A set of benzimidazole derivatives were tested for their inhibitory activities against the Gram-negative bacterium Pseudomonas aeruginosa and minimum inhibitory concentrations were determined for all the compounds. Quantitative structure activity relationship (QSAR) analysis was applied to fourteen of the abovementioned derivatives using a combination of various physicochemical, steric, electronic, and structural molecular descriptors. A multiple linear regression (MLR) procedure was used to model the relationships between molecular descriptors and the antibacterial activity of the benzimidazole derivatives. The stepwise regression method was used to derive the most significant models as a calibration model for predicting the inhibitory activity of this class of molecules. The best QSAR models were further validated by a leave one out technique as well as by the calculation of statistical parameters for the established theoretical models. To confirm the predictive power of the models, an external set of molecules was used. High agreement between experimental and predicted inhibitory values, obtained in the validation procedure, indicated the good quality of the derived QSAR models.

In the present paper, the antibacterial activity of some 1-benzylbenzimidazole derivatives were evaluated against the Gram-negative bacteria Escherichia coli. The minimum inhibitory concentration was determined for all the compounds. Quantitative structure-activity relationship (QSAR) was employed to study the effect of the lipophilicity parameters (log P) on the inhibitory activity. Log P values for the target compounds were experimentally determined by the "shake-flask" method and calculated by using eight different software products. Multiple linear regression was used to correlate the log P values and antibacterial activity of the studied benzimidazole derivatives. The results are discussed based on statistical data. The most acceptable QSAR models for the prediction of the antibacterial activity of the investigated series of benzimidazoles were developed. High agreement between the experimental and predicted inhibitory values was obtained. The results of this study indicate that the lipophilicity parameter has a significant effect on the antibacterial activity of this class of compounds, which simplifies the design of new biologically active molecules.

In the present paper, the antifungal activity of some 1-benzylbenzimidazole derivatives were evaluated against yeast Saccharomyces cerevisiae. The tested compounds displayed in vitro antifungal activity and minimum inhibitory concentration (MIC) was determined for all the compounds. Quantitative structure–activity relationship (QSAR) has been used to study the relationships between inhibitory activity and lipophilicity parameters ( log P). A variety of lipophilicity parameters ( log PHyper, CS log P, mi log P, A log P, IA log P, C log P, log PKow, and X log P) were calculated using different software products, and experimentally determined ("shake-flask" method). On the basis of correlations, the nonlinear structure–activity models were derived between the log 1/cMICand two different lipophilicity parameters. Four high-quality QSAR models were found to have a good predictive ability and a close agreement between the experimental and predicted values was obtained.

2-Amino and 2-methylbenzimidazole derivatives were tested in vitro for their inhibitory activity against the bacteria Bacillus cereus. The minimum inhibitory concentration (MIC) was determined for all compounds. The lipophilicity descriptors were calculated by using CS Chem-Office Software, version 7.0. The stepwise regression method was used to derive the most significant model as a calibration model for predicting the antibacterial activity of this class of compounds. A complete regression analysis resorting to linear and quadratic relationships was made. Theoretical models were validated by leaving one out (LOO) technique, as well as by the calculation of statistical parameters for the established models. The best QSAR model for the prediction of an inhibitory activity of the investigated series of benzimidazoles was developed. A high agreement between the experimental and predicted inhibitory values was obtained. The results indicated that the antibacterial activity could be modeled using the lipophilicity descriptor.