GQSAR presentation

(GQSAR, Group based QSAR), A fragment based
approach: Mitigating Interpretation Challenges in QSAR!

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Agenda

 Brief about QSAR
 A way forward - Why GQSAR
 GQSAR Method
 Case Studies
 Comparing GQSAR
 References
 Q&A

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Brief about QSAR

It is an integral part of rational drug discovery process
Response (y) = f(x1,x2,x3……….xp)

Objectives of QSAR

 To identify molecular features/properties responsible for variation in desired
response.

 To understand mechanism of molecular interaction based on the identified
important features/properties governing desired response

 To build a mathematical model which can be used to predict desired response
of new molecules.

 To generate clues for designing new molecules having promise of superior
activity.

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Continued...QSAR data

Activity Descriptors
Ranging from 0D, 1D, 2D, 3D, 4D
Y1 X11 X12 X13 . . . X1p
Where n > 20, Y normal distributed

Y2 X21 X22 X23 . . . X2p

Y3 X31 X32 X33 . . . X3p

. . . . . . . .

. . . . . . . .
Molecules

. . . . . . . .

Yn Xn1 Xn2 Xn3 . . . Xnp

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Continued...Type of Descriptors

• Hansch Method
Substituent based • Free Wilson Approach

• Conformation Independent
Whole molecule -
2D

• Conformation Dependent
Whole molecule -
• Conformation and Alignment
3D Dependent

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Continued... Qualities of useful QSAR

Offering site specific clues for novel molecule design

Flexibility to study molecular sites of interest

Ease of interpretation

Model to predict activity of novel molecule

Fast descriptor calculation

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A way forward – why GQSAR ?

Conventional 2D QSAR

 Enables fast calculation of molecular descriptors
 Offers basis for searching new candidates from database of already
synthesized molecules
 Interpretation of model is complex
 Offers no design (site specific)clues

3D QSAR

 Offers ease of model interpretation
 Offers clues for better design of molecules
 Tedious process
 Results dependent on selection of conformers and alignment of molecules

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Continued...why GQSAR??

QSAR – Molecular Descriptors Molecular Sites to optimize a particular chemical property
Magnitude and Direction
O count
Mol. logP
logP wt. ?
Mol. wt.
Mol.wt.
logP
Mol. wt. ? R3
logP wt. ?
Mol.
Mol.wt.
logP R4 R2
logP
logP wt.
Mol. ?
R5 No. of
HBA R1 logP wt.
Mol. ?
heavy atoms
N
N

HBD
H 2 NO 2 S

Interacting sites to be optimized simultaneously through
corresponding chemical properties

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Group Based QSAR Method[GQSAR]

Deals with molecular fragment/group based descriptors to build QSAR model

Fragments are derived by applying specific chemical rules by fragmentation
along:
 Specific bonds
 Bonds on the ring fusion
 Regions of molecules that can be separated from common structural feature
such as atom, bond and ring
 Any pharmacophoric feature such as HBA , HBD, hydrophobic group, charged
group etc.

GQSAR fragment descriptors should not be confused with already existing 2-
Dimensional topological Descriptor

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Continued… GQSAR

Fragment C

Fragment B

Fragment A

Identify important molecular sites and their corresponding properties to aid
in novel molecule design
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Continued… Group Descriptors
GQSAR, we are calculating these descriptors for each molecular
fragments and not for the whole molecule
2D Descriptors
HBA, HBD, rotatable bonds, chi indices, valence-chi indices, electro-
topological indices, substituent constants etc.

Fragment A Fragment B Fragment C

Volume, surface area, dipole moment, moment of inertia, radius of
gyration, polar surface area etc.
3D Descriptors
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Continued… Group Descriptors Cross Terms

D1A

D1A x D2 B
Fragment A D1
D1A - D2 B
D1A + D2 B
Fragment B D2
D1A / D2 B

D2 B f (D1A,D2 B )

Descriptors

Inclusion of Interactions of various fragments….
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GQSAR Modeling Methods

 Variable selection methods:
 Stepwise forward
 Stepwise forward-backward QSARpro software offers a very
 Stepwise backward
unique facility to couple any of
 Simulated annealing method
 Genetic algorithm the variable selection methods,

