
Research Article


2D Qsar Study of 7Methyljuglone Derivatives: An Approach to Design Anti Tubercular Agents 

M.C. Sharma
and
Smita Sharma



ABSTRACT

Antitubercular activity of 7methyljuglone derivatives series were subjected to quantitative structure activity relationship analysis with an attempt to derive and understand a correlation between the biological activity as dependent variable and various descriptors as independent variables. Several statistical regression expressions were obtained using multiple linear regression analysis. The QSAR models were generated using 19 compounds. The predictive ability of the resulting QSAR models was evaluated employing the leave oneout method of cross validation. Several statistical regression expressions were obtained using multiple linear regression analysis. The analysis of best resulted in the following 2D model which suggests that pIC_{50} = [0.025] MR+[0.278] StrE+[0.028] p+[3.04459] HOMO, n = 19, r = 0.87961, r^{2} = 0.81048, variance = 0.0805, SD = 0.4324, F = 85.78. The study suggested that substitution of group at R1 and R3 position on naphthoquinones ring with hydrophobic nature and low bulkiness are favorable for the antitubercular activity in the concerned microbes. The quantitative structure activity relationship study provides important structural insights in designing of potent antitubercular agents.





Received: December 07, 2010;
Accepted: March 27, 2011;
Published: May 31, 2011


INTRODUCTION
The emergence of drug resistant strains of Mycobacterium tuberculosis,
particularly multiple drug resistant strains has complicated treatment protocols
and raises the concern that tuberculosis may once again become an incurable
disease. For this reason it is critical to discover new drugs acting with a
mechanism different from those of presently used antitubercular drugs. Tuberculosis
(TB), caused by M. tuberculosis, is a major public health and socioeconomically
problem in most of the developing countries (Heym and Cole,
1997; Basso and Blanchard, 1998; Telenti
and Iseman, 2000). Tuberculosis (TB) is a contagious disease. Like the common
cold, it spreads through the air. Only people who are sick with TB in their
lungs are infectious. When infectious people cough, sneeze, talk or spit, they
propel TB germs, known as bacilli, into the air. A person needs only to inhale
a small number of these to be infected. Left untreated, each person with active
TB disease will infect on average between 10 and 15 people every year. Despite
the ready availability of effective treatments, tuberculosis remains a major
public health threat worldwide. With approximately onethird of the world population
currently infected, increased prevalence of the disease in HIVinfected patients
and the emergence of multidrugresistant bacteria, TB remains a major world
health problem. Hence there is a continuing need to find additional lead compounds
and biological targets for novel antitubercular chemotherapies. The acidfast
bacillus Mycobacterium tuberculosis is the causative agent of Tuberculosis (TB).
The tubercle bacillus is a slow growing organism, which does not elicit a sharp
and massive reaction from the host. The tubercle bacillus does not produce any
substance, which is toxic to the normal host. It acts as an irritating foreign
body and tubercle formation can be produced by virulent, avirulent and nonpathogenic
types. The tubercle bacillus is an intracellular parasite and lives and grows
within the host’s tissue cells, macrophages and epithelial cell. The efficacy
of the currently available agents used in standard Tuberculosis (TB) treatment
regimens is severely limited by several factors; including long treatment regimens,
multiple drug treatment regimens, drug interactions and drug resistance (WHO,
1995). Drug resistance and multidrugresistant tuberculosis is perceived
as a growing hazard to human health worldwide. TB ranks among the most important
burdens on human health, not only due to the large number of cases (~9 million/year
worldwide, with incidence rates typically measured per 100,000 population),
but also because about one quarter of sufferers die, most of them young adults.
Globally, the number of TB cases is currently rising at 2% year^{1}.
