Abstract Quantitative structure activity-relationship (QSAR

Pachori Kanti

Sharma Sonal

Veerkar Prachi

Yadav Mukesh

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 Research Journal of Chemical Sciences ______________________________________________ ISSN 2231-606X 
 Vol. 4(3), 76-80, March (2014) Res. J. Chem. Sci.  
 
 International Science Congress Association        76 
Inverse QSAR approach and Molecular docking studies to design novel 
methoxy substituted Chalcones and their Computational Anticancer activity 
evaluation 
Pachori Kanti
1
, Sharma Sonal
2
, Veerkar Prachi
3 
and Yadav Mukesh
3
 
1Dept. of Chemistry, Govt. Nirbhay Singh Patel Science College, Indore, MP, INDIA 
2Dept. of Chemistry, Govt. Holkar Science College, Indore, MP, INDIA 
3Dept. of Pharmaceutical Chemistry, Softvision College, Indore, MP, INDIA 
Available online at: www.isca.in, www.isca.me 
Received 2nd February 2014, revised 19th February 2014, accepted 16th March 2014 
 
 
 
Abstract  
Quantitative structure activity-relationship (QSAR) studies have emerged as promising tool to in silico prediction and 
optimization of potential bioactive compounds. The purpose of QSAR studies is to save time, cost, and animal toxicity and to 
support green chemistry. Present studies are efforts to design and identify novel chalcones having high potency and 
selectivity. Present investigations identify structural insights of methoxy substituted chalcones in Linear and non-linear 
QSAR models. QSAR studies identify the profound non-linear relationship among structures of methoxy substituted 
chalcones and their biological activity measures (IC50). It concludes that any structural variation in present class of 
chalcones would bring a non-linear change in IC50. MLR produced efficient QSAR models (R
2 
= 0.809). We have designed 
new candidates employing structure-activity relationship obtained from QSAR models. Descriptor based inverse QSAR 
approach has been applied in computational modeling of new small molecules. Furthermore, they have been compared with 
synthesized dataset of methoxy substituted chalcones using molecular docking and ADMET studies.  In course of molecular 
docking studies, newly designed molecules yielded promising results with better binding capacity (Docking rerank Score -
103.089) than previously synthesized compounds. Computational pharmacokinetic and pharmacodynamic (ADMET) studies 
revealed their better intestinal absorption, skin permeability and blood brain barrier penetration. The aim of our work is 
computational designing of novel methoxy substituted chalcones using QSAR and flexible molecular docking based 
techniques. 
 
Keywords: Inverse QSAR approach, flexible molecular docking, novel methoxy substituted chalcones, anticancer activity. 
 
Introduction 
Medicinal chemists today are facing a serious challenge because 
of the increased cost and enormous amount of time taken to 
discover a new drug, and also because of rigorous competition 
amongst different pharmaceutical companies. Thereby, 
importance of Computer Aided Drug Design (CADD) and 
molecular modeling is increasing nowadays. Target specific 
drug discovery is the need of the hour. Techniques evolved in 
the post genomic era have given us an opportunity to accelerate 
discovery process by looking at many cellular processes 
simultaneously. Advances in computational power, algorithms 
and modern database mining techniques are accelerating the 
discovery in science even more1. 
 
A major goal in current drug design is to develop new ligands 
with high affinity of binding toward a given protein receptor. 
Pharmacophore, which is the three dimensional arrangement of 
essential features that enable a molecule to exert a particular 
biological effect, is a very useful model for achieving this goal. 
If the three dimensional structure of the receptor is known, 
pharmacophore is a complementary tool to standard techniques 
such as docking, molecular modeling and offshoot of theoretical 
chemistry and an emerging new science, provides many 
technical advances in reducing the cost of drug discovery. A 
recent estimate puts about 8 months and $ 66 million savings for 
each drug due to use of this advance technology2. Molecular 
modeling and pharmaco-informatics, a new emerging field 
integrates bioinformatics and chemoinformatics applicable to 
drug discovery. This has been projected to save an additional 4-
8 months time and about $70 million per drug. 
 
