Virtual screening uses computer-based methods to identify potential drug candidates by assessing how well compounds interact with biological targets like proteins. It has advantages over laboratory experiments in being lower cost, allowing investigation of compounds that have not been synthesized, and enabling screening of a much larger number of potential compounds. Common virtual screening methods include similarity searching based on molecular fingerprints, pharmacophore searching to identify common chemical features among active molecules, and docking to computationally simulate ligand binding and predict binding affinity.

1. Virtual Screening
Prepared by
MAHENDRA.G.S
1 M pharm
Department of Pharmaceutical chemistry
J S S College of Pharmacy
Mysore

2. CONTENT
• Definition
• Advantages
• Virtual screening methods
• Scoring
• Reference

4. Choosing the right molecule
• Goal: to find a lead compound that can be optimized to give a drug
candidate.
Optimization: using chemical synthesis to modify the lead molecule
in order to improve its chances of being a successful drug.
• The challenge: chemical space is vast. Estimates vary
• There are ~65 million known compounds (example UniChem,
PubChem)
• A typical pharmaceutical compound collection contains ~1-5
million compounds.
• High throughput screening allows large (up to 1 million) numbers
of compounds to be tested
− But very small proportion of “available” compounds
− Large scale screening is expensive
− Not all targets are suitable for HTS

5. Virtual screening : a computational approach to assess
the interaction of an in silico library of small molecules
and the structure of a target macromolecule to rapidly
identify new drug leads.
Virtual screening:
Merits:
• Computational
• Only high scoring ligands
• goes to assay
Demerits:
• Molecular Complexity/
Diversity
• False Positives
• Synthesis Issue

7. Virtual screening:
Advantage: compare to laboratory experiments are
• Low cost.
• Investigate compounds that not been synthesized yet.
• Virtual screening can be used to reduce the initial number of
compounds be for using expensive HTS method.
• The number of passible virtual molecule available for virtual
screening is much higher than there available for HTS

12. What is required for a similarity search ?
• A Database SQL or NoSQL ( Postgres, MySQL,MongoDB) or flat
file of descriptors eg: ChemFP
•Chemical Cartridge to generate fingerprints(descriptors)
for molecules ( RDKit, openbabel)
• Similarity function to calculate similarity( Jaccard, Dice,Tversky)
this can be written in c,c++ or python as a function inside SQL
databases.

13. Pharmacophore searching
IUPAC Definition: “An ensemble of steric and electronic features that is
necessary to ensure the optimal supramolecular interactions with a
specific biological target and to trigger (or block) its biological
response”.
• In drug design, the term 'pharmacophore‘ refers to a set of features that is
common to a series of active molecules.
• Hydrogen-bond donors and acceptors, positively and negatively charged groups,
and hydrophobic regions are typical features.
We will refer to such features as 'pharmacophoric groups‘.

14. 3D- pharmacophores:
• A three-dimensional pharmacophore specifies the spatial relation-ships between the
groups
• Expressed as distance ranges, angles and planes

15. Workflow of pharmacophore modeling

16. Tools to perform pharmacophore searching:
1) Catalyst (Accelrys)
2) Phase (Schrodinger)
3) LigandScout (Inte:Ligand)
4) PharmaGist
5) Pharmer
6) SHAFTS

17. Docking:
Computational simulation of a candidate ligand binding to a receptor.

18. Protein Ligand Docking
Computational method which mimics the binding of a ligand to a protein.
It predicts ..
a) the pose of the molecule in the binding site
b) The binding affinity or score representing the strength of binding

19. Pose and 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 bund
with a known inhibitor.
• Pose ( “binding mode”)
- the geometry of the ligand in the binding site
- Geometry- location , orientation and conformation of the molecule.

20. Protein Ligand Docking
• How does a ligand (small molecule) bind into the active site of a
protein?
• Docking algorithms are based on two key components
− search algorithm
• to generate “poses” (conformation, position and orientation) of
the ligand within the active site
− scoring function
• to identify the most likely pose for an individual ligand
• to assign a priority order to a set of diverse ligands docked to the same
protein – estimate binding affinity.

21. Dock Algorithms
• DOCK: first docking program by Kuntz et al. 1982
− Based on shape complementarity and rigid ligands
• Current algorithms
− Fragment-based methods: FlexX, DOCK (since version 4.0)
− Monte Carlo/Simulated annealing: QXP(Flo), Autodock,
Affinity & LigandFit (Accelrys)
− Genetic algorithms: GOLD, AutoDock (since version 3.0)
− Systematic search: FRED (OpenEye), Glide (Schrödinger)

22. The scoring process evaluates and ranks each ligand pose in the target site
Energetically Favorable
Gibb’s Energy
H-Bond Formation
Other Scores
The GScore is a combination of different parameters.
GScore = 0.065 * van der Waal energy + 0.130 * Coulomb energy +
Lipophilic term + Hydrogen-bonding term + Metal-binding term +
Buried polar groups penalty + Freezing rotatable bonds penalty + Active
site polar interactions.
Scoring & Evaluation
Scoring & Evaluation

23. References:
• Lengauer T, Rarey M (Jun 1996). "Computational methods for biomolecular
docking". Current Opinion in Structural Biology. 6 (3): 402–6
• Kitchen DB, Decornez H, Furr JR, Bajorath J (Nov 2004). "Docking and scoring in virtual
screening for drug discovery: methods and applications". Nature Reviews. Drug
Discovery. 3 (11): 935–49.
• Shoichet, B.K., D.L. Bodian, and I.D. Kuntz, J. Comp. Chem., 1992. 13(3): p. 380-397.
• Meng, E.C., B.K. Shoichet, and I.D. Kuntz, J. Comp. Chem., 1992. 13: p. 505-524.
• Kuntz, I.D., J.M. Blaney, S.J. Oatley, R. Langridge, and T.E. Ferrin, J. Mol. Biol., 1982.
161: p.269-288.
• Meng, E.C., D.A. Gschwend, J.M. Blaney, and I.D. Kuntz, Proteins, 1993. 17(3): p. 266-
278.
• F. Barbault, C. Landon,M. Guenneugues,M. Legrain, et al, Biochemistry 2003. 42 14434-
42.