This document summarizes various virtual screening techniques used in drug discovery. It discusses ligand-based methods like similarity searching using 2D and 3D fingerprints, pharmacophore mapping. It also discusses structure-based methods like protein-ligand docking to predict binding poses and scores. Hybrid methods combining different techniques are also used. The document provides an overview of key virtual screening methods and their applications to enrich hit rates and select compounds for further testing from large libraries in an efficient manner during the drug discovery process.

1. Virtual screening techniques
Presented by- Presented to-
Rohit Pal Dr. Bhupinder Kumar
M.Pharm (Pharmacuetcal Chemistry, 2nd Semester)
Department of Pharmacuetical Chemistry
ISF College of Pharmacy, Moga (Punjab)

2. Table of content
• Introduction
• Virtual screening methods
• Ligand based
• Similarity Searching
• Pharmacophore Mapping
• Machinelearning methods
• Structurebased
• Protein LigandDocking
• MeritsandDemerits
• Hybridvirtualscreening

3. Drug discovery

4. Introduction
• Virtual screening (VS) is a computational technique
used in drug discovery to search libraries of small
molecules in order to identify those structures which are
most likely to bind to a drug target typically
a protein receptor or enzyme.
• Virtual screening has been defined as the "automatically
evaluating very large libraries of compounds" using
computer programs.

5. • As this definition suggests, VS has largely been a numbers game
focusing on how the enormous chemical space of over 1060 conceivable
compounds can be filtered to a manageable number that can be
synthesized, purchased, and tested.
• Although searching the entire chemical universe may be a theoretically
interesting problem, more practical VS scenarios focus on designing and
optimizing targeted combinatorial libraries and enriching libraries of
available compounds from in-house compound repositories or vendor
offerings.
• As the accuracy of the method has increased, virtual screening has
become an integral part of the drug discovery process. Virtual Screening
can be used to select in house database compounds for screening, choose
compounds that can be purchased externally, and to choose which
compound should be synthesized next.

6. r o h I t
F
I
L
T
E
R
Filters—
Shape
Conformers
Any rule like Lipinski’s rule, Veber rule,Ghose rule

7. Techniques of virtual screening

9. Methods of virtual screening
• Ligand-based
• Structure-based
• Hybrid methods

10. Ligand Based Virtual Screening
• In LBVS process, the most effective biologically active lead
molecule is detected using structural or topological similarity
or pharmacophoric similarity search.
• Taking into consideration several criteria such as structure as
well as shape of individual fragment or electrostatic
properties of the molecule carries out the similarity
comparisons.
• The leads generated are ranked based on their similarity
score, obtained using different methods or algorithms.

11. Classification of ligand based virtual screening

12. Similarity Searching
What is it ??
Chemical, pharmacological or biological properties of two compounds match.
The more the common features, the higher the similarity between two molecules.
Chemical
The two structures on top are chemically similar to each other. This is reflected in their common sub-graph, or scaffold: they
share 14 atoms
Pharmacophore
The two structures above are less similar chemically (topologically) yet have the same
pharmacological activity, namely they both are Angiotensin-Converting Enzyme (ACE) inhibitors

13. Similarity scale
80-99%
100%
60-79%
30-59%
Below 30%
Known inhhbitor
Downloaded library of ligands

14. 2D fingerprints: molecules represented as binary vectors
• Each bit in the bit string (binary vector) represents one molecular fragment. Typical length is ~1000
bits.
• The bit string for a molecule records the presence (“1”) or absence (“0”) of each fragment in the
molecule.
• Originally developed for speeding up substructure search for a query substructure to be present in a
database molecule. Each bit set to “1” in the query must also be set to “1” in the database structure.
• Similarity is based on determining the number of bits that are common to two structures.

15. 3D based similarity
• Shape-based
-ROCS (Rapid Overlay of Chemical
Structures).
- Silicos-it.com (Shape it).
•Computationally more expensive than 2D methods.
•Requires consideration of conformational flexibility
Rigid search - based on a single conformer
−Flexible search.

