This document summarizes a presentation on using quantum chemical parameters and graph theoretical indices in a QSAR study of quorum sensing inhibitors. It discusses molecular docking techniques to characterize ligand-protein binding and generate training data of inhibitor compounds. QSAR models were developed using topological and quantum chemical descriptors to correlate inhibitor structure to biological activity. The models accurately predicted activity for test compounds. The techniques showed potential for antimicrobial drug discovery by virtually screening compounds and optimizing hits through molecular dynamics simulations and property predictions.

1. QSAR study of quorum sensing inhibitors using
graph theoretical indices
Presented by
Rajesh Kr. Das
Department of Chemistry
University of North Bengal
Darjeeling, West Bengal
56th Annual Convention of Chemists
&
International Conference on Recent Trends in
Chemical Sciences
November 14 – 16, 2019
The School of Studies in Chemistry, Pt. Ravishankar Shukla University,
Raipur, Chhattisgarh.

2. Drug Discovery
 Molecular Dynamics
 Docking
 QSAR
 ADMET
Theoretical
Approach
Practical
Approach
Synthesis in wet Lab
in situ application

3. Where to Start ?
Ligand
(analog-based)
Target
(structure-based)

4. Goals of Docking
1) Characterize binding site
 make an image of binding site with
interaction points
2) Orient ligand into binding site
 grid search
 descriptor mapping
 energy search
3) Evaluate strength of the interaction
DG bind= DG complex – (DGligand + DGtarget)

5. Each protein has a unique structure!
Amino acid sequence
Folding!
NLKTEWPELVGKSVEEAKK
VILQDKPEAQIIVLPVGTIVT
MEYRIDRVRLFVDKLD

6. Secondary structures, α-helix and β-sheet, have regular hydrogen-
bonding patterns
α-helix
β-sheet

7. Docking of Ligand to the Active site of Protein

8. Protein-ligand docking
• A Structure-Based Drug Design (SBDD) method
“structure” means “using protein structure”
• Computational method that mimics the binding of a ligand to a protein
• Given...
• Predicts the pose of the molecule in the
binding site
• The binding affinity or a score
representing the strength of binding

9. 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. Uses of docking
• The main uses of protein-ligand docking are for
 Virtual screening, to identify potential lead compounds
from a large dataset
 Pose prediction
Pose prediction states that
If we know exactly where and how a
known ligand binds...
 We can see which parts are
important (active moiety) for
binding to avoid changes that
will ‘clash’ with the protein
binding
 We can suggest changes to
improve affinity

11. Chemical structures of 10 training compounds

12. Approach of QSAR
 Topological descriptors: Wiener Index, Balaban
Index , Randic Parameter.
 Quantum chemical descriptors: atomic charges,
molecular orbital energies, dipole moment,
Polarisability etc.
 Hansch’s descriptors: Affinity data, inhibition
constant, pharmacokinetic parameters to study
on antibacterial, anticancer and anti-malarial
drugs
Free-Wilson approach: Overall biological activity.

13. Binding energy of 10 training compounds with IC50 value

14. Chemical structures of 5 test compounds

15. Chemical structures of 5 test compounds
Molecule No. IC50(µM) Binding Energy
(Kcal/mol)
11 20 -5.2
12 50 -6.2
13 100 -5.8
14 101 -5.7
15 120 -4.9

16. Chemical structures of three other important compounds

17. Value of quantum chemical parameters and lnIC50 values of ten
training compounds

18. Value of quantum chemical parameters and lnIC50
values of 5 test compounds

19. Correlation matrix between quantum chemical parameters and
lnIC50 value of 10 training compounds.

20. Regression models generated using quantum
chemical parameters

21. Experimental and predicted lnIC50
value of 10 training compounds by
using Model 5
Experimental and predicted lnIC50
value of 5 test compounds using
Model 5

22. Correlation graph between
experimental and predicted
activity of 10 training compounds
Correlation graph between
experimental and predicted
activity of 5 test compounds
4
4.5
5
5.5
6
6.5
7
7.5
8
4 4.5 5 5.5 6 6.5 7 7.5 8
Predicted
lnIC50
Experimental lnIC50
4
4.5
5
5.5
6
6.5
7
7.5
8
2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8
Experimental lnIC50
Predicted
lnIC50

23. Correlation graph between experimental and
predicted activity of 03 training compounds

24. Advantage of QSAR
Before invention of this method the prediction of the biological activities and physico
chemical properties of any compound were obtained by rational means, to comprehend and
rationalize the mechanism of action within a series of chemicals.
But the same things are obtained using this technique along with
 Substantial savings as regards the cost of product development (e.g., in the area of
pharmaceutical, pesticide, personal product etc.).
 Additional advantage in the reduction of number and intensity of animal model validation.
 Reducing animal use in conformity with the present-day bioethical norm.
 Diminishing the pain and discomfort suffered by animals as experimental subjects.
 predictive and diagnostic process used for finding associations between chemical and
biological activity.

25. Utility of QSAR
 The increasing resistance of harmful microorganisms to
conventional antibiotics has given rise to a demand for new
antimicrobial agents.
 Computational approaches, such as cheminformatics and
bioinformatics are accelerating the process of antimicrobial
drug discovery and design of new potent antimicrobial drugs
through the selection of chemical structure having a rational
basis.

26. Future Prospects
 Chemical structure of a compound correlate well with the
biological activity as well as with their physical, chemical and
technological properties.
 Estimate biological activities and physical or chemical
properties of a series of newly designed compounds before
coming to a final conclusion as to whether or not to
synthesize these compounds.
 QSAR is a promising method for facilitating the drug discovery
process.
 It also be used in such industries as food industry, agro
chemicals, fine chemical industry and material design.

27. Optimized Inhibitors
HOMO
LUMO
DM
ETOTAL
logP
MV
BBB
PPB
TPSA
Natoms
nON
nOHNH
nrotb
CMC
MDDR
WDI
Rule of five
Drug-
likeness
Drug Score
IC50
(µM)
Molecular Docking
Optimized Structure
(Using MD simulation)
∆G binding
(kcal/mol)
QSAR
Equation Derived
Validation of predicted & experimental IC50
Best fit equation
Proposed Theoretical IC50 of leading
compounds (Drugs)
Protein (PDB)
Inhibitors (binding db)
DFT
ADME
PreADMET
mol inspiration
Orisis Property Explorer

28. Ongoing Research Scholars

29. Thank
You