Quantitative Structure Activity Relationships (QSAR) analyse 
the correlation between the structural features and the biological 
activity in order to predict the activity level of new compounds. 
Using statistical correlation methods, it builds models to predict 
quantities such as   binding affinity, toxicity, or pharmacokinetic 
parameters of existing or hypothetical molecules3. 3D-QSAR 
analyse the three dimensional forces like hydrogen bonds, metal 
ligand contacts, polarization effect and the interaction between 
the electric dipoles. 
 
Chalcones are the precursors of flavanoid and isoflavanoid 
family4. Chalcones are α,β -unsaturated ketone containing the 
reactive ketoethylenic group –CO-CH=CH-5. Chalcones itself 
Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231-606X 
Vol. 4(3), 76-80, March (2014) Res. J. Chem. Sci. 
   
International Science Congress Association  77 
and its derivatives are posses wide range of therapeutic 
activities as antioxidant,4 cytotoxic,6-8 antibacterial,9 anti 
malarial, anti inflammatory10,11, anti – HIV, antifungal, tyrosine 
kinase inhibitory activity12,13. Chalcones exhibit cancer cell 
proliferation by various mechanisms as inducing apoptosis; 
inhibiting tubulin polymerization14,15, inhibiting 
angiogenesis16,17, anti estrogenic activity, uncouple the 
mitochondrial respiration and collapse the mitochondrial 
membrane potential18.   
 
Methodology  
Target Structure Selection and Preparation: In structure-
based computational techniques, it is crucial that we retrieve and 
use appropriate 3D structure of drug target. It is even more 
recommendable to prepare 3D structure of target towards any 
missing chemical information in terms of hybridization, bond 
lengths, bond angles, torsions etc19. Present studies included 3D 
structure Tubulin-Cholchicin protein bound with stathmin-like 
domain complex (PDB code: 1SA0) available from protein data 
bank (PDB) at the Research Collaboratory for Structural 
Bioinformatics (RCSB). We decided to use this specific 
structure since we use only B-chain of the protein composed of 
cofactors, ligands, α-chain (a.c) length-451, β-chain (b.d) 
length-445 and E-chain of stathmin length-142. Molecules 
sketched, cleaned in 3D and saved in MDL SDF file format 
imported in the workspace. For protein preparation while import 
select the option always in assign all below in Molegro Virtual 
Docker (MVD). It assign bonds, assign bond orders and 
hybridization, create explicit hydrogen, assign charges, detect 
flexible torsions in ligands, assign tripos atom types. Preparation 
options (If Missing, Always, Never, Remove) applies to each 
individual molecule (not each individual bond or atom). 
 
Docking Parameters: Molegro Virtual Docker (MVD) sets 
latest and efficient algorithms named PLANTS and PLP scores 
to calculate molecular fields and receptor-ligand interactions. 
PLANTS score was developed by Korb et al20 while combined 
efforts of Yang et al21 and Gehlhaar et al developed PLP 
score22,23. We have selected score as MolDock score [GRID] 
with GRID resolution of 20 A°. Ligand evaluation was applied 
with internal electrostatic ES, internal H-bond, sp2- sp2 
torsions. Binding site coordinates: X- 116.763, Y-90.64, and Z-
6.248 within a constraint of 10 A°. Though a receptor-ligand 
interaction is depicted by a lock and key model arbitrary, it is 
much more complex and case specific specially when there 
could be many keys (ligands) opening same lock (receptor) to 
various extents. Selection of accurate site and most favourable 
pose is bottle neck step in performing molecular docking24. 
Inhibitor binding site was identified from literature; in addition, 
all the available cavities were screened for possible binding. 
 