The fear is that the number of cases resistant to antitubercular drugs may be
increasing much faster (Dye et al., 2002; Dye
and Williams, 2000; PablosMendez et al., 2002;
Petrini and Hoffner, 1999). The major concerns over
drug resistance were a fear of the spread of drugresistant organisms and the
ineffectiveness of chemotherapy in patients infected with them. If these spread
increasingly in a community, TB may become progressively uncontrollable using
currently available chemotherapy. One of the strategies suggested for overcoming
this problem is the fully exploiting the potential of standard short course
chemotherapy based on cheap and safe first line drugs. Furthermore, the development
of potent new antitubercular drugs without crossresistance with known antimycobacterial
agents is urgently needed (Tomioka, 2002). According
to statistics, onethird of the world’s population is currently infected
with the TB bacillus, each year, 8 million people worldwide develop active
TB and about 1.7 million people die (http://www.who.int/tb/en/.).
Currently, an important problem in TB treatment is the development of multidrug
resistant tuberculosis (MDRTB), which can be defined as strains that are resistance
to at least isoniazid and rifampicin, important first line drugs used in TB
treatment. Therefore, there is a need for new drugs of new structural classes
and with a novel mechanism of action other than isoniazid (INH), Rifampicin
(RIF) and Pyridazinamide (PZA). In this regard, since the last decade search
for new antitubercular substances has ranked among the priority areas of chemotherapeutic
research. The spread of MDRTB could cost between 100 and 1400 times the available
treatment costs and further threatens to make TB incurable. Exact data are hard
to estimate but at least 4% of all worldwide TB patients are resistant to at
least one of the current first line drugs. Another serious problem, in the context
of MDRTB, is the XDRTB, abbreviation for extensively drugresistant tuberculosis
(TB) which are strains resistant to first and second line antiTB drugs (De
Souza 2006a, b). The dramatic increase in TB cases
observed in the recent years is a result of two major factors. First is the
increased susceptibility of people infected with Acquired Immunodeficiency Syndrome
(AIDS) to TB, which augments the risk of developing the disease 100fold with
some showing crossresistance to as many as nine drugs (El
Sayed et al., 2000). A Quantitative Structureactivity Relationship
(QSAR) enables the investigators to establish a reliable quantitative structureactivity
and structureproperty relationships to derive an in silico QSAR model
to predict the activity of novel molecules prior to their synthesis. The overall
process of QSAR model development can be divided into three stages namely, the
data preparation, data analysis, and model validation, representing a standard
practice of any QSAR modeling. The purpose of QSAR study is to find a relation
between the composition or structure of a compound with its bio or chemical
activity, in order to design a new compound with expected properties or predict
the properties of an unknown compound (Gupta and Kumaran,
2006; Karthikeyan et al., 2006, 2007;
Moorthy and Trivedi, 2006; Chaudhary
et al., 2008; Gokhale and Kulkarni, 2000).
A wide range of descriptors has been used in QSAR modeling. These descriptors
have been classified into different categories, including constitutional, geometrical,
topological, quantum chemical and so on. There are several variable selection
methods including Multiple Linear Regression (MLR). To gain insight into the
structural and molecular requirement influencing the antitubercular activities,
we herein describe the QSAR analysis of 7methyljuglone derivatives. The relevance
of the model for the design of novel derivatives should be assessed not only
in terms of predictivity, but also in terms of their ability to provide a chemical
and structural explanation of their binding interaction. In this research, an
attempt has been made to describe the Quantitative Structureactivity Relationship
(QSAR) analysis of 7methyljuglone derivatives to study and deduce a correlation
between structure and antitubercular activity of these derivatives.
MATERIALS AND METHODS
Data set: The activity data of 7methyljuglone derivatives was taken
from the reported study of Mahapatra et al. (2007).
The activity data have been given as IC_{50} values, where IC_{50}
refers to the experimentally determined molar concentration of the 7methyljuglone
derivatives required to fifty percentage inhibitory concentrations. The biological
activity values [IC_{50} (μg mL^{1})] reported in the
study were converted to nanomolar units and then further tolog scale and subsequently
used as the response variable for the QSAR analysis. The log values of IC_{50}
along with the structure of compounds in the series is presented in Table
1. Initially the series was subjected to analysis (Fujita
and Ban, 1971) using a regression technique to estimate the de novo contribution
of substituents to the activity of the molecules. The method used in this study
is a modification of the FreeWilson technique. Here, the log of activity is
considered to be a free energy related parameter which is additive in nature.
