VLifeMDS 4.1 - Molecular Design Suite

All trademarks, methodologies, product names mentioned in this
presentation are sole property of their respective owners.

Non – confidential © 2011, VLife Sciences Technologies Pvt. Ltd. All rights reserved www.vlifesciences.com

VLife and your research requirements

VLife can help you with it’s
Are you computational
innovative computational
chemist/biologist?
platform VLifeMDS

VLife can help you with it’s
Are you an
strong decision support system
experimental setup?
for efficient results

Already doing VLife can help you build
computation with one consensus with an Unbiased
tool? perspective

VLife can do a time base service
Looking for leads or
for identifying, screening and
optimization?
optimizing leads

Looking for new target VLife can do a time base service
or multi-target? on VLife RVHTS platform


Multiple scenarios single
NewEdge platform: Application summary
platform
Approaches Applications

Protein structure
Activity I analysis
data

NewEdge: End-to-end capabilities
Yes Active site analysis

Primary Homology modeling
No II
Yes lead activity
chemistry data Pharmacophore
No III identification

Target Conformer generation
structure Yes IV
Primary Combinatorial library
Close lead
homolog chemistry
No V Property visualization

No Docking
Activity VI
data QSAR analysis
Remote Database querying
homolog No
activity VII
Virtual screening
data

NewEdge Technologies

Hit Identification Hit Filtration Hit to Lead Library Generation Lead Optimization

Shape similarity Target specificity Fragment based Scaffold based Fragment based
Ligand Structure SATREA GQSAR LeadGrow GQSAR
ChemDBS

Pharmacophore QSAR based Scaffold hopping Fragment Template Structure Ligand
Ligand Structure VLife QSAR GQSAR + GLib Based Hybrid method
ChemDBS – MolSign Adv LeadGrow VLife SCOPE

Fingerprint 3D-QSAR
Ligand based VLife QSAR
ChemDBS

Docking Target Specificity
Structure based SATREA
BioPredicata


SATREA: For target specificity
SATREA: Specificity Analysis Tool for Region Exploration and Avoidance

Key elements of SATREA
 Identification of regions of active site
to be explored by ligand
 Identification of regions of active site
to be avoided (in green) by ligand
 Electrostatic or hydrophobicity
mapping on the regions to be
explored (in blue and yellow)
 List of neighboring residues to be
explored/avoided
 Quantification of specificity based on
ratio of overlap volumes
Where is SATREA useful
- Specificity analysis of target AKT1 wrt AKT3
- Dotted region is common for both targets  Tool to aid in depth understanding of
- Property mapped region is necessary for specificity specificity requirements of a target
- Unmapped green colored regions must be avoided
 Provide clues in growth of molecules
in the active site

SATREA: Specific and Non-specific Inhibition

FAK2 (PDB id: 3FZS) STK6 (PDB id: 3E5A)
MK14 (PDB id: 1KV2) ABL1 (PDB id: 2F4J)
Specificity MK14/FAK2: >1000 Specificity STK6/ABL1: 1

Left Panel: Yellow ligand is
in common region &
overlaps with only white
region. Magenta ligand
overlaps with white
region, which is avoidance
region for ligand of target
B leading to loss of
specificity
Right Panel: Both yellow
and magenta ligands
accommodate in the
common region & have no
overlap with white or
green region. No target
specificity
Yellow ligand & white isosurface associated to target A (target of interest).
Magenta ligand & green isosurface associated with target B (target against which
specificity to achieve). Dotted region is common between both the targets.

GQSAR: For lead optimization

Key elements of GQSAR
 Alignment independent fragment based
QSAR modeling
 Conformer independent method
 GQSAR models generation for both
congeneric and non-congeneric data
 Provides site specific clues
 Patented method

Where is GQSAR useful
 Lead optimization by using site specific
clues from GQSAR model
 Scaffold hopping by choosing
Publication references
groups/fragments satisfying descriptor
• QSAR Combi Science 2009, 28:36–51 ranges of actives in the dataset
• J Mol Graph Mod 2010;28:683-694  Novel library generation along with
predicted activity of ligands

GQSAR: For lead optimization

Actual GQSAR snapshot
shows the newly
optimized molecule
formed on the screen
with R1 fragment (red) of
Akt225 and R2 fragment
(yellow) of Akt126