Inverse QSAR- Design of novel Chalcones: The novel 
molecules were designed using inverse QSAR approach 
wherein we used QSAR models obtained. In this approach the 
significant descriptors responsible for regulation for IC50 (nM) 
values chalcones were studied and optimized. The MLR results 
of QSAR studies were employed to calculate or predict the 
potential (IC50, nM) of new molecules designed 
computationally. The tetra-variable model used for designing of 
novel methoxy substitutes Chalcones, 
 
Model-4 Tetra-variable: IC50 (nM) = 849.395 + 
232.141(Mor21u) - 679.357(Mor29e) - 1023.803(Ele) + 91.605 
(Depressant 50) N=26, R2=0.809, R2A=0.773    
 
Perusal reveals that increase in the value of coefficients for 
descriptors with negative coefficient and decrease in the 
coefficient value for descriptors with positive coefficient value 
could enhance the activity of the molecules and yield better 
molecules with best biological activity. 45 new molecules were 
designed using descriptor based approach.  
 
Results and Discussion 
QSAR Studies: In the present work novel methoxy substituted 
chalcones are designed. The molecular descriptors are numerical 
representation to evaluate and establish the structural activity 
relationship. The structures of chalcones drawn in ChemSketch, 
afterword they converted into SMILES data format (Simplified 
molecular line entry specification). The SMILES data format 
was used to calculate descriptors using E-Dragon (version 5.4). 
The 2500 descriptors belonging to various classes were 
calculated and imported into SARCHITECT evaluation version 
along with structures and their respective biological activity.   
 
The MLR model of QSAR was prepared for methoxy 
substituted chalcones then significant and internally non-
correlated sets of descriptors have been chosen with target size 
four (tetra-variable) limited to thumb rule. MLR results have 
been discussed using tri-variable and tetra-variable models with 
appreciable set of statistical parameters as shown in table 1. To 
produce the novel methoxy substituted chalcone compounds for 
anticancer activity, we used approximately 2000-2500 
molecular descriptors in QSAR, out of that we divided 
descriptors in to training and test data sets.  
 
Tetra-variable MLR model obtained (figure 1) shows linearity 
between predicted and observed IC50 (nM) activities. 
 
Docking Studies: Docking studies have provided the 
comparative view for binding capacity of dataset molecules 
(synthesized) with new molecules designed using QSAR. 
 
Docking is applied as evaluation criteria for newly designed 
molecules. Designed molecule TC-14 shows better binding 
capacity (Rerank Score -103.089) to that of dataset molecules 
reported in literature.  While molecule No. 15 and 34 docked 
with rerank score -102.854 and -98.128 respectively. Table 2 
presents predicted IC50 (nM) of newly designed chalcones and 
docking scores. 
 
Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231-606X 
Vol. 4(3), 76-80, March (2014) Res. J. Chem. Sci. 
   
International Science Congress Association  78 
Table-1 
Statistics of multivariate models used in MLR 
Models R
2 
R
2
CV R
2
A S.E. F-STAT 
Uni-variable 
Ele 
0.335 0.2327 0.308 67.884 12.130 
Bi-variable 
Mor29e+Ele 
0.562 0.4512 0.524 56.288 14.774 
Tri-variable 
Mor21u+Mor29e+Ele 
0.654 0.5241 0.607 51.098 13.922 
Tetra-variable 
Mor21u+Mor29e+ Ele + Depressant-50 
0.809 0.7209 0.773 38.87 22.288 
 
 
Figure-1 
Training set correlation of observed and calculated IC50 nM using tetravariable model of MLR 
Research Journal of Chemical Sciences ___________________________________________________________ ISSN 2231-606X 
Vol. 4(3), 76-80, March (2014) Res. J. Chem. Sci. 
   