Fujita and Ban derived a linear equation for a set of substituent’s, in
the form of Eq. 1 as follows:
where, BA is biological activity, Gi is the log activity contribution or the log activity enhancement factor of the ith substituent relative to that of H and Xi is a parameter which takes a value of 1 or 0 according to the presence or absence of the ith substituent and μ = log BA, calculated for the unsubstituted compound, i.e., parent compound. The data was transferred to a statistical program in order to establish a correlation between physicochemical parameters as an independent variable and the antitubercular activity as a dependent variable using a sequential multiple linear regression analysis method (in sequential multiple regression the program searched for all permutation and combination sequentially for the data set).
Geometry optimization: The series was further subjected to molecular
modeling studies using CS Chem Office Software version 7.01 (Cambridge soft)
(CS Chem Office, 2002). The structure of the compounds
was drawn in Chem Draw Ultra version 7.01 and then copied to Chem 3D Ultra to
create the threedimensional (3D) model, which was saved as the template model.
For every compound, the template model was suitably modified considering its
structural features so that every compound maintained the same sequence of atoms.
Table 1: 
Structure and observed biological activity of series 

^{a}:IC_{50} (in nM) was the in vitro
observed biological activity of compounds,^{ b}: Negative logarithmic
value of IC_{50}, c: Test compounds 
The molecular structures of all 19 compounds were sketched using the Chemdraw
Ultra (Version, 7.01) software and energy minimized via MOPAC with energy tolerance
value of root mean square gradient 0.001 kcal mol^{1} and maximum number
of iteration set to 1000. These structures were then subjected to energy minimization
using force field molecular mechanics2 (MM_{2}) until the Root Mean
Square (RMS) gradient value became smaller than 0.1 kcal mol^{1}. Å.
Minimized molecules were subjected to reoptimization via Austin model1 (Kier,
1971) method until the RMS gradient attained a value smaller than 0.0001
kcal mol^{1} Å using MOPAC. The geometry optimization of the
lowest energy structure was carried out using Eigenvector following routine.
The descriptor values for all the molecules were calculated using compute properties
module of programme. The energy minimized geometry was used for the calculation
of physicochemical descriptor and extended Huckel charges of different atoms.
These compound properties are the physicochemical descriptors that may be used
to estimate the SAR of molecules. All conformers generated for each structure
were analyzed in conformational geometrics panels with great care, and the lowest
energy conformation of each structure was selected and added to a molecular
database to compute various physicochemical properties. The descriptor values
used in the model generation are shown in the Table 2.
Table 2: 
Calculated values of independent variables 

Statistical methods and molecular descriptors: The values of substituent
constants like hydrophobic (p), steric (Molar refractivity or MR), Hydrogen
Acceptor (HA), Hydrogen Donor (HD) and electronic (field effect or F,
resonance effect or R and Hammett's constant or s) were taken from the literature
(Hansch and Leo, 1979). The series was divided in to a
training set of 15 compounds and a test set of 4 compounds carried out automatically
by the VALSTAT software. The sequential multiple linear regression analysis
method was employed. In sequential multiple linear regression, the program searches
for all permutations and combinations sequentially for the data set. The ±
data with in the parentheses are the standard deviations associated with the
coefficient of descriptors in regression equations. Calculated thermodynamic
descriptors included Critical Temperature (Tc), Ideal Gas Thermal Capacity (Cp),
Critical Pressure (Pc), Boiling Point (BP), Henry's Law Constant (H), Bend Energy
(Eb), Heat of Formation (Hf), Total Energy (TE) and Partition Coefficient (PC).