 Statistical model building methods: with any statistical model
 Multiple regression building method
 Principal component regression
 Partial least squares regression
 k-nearest neighbor
 Neural Networks

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GQSAR – Molecular Fragmentation

Congeneric series Non-congeneric series

R3
D B
R4 R2
3 1 A
R5
R1
N
N
Y X
H2NO 2S C
Substituted 1,5-diphenylpyra z ole as Fragmentation of AKT1 inhibitors
Cox-2 Inhibitors

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GQSAR Case Study I
Congeneric series

R3
R4 R2

R5
R1
N
N

H2NO 2S

Substituted 1,5-diphenylpyra z ole as Cox-2
Inhibitors

Desiraju, G. R.; Gopalakrishnan B.; Jetti, R. K. R.; Raveendra, D.; Sarma, J. A. R. P.; Subramanya, H. S., Molecules 2000, 5, 945-
955 © VLife Sciences Technologies Pvt. Ltd. 15
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COX-2 GQSAR Model

 Fragmentation of molecule based on substitution sites R1, R2, R3, R4 and
R5

 Calculation of simple 2D descriptors like log P, molecular weight, electro-
topological index, molecular refractivity, Baumann alignment independent
topological descriptors etc.
QSARpro software allows the user to calculate and use more than 1000
descriptors
 For model validation, a training set of 25 and test set of 5 molecules were
considered as reported in the paper

 Model building using stepwise variable selection and multiple regression
and PLS regression methods

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COX-2 Dataset – Model Results
Parameters QSAR GQSAR
Train/Test 25/5 25/5
No. of Descriptors 8 12
Optimum Components 6 4
r2 0.93 0.93
CV_r2 (q2) 0.75 0.86
Pred_r2 0.86 0.90
F-test 41.12 67.46
Prob. Of Significance <0.00001 <0.00001
SEE 0.36 0.34
CV_SE 0.70 0.50
Pred_SE 0.22 0.17
Zscore_CV 3.48 4.02
Best_ran_CV -0.77 -1.28
α_ran_CV <0.001 <0.001
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GQSAR Flow
Set of molecules with reported activity or property

Define rules to fragment molecules

Create fragment for each molecule in the set

Calculate molecular fragment descriptors

Apply variable selection and model building method

Validate/Finalize GQSAR model

Interpret finalized model and utilize direction to design novel molecules

Predict activity of designed molecule using developed model

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Addressing inverse QSAR problem

 GQSAR – Sites to optimize along with a particular chemical property
 Identify R1-R5 ranges using active molecules properties ranges in the
dataset – using kNN and regression method

R3
R4 R2

Search Similar R5
N
Fragments within N R1
Applicability domain
H2 NO2S

Combining New molecules with
Fragment Database fragments R1-R5 desired activity
that satisfy ranges
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QSAR Methods - Comparison

Substituent 2D QSAR 3D QSAR GQSAR
Based
Conformation Independent Y Y N Y
Alignment Independent Y Y N Y
Fast Calculation of
N Y Y Y
Descriptor
Interaction Descriptor N N N Y
Site Specific Clues for NCE
N N N Y
Design
Screening of Large
Y Y N Y
Databases
Generation NCE Library
Y N Y Y

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References

 GQSAR is patented by VLife Sciences Technologies Pvt. Ltd.
 References
 "Group B ased QSAR (G-QSAR) : Mitigating Interpretation Challenges in QSAR”,
QSAR & Combinatorial Science, 28(1),36–51(2009)
 "A Comprehensive Structure-Activity Analysis of Protein Kinase B-alpha (Akt1)
Inhibitors“, Journal of Molecular Graphics and Modelling, (2010) doi:
10.1016/j.jmgm.2010.01.007

For more information & QSARpro trial: yogeshw@vlifesciences.com

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VLife Sciences Technologies Pvt Ltd
101-102 Pride Purple Coronet, Baner Road, Pune 411 045 (MS) India
Tel / Fax : +91 20 2729 1590/1
Email : yogeshw@vlifesciences.com www.VLifeSciences.com

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