GQSAR: Scaffold Hopping of Akt1 inhibitors
Original Dataset Scaffolds New Scaffolds suggested by
used in GQSAR GQSAR & are in BindingDB

GQSAR model built
using 264 molecules
from BindingDB and
corresponding
scaffolds are shown
(left panel). Use of
GQSAR model to find
new scaffolds showed
match from revised
dataset of Akt1
inhibitors (right panel)
from latest BindingDB

This demonstrates that GQSAR is useful in scaffold hopping

kNN-MFA: For lead optimization

Key elements of kNN-MFA
 Novel 3D QSAR method that inherently
captures non-linearity in the relationship
of activity with field values
 Considers steric, electrostatic &
hydrophobicity fields
 Improved predictive ability than
conventional 3D-QSAR methods
 Extensively used method in the literature
(~35 publications)
Where is kNN-MFA useful
 Location of field values and ranges of
field values provide clues for lead
optimization
Publication reference  Automatic selection of groups at a given
J Chem Inf Model 2006;46: 24-31 site satisfying required field ranges
providing optimized lead

VLifeSCOPE: For lead optimization

Key elements of VLifeSCOPE
 Active site residues are considered
 Partitioning of binding energy or docking
score in to residue wise interactions terms
& utilized as descriptors, f(Exp. Activity)
 Generates QSAR models of docked
compounds
Where is VLifeSCOPE useful
Met258 Lys120
 Identifies key residues for protein-ligand
interactions leading to optimization of
Steric groups Ligand
H-Bond
Val49  Improved ranking of ligands compared to
Ile219
Arg47 docking
Publication reference  Allows screening of large databases to
predict the activity of new compounds
• Bioorg Medl Chem 2004; 12: 2937-2950
• Chem Biol Drug Des 2009; 74: 582–595

GLib: Library generation by scaffold hopping

Key elements of GLib
 Generates novel molecules by
combinatorial principle using
fragments of existing dataset
 Generated library adheres to
applicability domain of original
dataset
 Activity prediction for newly
generated molecules
 Intuitive graphical interface

Where is GLib useful
 Exhaustive chemical space exploration by hybrid library provides optimized leads
 Suggests new molecules to be synthesized in the series


QSAR benchmarking II: kNN
Activity prediction benchmarking : VLifeSCOPE
MFA
Comparison of VLifeSCOPE with force field based docking as a means of
predicting likely experimental MIC
Accuracy measure: Rank order comparison of each molecule of the data set with their MIC

9
8 Rank in the Lab
8 8 8
7 VLife SCOPE
7 7 7
6 Binding Energy
6 6 6
5
5 5 5
4
Reference:
4 4 4
3 Modeling and
3 3
3 interactions of
2
2 2 2 Aspergillus fumigatus
1 lanosterol 14-α
1 1 1
demethylase ‘A’ with
0 azole anti fungals
Voriconozol ER30346 TAK187 J1_114 Sankyo SCH42427 Itraconozole Fluconozole (Bioorganic & Medicinal
Chemistry 2004, 12
2937–2950)

With VLife SCOPE predicted rank order for first four compounds exactly matches
experimental finding while binding energy based rank order is completely off track.


QSAR benchmarking I:
QSAR benchmarking : GQSAR
GQSAR
Comparison of patent pending GQSAR with other 2D QSAR and 3D QSAR
methods for accuracy of predicted activity
Accuracy measure: Established statistical measures, pred_r2 and q2

2D QSAR 3D QSAR NewEdge 1
GQSAR 0.9
0.8
Independent of
conformations
 X  0.7
0.6
Molecule
alignment
 X  0.5
independent 0.4
0.3
Fast evaluation
of descriptors
   0.2
0.1
0
Site specific
clues for NCE
X   Pred_R2 Q2
design
Solution to
inverse QSAR
X X 
problem

Reference: Group-Based QSAR (G-QSAR): Mitigating Interpretation Challenges in QSAR ,Subhash Ajmani, Kamalakar
Jadhav, Sudhir A. Kulkarni, QSAR & Combinatorial Science, 28, 1, 2009, 36–51

VLife’s patented GQSAR is more accurate than similar technologies and far more
insightful for lead optimization.