Steric descriptors derived were Connolly Accessible Area (CAA), Connolly Molecular
Area (CMA), Connolly Solvent Excluded Volume (CSEV), Exact Mass (EM), Molecular
Weight (MW), Principal Moment of InertiaX component (PMIX), Principal Moment
of InertiaY Component (PMIY), Principal Moment of InertiaZ Component (PMIZ),
Molar Refractivity (MR) and Ovality (OVAL). Electronic Descriptors Such as Dipole
(DPL), Electronic Energy (ElcE), Highest Occupied Molecular Orbital Energy (HOMO),
Lowest Unoccupied Molecular Orbital Energy (LUMO), Repulsion Energy (NRE), VDW1,4energy
(E14), Non1, 4VDW energy (Ev) and total energy (E) were calculated. Stepwise
multiple linear regression analysis method was used to perform QSAR analysis
employing inhouse VALSTAT (Gupta et al., 2004)
programme establish a correlation between physicochemical descriptor used in
this study as independent variable and Antitubercular activity as dependent
variable using sequential multiple linear regression analysis method (in sequential
multiple regression the program searched for all permutation and combination
sequentially for the data set). The ±data within the parentheses are
the error of regression coefficients associated with corresponding regression
coefficients in regression equation. The best model was selected on the basis
of various statistical parameters such as Correlation Coefficient (r), Standard
Error of Estimation (SE), Sequential Fischer test (F) the Bootstrapping r^{2},
chance, Q^{2} value, S_{press} value, Standard Deviation of
Error Prediction (SDEP) and the predictive squared correlation coefficient of
the test set (r^{2} pred) crossvalidated squared correlation coefficient
using leave one out procedure r^{2} chance statistics (evaluated as
the ratio of the equivalent regression equations to the total number of randomized
sets; a chance value of 0.001 corresponds to 0.1% chance of fortuitous correlation),
outliers (on the basis of Zscore value) and predictive squared correlation
coefficient of test set r^{2} pred r^{2}. The squared correlation
coefficient (or coefficient of multiple determination) r^{2} is a relative
measure of fit by the regression equation. Correspondingly, it represents the
part of the variation in the observed data that is explained by the regression.
The correlation coefficient values closer to 1.0 represent the better fit of
the regression. The Ftest reflects the ratio of the variance explained by the
model and the variance due to the error in the regression. High values of the
F test indicate that the model is statistically significant. Standard deviation
is measured by the error mean square, which expresses the variation of the residuals
or the variation about the regression line. Thus standard deviation is an absolute
measure of quality of fit and should have a low value for the regression to
be significant. Quality of the each model was estimated from the crossvalidated
squared correlation coefficient (Kubinyi, 1993) calculated
root mean square error (SDEP), chance statistics evaluated as the ratio of the
equivalent regression equations to the total number of randomized sets; a chance
value of 0.01 corresponds to 1% chance of fortuitous correlation and bootstrapping
square correlation coefficient (r^{ 2 }subbs), which confirm the robustness
and applicability of QSAR equation.
Multiple linear regression analysis: Multiple linear regression analysis
and other statistical analysis were carried out on all the 19 molecules. The
outlier molecules were then removed to improve the equation's predictive power.
The final set of equations was obtained using 19 molecules and the best equation
was obtained by using the optimal combination of descriptors. Descriptors were
selected for the final equation based on their correlation coefficients and
those descriptors having intercorrelation coefficient below 0.7 were considered,
to select the best equation. Cross validation by leave one out method was carried
out on these final set of 19 molecules to further enhance and validate the predictive
power of the equation. Acceptability of the regression equation was judged by
examining the statistical parameters. Correlation matrix was obtained to justify
the use of more than one variable in the study. The variables used were with
maximum correlation to activity and minimum intercorrelation with each other.
From the statistical viewpoint, the ratio of the number of samples (N) to the
number of variables used (M) should not be very low; usually it is recommended
that N/M = 5. The QSAR equations were constructed for efficacy data of both
species of malaria parasite with the physciochemical descriptors and indicator
variables. The statistical quality of the equations was judged by the parameters
like correlation coefficient (r), explained variance (r^{2}), standard
error of estimate (s) and the variance ratio or overall significance value (F).
