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A set of benzimidazole derivatives were tested for their inhibitory activities against the Gramnegative bacterium
The benzimidazoles are a large chemical family used as antimicrobial agents against the wide spectrum of microorganisms [
Different substituted benzimidazolyl quinolinyl mercaptotriazoles are remarkably effective compounds both with respect to their virus inhibitory activity and their favourable antibacterial activity [
Although a variety of benzimidazole derivatives are known, the development of new and convenient strategies to synthesize new biologically active benzimidazoles is of considerable interest. Quantitative structure activity relationship (QSAR) studies are useful tools in the rational search for bioactive molecules. The main success of the QSAR method is the possibility to estimate the characteristics of new chemical compounds without the need to synthesize and test them. This analysis represents an attempt to relate structural descriptors of compounds with their physicochemical properties and biological activities. This is widely used for the prediction of physicochemical properties in the chemical, pharmaceutical, and environmental spheres. This method included data collection, molecular descriptor selection, correlation model development, and finally model evaluation. QSAR studies have predictive ability and simultaneously provide deeper insight into mechanism of drug receptor interactions [
In view of the above and in continuation of our studies on the inhibitory activities of benzimidazole derivatives, as well as on correlation of molecular properties with activity [
In the first step of the present study, different substituted benzimidazoles (
The screening results reveal that all the compounds exhibited appreciable
Developing a QSAR model requires a diverse set of data, and, thereby a large number of descriptors have to be considered. Descriptors are numerical values that encode different structural features of the molecules. Selection of a set of appropriate descriptors from a large number of them requires a method, which is able to discriminate between the parameters. Pearson's correlation matrix has been performed on all descriptors by using NCSS Statistical Software. The analysis of the matrix revealed nine descriptors for the development of MLR model. The values of descriptors selected for MLR model are presented in
The specifications for the bestselected MLR models are shown in
It is well known that there are three important components in any QSAR study: development of models, validation of models and utility of developed models. Validation is a crucial aspect of any QSAR analysis [
For the testing the validity of the predictive power of selected MLR models the LOO technique was used. The developed models were validated by the calculation of following statistical parameters: PRESS, SSY, S_{PRESS}
PRESS is an acronym for prediction sum of squares. It is used to validate a regression model with regards to predictability. To calculate PRESS, each observation is individually omitted. The remaining n – 1 observations are used to calculate a regression and estimate the value of the omitted observation. This is done n times, once for each observation. The difference between the actual Y value, y_{obs}, and the predicted Y, y_{calc}, is called the prediction error. The sum of the squared prediction errors is the PRESS value. The smaller PRESS is, the better the predictability of the model. Its value being less than SSY points out that the model predicts better than chance and can be considered statistically significant. SSY are the sums of squares associated with the corresponding sources of variation. These values are in terms of the dependent variable, y.
The PRESS value above can be used to compute an
In many cases
To confirm the predictive power of the QSAR models, an external set of benzimidazoles was used. Five benzimidazole derivatives which were tested in our previous paper for their antibacterial activity against the
The values of inhibitory activitiy of a test set of molecules was calculated with the models 1 and 2. These data are compared with experimentally obtained values of antibacterial activity against the same bacteria. From the data presented in
Comparing the activities of the tested molecules it was found that 2aminobenzimidazole derivatives (compounds
From the results and discussion presented above, we conclude that the 2amino and 2 methylbenzimidazole derivatives are effective
All the compounds, were synthesized by a general procedure described by Vlaović [
All the benzimidazole derivatives were tested for their
The MIC measurements were performed by the agar dilution method according to the guidelines established by the NCCLS standard M7A5 [
All molecular modeling studies were performed by using HyperChem 7.5 software (HyperCube Inc, Version 7.5) running on a PIII processor [
The numerical descriptors are responsible for encoding important features of the structure of the molecules and can be categorized as electronic, geometric, hydrophobic, and topological characters. Descriptors were calculated for each compound in the data set, using the software HyperChem [
The complete regression analysis were carried out by PASS 2005, GESS 2006, NCSS Statistical Softwares [
The authors wish to acknowledge the financial support from the Ministry of Science and Technological Development, Republic of Serbia, for this research work.
Plots of predicted versus experimentally observed inhibitory activity of benzimidazoles against
The structures of the compounds studied.
Cmpd  R_{1}  R_{2}  Cmpd  R_{1}  R_{2} 

NH_{2}  H  CH_{3}  H  
NH_{2} 

CH_{3} 
 
NH_{2} 

CH_{3} 
 
NH_{2} 

CH_{3} 
 
NH_{2} 

CH_{3} 
 
NH_{2} 

CH_{3} 
 
NH_{2} 

CH_{3} 

Antibacterial screening summary.
Compound  MIC (μg/mL)  log1/ 

50  3.425  
25  3.951  
12.5  4.278  
6.25  4.615  
50  3.676  
12.5  4.303  
6.25  4.638  
100  3.121  
50  3.648  
25  3.975  
12.5  4.312  
100  3.373  
25  4.000  
12.5  4.335  
12.5  4.446  
0.78  5.787 
Values of molecular descriptors used in the regression analysis.
Cmpd  Clog  

43.63  15.13  430.33  −11.28  133.15  −1.21  292.48  1.54  −0.61  
77.28  26.63  675.88  −7.12  223.28  −9.75  416.78  1.45  0.65  
81.56  28.46  728.44  −5.95  237.30  −9.81  442.99  1.53  0.80  
81.99  28.55  712.38  −6.72  257.72  −9.81  437.70  1.48  0.43  
77.21  26.71  666.31  −7.67  237.29  29.80  409.30  2.16  0.07  
81.49  28.55  720.15  −6.52  251.29  29.61  437.71  2.13  0.22  
80.61  28.64  710.59  −7.35  271.71  30.38  434.96  2.88  0.44  
44.83  15.62  450.85  −4.75  132.16  10.35  304.78  1.36  −0.51  
78.48  27.11  693.35  −2.73  222.29  1.34  423.77  1.32  0.75  
82.76  28.94  745.41  −1.61  236.32  1.23  453.96  1.45  0.90  
83.19  29.04  737.17  −2.44  256.73  1.63  448.98  1.69  0.53  
78.41  27.20  686.80  −3.68  236.27  53.89  422.33  2.40  0.17  
82.69  29.03  741.13  −2.53  250.30  53.68  452.01  2.42  0.32  
81.81  29.12  731.39  −3.36  270.71  54.42  447.07  2.86  0.54 
Best MLR models for the prediction of antibacterial activity.
Model  Coefficient  Error  

1  Intercept  2.0228  0.4432  14  0.7910  0.2999  20.053 
0.0085  0.0019  
2  Intercept  1.88448  0.3965  14  0.8587  0.2624  15.437 
0.0098  0.0018  
−0.0068  0.0031 
Crossvalidation parameters.
Model  PRESS  SSY  PRESS/SSY  S_{PRESS}  

1  1.4444  2.8835  0.5009  0.3212  0.4991  0.5944 
2  1.2609  2.8835  0.4373  0.3001  0.5627  0.6895 
Predicted log1/c_{MIC} values of benzimidazoles tested against
Cmpd  log1/c_{MIC}exp.  Model 1  Model 2  

log1/c_{MIC} pred.  Residuals  log1/c_{MIC} pred.  Residuals  
4.328  4.287  0.041  4.298  0.030  
4.278  4.040  0.238  4.030  0.248  
4.314  4.213  0.101  4.233  0.081  
4.333  4.312  0.021  4.337  −0.004  
4.352  4.414  −0.062  4.442  −0.090 