International Science Congress Association  79 
Table-2 
Docking results of newly designed molecules 
Designed 
Molecule 
Code 
MolDock 
Score(GRID) 
Score(GRID) 
MolDock 
Score 
Rerank 
Score 
Torsions 
TC-01 -101.33 -100.597 -86.5799 3 
TC-02 -90.8473 -88.6096 -81.7398 3 
TC-03 -99.3372 -100.126 -88.5429 3 
TC-04 -99.3228 -99.1151 -85.4758 3 
TC-05 -95.8902 -92.9692 -85.026 3 
TC-06 -95.3092 -96.1909 -85.1984 4 
TC-07 -101.141 -100.296 -86.7889 3 
TC-08 -93.5568 -94.2729 -82.5531 2 
TC-09 -93.5605 -94.2828 -82.5274 2 
TC- 10 -91.408 -91.9753 -81.1378 2 
TC-11 -93.1726 -93.7984 -82.3324 2 
TC-12 -91.409 -92.0253 -81.2265 2 
TC-13 -92.0234 -92.4188 -81.3354 2 
TC-14* -119.638 -119.321 -103.089 3 
TC-15 -119.625 -119.289 -102.854 3 
TC-16 -92.5672 -93.3589 -82.1232 2 
TC-17 -91.4407 -92.1835 -81.1593 2 
TC-18 -92.0636 -92.9503 -81.904 2 
TC-19 -92.0636 -92.9464 -81.8328 2 
TC-20 -98.4921 -99.8874 -81.7211 3 
TC-21 -98.0289 -99.0462 -84.6618 3 
TC-22 -98.137 -99.0713 -83.5998 3 
TC-23 -98.1239 -99.0365 -84.5198 3 
TC-24 -98.1495 -99.0723 -84.9271 3 
TC-25 -95.5168 -96.1614 -83.5972 3 
TC-26 -97.0112 -97.2025 -85.8632 3 
TC-27 -97.6989 -98.5404 -87.5783 3 
TC-28 -93.6295 -94.1854 -82.5108 3 
TC-29 -95.9762 -96.5836 -84.9201 3 
TC-30 -97.7272 -98.6252 -86.6424 3 
TC-31 -132.964 -134.718 -97.0981 8 
TC-32 -116.643 -116.631 -86.827 5 
TC-33 -110.381 -107.472 -84.3514 2 
TC-34 -109.997 -111.291 -98.1283 3 
TC-35 -115.75 -115.295 -94.7381 3 
TC-36 -105.414 -106.517 -92.0256 3 
TC-37 -106.533 -107.605 -90.3713 2 
TC-38 -106.457 -103.487 -91.1246 3 
TC-39 -97.4048 -98.0125 -84.0592 2 
TC-40 -95.4614 -96.1517 -83.9843 2 
TC-41 -100.449 -101.212 -82.8946 2 
TC-42 -101.094 -102.072 -84.3186 2 
TC-43 -102.784 -103.706 -84.6786 2 
TC-44 -101.973 -101.563 -81.7831 2 
TC-45 -95.4339 -96.8008 -81.4733 3 
 
Conclusion  
Present investigation undertakes multiple linear regression 
(MLR) aided QSAR studies towards identification of structural 
insights of methoxy substituted chalcones. QSAR studies 
identified the profound linear relationship among structures of 
methoxy substituted chalcones and their biological activity 
measures IC50 (nM). It concludes that any structural variation in 
present class of chalcones would bring a linear change in IC50. 
Tetra-variable QSAR model was found statistically fit 
(R2=0.809) and significant in predictive powers. We designed 
new candidates furnishing the knowledge of present QSAR 
studies. Descriptor based inverse QSAR approach has been 
applied to computational modeling of new small molecules as 
chalcones derivatives. Furthermore, they have been compared 
with present dataset of methoxy substituted chalcones 
(synthesized) using molecular docking studies.  In course of 
molecular docking studies newly designed molecules yielded 
promising results in terms of candidate TC-14 showing better 
binding capacity (Rerank Score -103.089) than dataset used in 
supervised training of QSAR studies. 
 
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Inverse QSAR approach and Molecular docking studies to design novel methoxy substituted Chalcones and their Computational Anticancer activity evaluation

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Inverse QSAR approach and Molecular docking studies to design novel methoxy substituted Chalcones and their Computational Anticancer activity evaluation

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