The accepted equations are validated for stability and predictive ability using
leaveoneout and cross validation technique. The statistical parameters used
to access the quality of the models are the Predictive Sum of Squares (PRESS)
of validation. Finally, the standard crossvalidation correlation coefficient
r^{2 }and q^{2} are also calculated:
PRESS = Σ (Ypred  Y obs)^{2
}S_{press} = / PRESS/(nk1) 
Where:
N 
= 
No. of compounds used for crossvalidation 
Y_{i} 
= 
Experimental value of the physicchemical property for the i^{th}
sample 
Y 
= 
Value predicted by the model built without the sample i 
RESULTS AND DISCUSSION
When data set was subjected to stepwise multiple linear regression analysis,
in order to develop 2DQSAR between antitubercular activity in various microbes
as dependent variables and substituent constants as independent variables, several
models were obtained. Acceptability of the regression model was judged by examining
the correlation coefficient (r), squared correlation coefficient (r^{2}),
Ftest (F) and standard deviation. QSAR (Desai et al.,
2001; Ghosh and Bagchi, 2010; Manvar
et al., 2010; Narute et al., 2008;
Sivakumar et al., 2007, 2010a,
b; Sharma et al., 2010;
Sharma and Sharma, 2010) in Biological activity data
and various physicochemical parameters were taken as dependent and independent
variables respectively and correlations were established using sequential multiple
regression analysis in the help of design new molecules of antitubercular activity.
• 
pIC_{50} = [0.025] MR +[0.278] StrE + [0.028] p +
[3.04459] HOMO 
• 
n = 19, r = 0.87961, r^{2} = 0.81048, variance = 0.0805, SD =
0.4324, F = 85.78 
Model1 has a good correlation coefficient (r = 0. 87961), the model is tested
for outlier by Zscore method and no compound was found to be an outlier, which
suggested that the model is able to explain the structurally diverse analogs.
The r^{2}_{bs }is 0.5964 as par with the conventional squared
correlation coefficient (r^{2}). Randomized biological activity test
(p<0.014) revealed that the results were not based on chance correlation.
The inter correlation among the parameters is 0.108. The cross validated squared
correlation coefficient (Q^{2} = 0.7985), the predictive residual sum
of square S_{PRESS} = 0.2665) and the standard error of prediction (S_{DEP
}= 0.7259) suggested good internal consistency as well as predictive activity
of the biological activity with high HOMO. The above model is validated by predicting
the biological activities of the test molecules, as indicated in Table
3. The plot of observed versus predicted activities for the test compounds
is represented in (Table 3). From Table 3
it is evident that the predicted activities of all the compounds in the test
set are in good agreement with their corresponding experimental activities and
optimal fit is obtained.
• 
pIC_{50} = [+1.158] + PMIY [9.4057] +SBE [0.156]
+HOMO [0.024] +Ovality [0.365] 
• 
n = 15, r = 0.81627, r^{2 }= 0.76205, SD. = 0.104, F = 7.636 
Model2 has a better correlation coefficient (r = 0.81627), the model is tested
for outlier by Zscore method and no compound was found to be an outlier, which
suggested that the model is able to explain the structurally diverse analogs.
The inter correlation among the parameter of the descriptors is 0.108. The r^{2}_{bs
}is 0.7513 as par with the conventional squared correlation coefficient
(r^{2}). Randomized biological activity test (p<0.001) revealed that
the results were not based on chance correlation. The inter correlation among
the parameters is 0.108. In general the model fulfills the statistical validation
criteria to the significant extent however; it is a useful theoretical base
for proposing more active compounds. In model PMIY,SBE, HOMO contributed positively
to the activity.
Table 3: 
Predicted biological activity and LOO predicted activity
of QSAR models 

The above model is validated by predicting the biological activities of the
test molecules, as indicated in Table 3. The plot of observed
versus predicted activities for the test compounds is represented in Table
3.