16. Pharmacophore Mapping
• Pharmacophore is an abstract description of molecular features which are necessary for molecular
recognition of a ligand by a biological macromolecules.
• Pharmacophore mapping is the definition and placement of pharmacophoric features and the
alignment techniques used to overlay 3-D.
• It consist of three steps:-
1. Identifying common binding element that are responsible for biological activity.
2. Generating potential conformations that active compound may adopt.
3. Determining the 3-D relationship between pharmacophore element in each conformation
generated.
Pharmacophore Mapping Software:
• Window and Linux based protein modelling software.
• Programs that perform pharmacophore based searches are 3D search UNITY, MACCS-3D and
ROCS.

17. Generating pharmacophore
models: Ligand-based
Rimonabant
Trying to predict how the ligands will bind to the
receptor
without knowing the structure of thereceptor

18. 3-D Pharmacophores
• A three-dimensional pharmacophore specifies the spatial relation-ships between
the groups
• Expressed as distance ranges, angles and planes

19. Workflow of pharmacophore modeling

20. Machine learning methods
• SAR Modeling:
 Use knowledge of known active and known inactive compounds to build a
predictive model
 Quantitative-Structure Activity Relationships (QSARs)
 Long established (Hansch analysis, Free-Wilson analysis)
 Generally restricted to small, homogeneous datasets e.g. lead optimization.
 Structure-Activity Relationships (SARs)
 “Activity” data is usually treated qualitatively
 Can be used with data consisting of diverse structural classes and multiple binding modes
 Some resistance to noisy data (HTS data)
 Resulting models used to prioritize compounds for lead finding (not to identify
candidates or drugs)

21. Structural based virtual screening
• Structural based virtual screening begins with the identification of a potential
ligand-binding site on the target molecule.
• Ideally the target site is a pocket or protuberance having a variety of probable
hydrogen bond donors and acceptors, hydrophobic characteristics, and with
molecular adherence surfaces.
• The ligand-binding site can be the active site as in an enzyme; an assembly site
with another macromolecule or a communication site, which is necessary in the
mechanism of the molecule.
• Determining the structure of a target protein by NMR, X-ray crystallography or
homology modelling befalls as a major and initializing stair in structure based
virtual screening.
• Numerous X-ray crystallographic and NMR studies are helpful in determining the
Virtual Screening perimental structures of ligands bound to the enzymes, serve as a
major source of ideas for analogue design, intern useful for the docking studies.

22. Methods of structure based virtual screening

23. Protein LigandDocking
• Computational method which mimics the binding of a ligand to a protein.
• It predicts -
a) The pose(the geometry of the ligand in the binding site of the molecule in the binding site
b) The binding affinity or score representing the strength of binding.

24. The searchspace
• The difficulty with protein–ligand docking is in part due to the fact that it involves many degrees
of freedom
 The translation and rotation of one molecule relative to another involves six degrees of
freedom
These are in addition the conformational degrees of freedom of both the ligand and the protein
 The solvent may also play a significant role in determining the protein–ligand geometry (often
ignored though)
• The search algorithm generates poses, orientations of particular conformations of the
molecule in the binding site
Tries to cover the search space, if not exhaustively, then as extensively as possible
There is a trade off between time and search space coverage

25. DockAlgorithms
• 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)

26. Merits:
• Computational.
• Only high scoring ligands.
• Reducing real laboratory experiments and accelerates drug discovery.
Demerits:
• Molecular complexity/diversity.
• False positives.
• Synthesis issue.

27. Hybrid VirtualScreening
Mostly, people in pharmaceutical industry does not follow a specific route they follow a hybrid of methods as discussed in previous
slide.
Shape
Similarity
Structure based
Pharmacophore
Docking based
Screening
Post Process
Starting
database
Potential Lead compounds
Filter : Rule of 5 ,
ADME, TOX
ROCS, FlexS
Pharmacophore
based Screening
Ligand Scout, Phase, Ligand fit
Prepared
database
Dock, Gold, Glide, ICM
Cscore, MM/PBSA, Solvation Corrections
LUDI, Ligand Scout,
Phase, DrugScore
Cleaning Molecules
Remove isotopes, salts and
mixtures
Protonation and
normalization
Remove duplicates and
invalid structures
Filtering Molecules
User defined or other
filter
Remove problematic
moieties using PAINS,
Frequent Hitters etc.
PhyChem property
descriptor calculation
and filtration
Apply protonation at
pH 7.4

28. THANK UOU