QSAR benchmarking II: kNN
QSAR benchmarking : kNN-MFA
MFA
Comparison of kNN MFA method with other QSAR methods for accuracy of
prediction in case of non-linear relationships
Accuracy measure: Established statistical measures, pred_r2 and q2

Steroids Anti-Inflammatory Cancer
1.2
1 0.9
1
0.9 0.8
0.8
0.8 0.7 0.6
0.7
0.6 0.4
0.6
0.5 0.2
0.5 0
0.4
0.4 -0.2
0.3
0.3 -0.4
0.2 0.2 -0.6
0.1 0.1 -0.8
0 0 -1

Pred_r2 q2 Pred_r2 q2 Pred_r2 q2

Reference: Three-Dimensional QSAR Using the k-Nearest Neighbor Method and Its Interpretation by Subhash
Ajmani, Kamalakar Jadhav, Sudhir A. Kulkarni , Journal of Chemical Information and Modeling, 2006, 46, 24-31

VLife’s kNN-MFA method is consistently more accurate than similar technologies
across widely varying chemistries.

Docking benchmarking I:
Docking benchmarking – I: GRIP
GRIP
Comparison with multiple other technologies for accuracy
Accuracy measure: Difference of < 1A0 between predicted and laboratory determined result
Number of protein ligand complexes

250
100

Protein ligand complexes
200
80
with RMSD < 1

60 150

40 100

20 50

0
0
Docking tool
RMSD1 <1.0 RMSD1 <1.5
Accuracy

Reference: Standard data for comparison taken from ‘Deciphering common failures in molecular docking of ligand-protein
complexes’ by G.M. Verkhivker, D. Bouzida, D.K. Gehlhaar, P.A. Rejto, S. Arthurs, A.B. Colson, S.T. Freer, V. Larson, B. A.
Luty, T. Marronne, P.W. Rose, J. Comp. Aid. Mol. Des., 2000, 14, 731-751


Docking benchmarking II:
Docking benchmarking – II: GRIP
GRIP
Comparison with multiple other technologies for speed and ability to handle
complex molecules
Speed measure: Minutes taken per docking
Molecular complexity measure: Number of rotatable bonds within molecule

Speed Complexity
4
100
Average time per docking

3.5

Percentage structures
3 80

below 1.0 A
2.5
60
2
1.5 40

1
20
0.5
0 0
Docking tool >1 >5 > 10 > 15
Number of rotatable bonds

VLife’s GRIP docking is faster, more accurate and is better able to handle complex
molecules vis-a-vis wide spectrum of competing technologies.

Docking benchmarking II:
Peer Reviewed Publication Citations
GRIP
VLife software and research has been cited in more than two hundred peer
reviewed publications in the last 3 years

Product citations in Peer Reviews Journals VLife Component No. of Citations
for the last 3 years BioPredicta 50
80 ChemDBS 4
60 GQSAR 4
No. of Citations 40 LeadGrow 4
MolSign 2
20 Citations Proviz 7
0
QSARPlus 80
>3 2.0 to 2.9 <2
VLife SCOPE 2
Impact Factor
VLife Research work 107

Representative list of Journals (Impact Factor)
• Biosensors and Bioelectronics (5.143) • Bioorganic & Medicinal Chemistry (3.075)
• Current Medicinal Chemistry (4.994) • Mutation Research - Fundamental and Molecular
• Journal of Medicinal Chemistry (4.898) Mechanisms of Mutagenesis (3.198)
• Protein Science (4.856) • Journal of Chemical Information and Modeling (2.986)
• International Journal of Cancer (4.734) • European Journal of Medicinal Chemistry (2.882)
• Molecular BioSystems (4.23) • Molecular Diversity (2.708)
• BMC Bioinformatics (3.78) • QSAR & Combinatorial Science (2.594)
• Journal of Computer-Aided Molecular Design (3.62) • Journal of Molecular Graphics and Modeling (2.347)
• Journal of Molecular Modeling (2.018)

VLife Sciences Technologies Pvt. Ltd. Yogesh Wagh
101, Pride Purple Coronet, Manager Scientific Solution & Services
Pune , MH 411 045 Email : yogeshw@vlifesciences.com
India Phone: +91 202 729 1590


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