• 
pIC_{50 }= 0.322 (±0.199) Log P+0.0237 (±0.019)
DPL10.189 ( ±0.341) LUMO 
• 
n = 15, r = 0.8038, r^{2 }= 0.75321, variance = 0.00431, SD =
0.56396, F = 51.769 
Model3 fulfills many of the statistical validations such as the correlation
coefficient; the cross validated squared correlation coefficient, standard deviation,
bootstrapping squared correlation coefficient and chance. But the predictive
residual sum of square standard error of prediction is less than 0.5 (0.35).
The correlation accounted for more than 72.9% of the variance in the activity.
The data showed an overall internal statistical significance level better than
99.9% as F_{ (3, 16 α 0.001)} = 51.769 which exceeds the tabulated
F_{ (3, 16 α 0.001)} = 9.01, the cross validated squared correlation
coefficient (Q^{2} = 0.7032), the predictive residual sum of square
S_{PRESS} = 0.1769) and the standard error of prediction (S_{DEP}
= 0.2784) suggested good internal consistency as well as predictive activity
of the biological activity with high logP. In general the model fulfills the
statistical validation criteria to the significant extent however; it is a useful
theoretical base for proposing more active compounds. In model LUMO contributed
negatively and LogP, DPL contributed positively to the activity. The above model
is validated by predicting the biological activities of the test molecules,
as indicated in Table 3 The plot of observed versus predicted
activities for the test compounds is represented in Table 3.
• 
pIC_{50}= [2.392] +BE [0.000108] +SBE [0.212] +VDW
[0.074] 
• 
n = 159, r = 0.74610, r^{2 }= 0.72183, SD. = 0.123688, F = 45.3295 
Model 4 (pIC_{50}) has a better correlation coefficient (r = 0.74610), the model is tested for outlier by Zscore method and no compound was found to be an outlier, which suggested that the model is able to explain the structurally diverse analogs. The inter correlation among the parameter of the descriptors is 0.3967. The r^{2}_{bs }is 0.759 as par with the conventional squared correlation coefficient (r^{2}). Randomized biological activity test (p<0.003) revealed that the results were not based on chance correlation. The inter correlation among the parameters is 0.108. In general the model fulfills the statistical validation criteria to the significant extent however; it is a useful theoretical base for proposing more active compounds. In model BE contributed negatively and SBE, VDW contributed positively to the activity.
• 
pIC_{50}= [2.392] +BE [0.000108] +MR [0.212] +VDW
[0.074] + HOMO [0.243] 
• 
n = 19, r = 0.7309, r^{2 }= 0.5338, SD. = 0.123688, F = 35.3295 
Model5 has a better correlation coefficient (r = 0.730), the model is tested for outlier by Zscore method and no compound was found to be an outlier, which suggested that the model is able to explain the structurally diverse analogs. The inter correlation among the parameter of the descriptors is 0.397. The r^{2}_{bs} is 0.759 as par with the conventional squared correlation coefficient (r^{2}). Randomized biological activity test (p<0.003) revealed that the results were not based on chance correlation. The inter correlation among the parameters is 0.108. In general the model fulfils the statistical validation criteria to the significant extent however, it is a useful theoretical base for proposing more active compounds. In model BE contributed negatively and SBE, VDW contributed positively to the activity. Validation of the model: Model1 shows a better correlation coefficient (r = 0.8796) which accounts for more than 87.96% of the variance in the activity, also the intercorrelation among the parameters is less (0.2315). The model shows that in the multivariant model, the dependent variable can be predicted from a linear combination of the independent variables. The pvalue is less than 0.001 for each physiochemical parameter involved in model generation. The data showed an overall internal statistical significance level better than 99.9% as it exceeded the tabulated F_{(3, 17 α 0.001)} = 85.78. The model was further tested for the outlier by the Zscore method and no compound was found to be an outlier, which suggested that the model is able to explain the structurally diverse analog and is helpful in designing more potent compounds using physiochemical parameters. The leaveoneout cross validation method was employed for the prediction of activity (Table 3). The crossvalidated squared correlation coefficient (in the biological activity data of leaveoneout) (Q^{2} = 0.7985), predictive residual sum of square (S_{PRESS} = 0.2665), and standard error of prediction (S_{DEP} = 0.7259) suggested a good internal consistency as well as predictive ability