjected to the vHTS by molecular
docking and ΔG of binding calculation by prime MMGB/SA calculation using
depicted strategy.
Molecular docking and binding affinity calculations
HTVS docking
SP docking
XP docking
MMGB/SA
Top 30 %
Top 30 %
Top 30 %
Docking by Glide
MMGB/SA by Prime
CYP1B1 ANF-CYP1B1 complex

33. Site Score
R4 -1.83
R3 -1.52
R5 -1.43
R6 -1.26
ANF-CYP1B1
complex
ANF E-pharmacophore
E-Pharmacophore design and screening
 Ligand based screening is one of the powerful tool to identify the new chemotypes
matching the query ligand.
 From structure based docking, those sites which are crucial for target CYP1B1 binding
were recruited based on site score and incorporated to the E-pharmacophore
Therefore 50,000 in-house molecules were subjected to E-pharmacophore screening using PHASE.

34. 50,000 in house compounds
Best 1000
Compounds
Knowledge based selection of
300 candidates for screening
CYP1B1 structure
guided virtual
Screening
E-Pharmacophore
screening using
ANF O
O
ANF
CYP1A1 IC50= 60 nM
CYP1A2 IC50= 6 nM
CYP1B1 IC50= 4 nM
CYP1B1 Structure
Experimental Validation
of proposed hits
Combined Ligand and structure based virtual screening
HTVS docking
SP docking
XP docking
MMGB/SA
Top 30 %
Top 30 %
Top 30 %

35. Efficacy of identified hits for CYP inhibition in human cell assay
Code CYP enzyme inhibition IC50in live human cells (µM)*
1A1 1A2 1B1 2D6 2C9 2C19 3A4
5121780 (119) 0.269 0.03 0.0565 >10 >10 >10 >10
5653386 (126) >10 20.1 13.55 >10 >10 >10 >10
D727-0773 (229) 1.45 >10 1.681 >10 >10 >10 >10
D727-0720 (266) 3.75 10.5 13.02 >10 >10 >10 >10
K227-0140 (198) 0.667 9.5 1.123 >10 >10 >10 >10
ANF 0.383 0.078 0.052 >10 >10 >10 >10
5121780 (119) 5653386 (126)
D727-0773 (229)
D727-0773 (229)
D727-0773 (229)

36. 10 ns MD
Simulation of
5121780 (119)
with CYP1
enzymes
IC50 calculation of 5 compounds in
SacchrosomesTM and live human cells
Commercially purchased 50,000
compounds library
CYP1A1 Structure Based and ANF e-
Pharmacophore screening
CYP1A1 inhibition of 300 ligands in
EROD assay using sacchrosomesTM
300 hits
Virtual screening
5 compounds
N
N
5121780 (119)
Live human cells
CYP 1A1 IC50: 269 nM
CYP 1B1 IC50: 56 nM
CYP1A2 IC50: 30 nM
CYP2D6, 2C9, 2C19, 3A4 IC50: >10 M Potent
CYP1 inhibitor
Summary of research work