Antibacterial activity of imidazolium-based ionic liquids investigated by QSAR modeling and experimental studies

Abstract

Predictive QSAR models for the inhibitors of B. subtilis and Ps. aeruginosa among imidazolium-based ionic liquids were developed using literary data. The regression QSAR models were created through Artificial Neural Network and k-Nearest Neighbor procedures. The classification QSAR models were constructed using WEKA-RF (Random Forest) method. The predictive ability of the models was tested by 5-fold cross-validation; giving q²=0.77-0.92 for regression models and accuracy 83-88% for classification models.

20 synthesized samples of 1,3-dialkylimidazolium ionic liquids with predictive value of activity level of antimicrobial potential were evaluated. For all asymmetric 1,3-dialkylimidazolium ionic liquids, only compounds containing at least one radical with alkyl chain length of 12 carbon atoms showed high antibacterial activity. However, the activity of symmetric 1,3-dialkylimidazolium salts was found to have opposite relationship with the length of aliphatic radical being maximum for compounds based on 1,3-dioctylimidazolium cation.

The obtained experimental results suggested that the application of classification QSAR models is more accurate for the prediction of activity of new imidazolium-based ILs as potential antibacterials.

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The developed QSAR models can be used to search for a new active symmetric and asymmetric 1,3-dialkylimidazolium ionic liquids against B. subtilis and Ps. aeruginosa bacterial strains.

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