of the biological activity with low S_{DEP}. The r^{2}_{bs} is at par with the conventional squared correlation coefficient (r^{2}). Randomized biological activity results were not based on the correlation. The robustness and wide applicability of the model were further explained by significant r^{2}_{pred} value (0.81048) .In general, the model fulfils the statistical validation criteria to a significant extent to be a useful theoretical base for proposing more active compound. In model (1) p contributed positively where as StrBE contributed positively towards biological activity is representative of atoms of hydrophobic nature in the molecules and suggests that substitution of groups, which are high hydrophobic in nature, might increase the biological activity. Thus, reimproving the p characteristics of the molecule increases the selective activity. Whereas minimizing the property like Str. BE which is helpful for rationalizing the interaction between molecule and receptor surface. The study revealed that distal end substitutions might interact with a hydrophobic pocket at receptor site, hence increasing hydrophobicity of the substituent increase the binding capacity between molecule and receptor surface which potentiate the selectivity as well as activity. CONCLUSION Although, generation of QSAR models with good statistical significance is of paramount importance, the models should also exhibit good predictive ability. 2DQSAR analysis suggested that for all the, substitution at R_{1} position very much dominates the activity as compared to the indicator variable at R_{2} and R_{ 3} position (Compound 116). At R_{1} position, logp contributed positively, which is responsible for hydrophobicity of the molecules; but MR contributed negative, which suggests that less bulky substitutions form the activity. The series was also subjected to molecular modelling using 3DQSAR; all the descriptor values for the molecules, calculated from the programme, were considered as independent variables. The 2D analysis suggested that the substitution at R_{1} position with various alkyl groups affect the antitubercular activity of naphthoquinones ring analogues as compared to substitutions at R_{ 2} and R_{ 3} position. The QSAR studies revealed that spatial parameter PMIY plays a significant role in explaining antitubercular activity of 7methyljuglone. It contributed negatively to the expression, which suggested that less bulky groups around Yaxis in the molecules are favorable for the activity. DPL contributed positively to the activity up to a small extent as compared to the PMIY, suggesting that the moiety, which increases the charge distribution over the molecules, is favourable for the activity and optimising the hydrophobicity and bulkiness at R_{1} position. The developed QSAR model can be utilized for the further development of new molecules belonging to the class of 7methyljuglone to exhibit good antitubercular activity, as it reveals the various physicochemical parameters that play important roles in exhibiting potential antitubercular activity. The predictive ability of the models was gauged by a cross validation procedure following a leaveoneout scheme. All the models exhibit high q2 and low r^{2}se and q2se values confirm their excellent predictive potential. The linear QSAR models have been successfully established using the different chemo metric tools like enhanced replacement method, multiple regressions. Predictive quality of the models was tested using predictive r^{2} (predr^{2} ), followed by a modified r^{2} (r^{2}m) value based on training set, test set. (LOO) values for whole data set and training/test set are in agreement reflecting external validation characteristic of the developed QSAR models. The values of these parameters ensure the predictability, reliability and acceptability of the model. Based on all the statistical and validation parameters enhanced replacement method was found to give better results. QSAR models were proposed for antibacterial activity of the 7methyljuglone derivatives using chem. SAR descriptors employing sequential multiple regression analysis method. The models also provide valuable insight into the mechanism of action of these compounds. The developed models showed good statistical significance in internal (r_{2}, q_{2} group cross validation and bootstrapping) validation and performed very well in predicting the Biological Activity (BA) of the compounds in the test set. ACKNOWLEDGMENT The authors are thankful to Head, School of Pharmacy, Devi Ahilya Vishwavidyalaya Indore India to provide trial version of software.

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