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 fivefold cross-validation; giving q(2) = 0.77-0.92 for regression models and accuracy 83-88% for classification models. Twenty 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.

A review of recent advances towards the development of QSAR models for toxicity assessment of ionic liquids

“…Classification and regression QSAR models with good predictive power with accuracy over 88 % and a coefficient Q 2 0.77-0.92 were designed (Hodyna et al, 2016a). The obtained model of antibacterial activity of imidazolium-based ILs was stored in the OCHEM database (www.ochem.eu) and assisted in searching for new potential antimicrobial agents against B. subtilis and Ps.…”

Quantitative structure–activity relationship methods in the discovery and development of antibacterials

“…Similarly, ANN and RF were applied for the creation of a prediction model of Pseudomonas aeruginosa and Bacillus subtilis inhibitors using bibliographical data of imidazolium‐based ionic liquids . Twenty theoretically active synthesis compounds of 1,3‐dialkylimidazolium ionic liquids were assayed.…”

“…Similarly, ANN and RF were applied for the creation of a prediction model of Pseudomonas aeruginosa and Bacillus subtilis inhibitors using bibliographical data of imidazolium-based ionic liquids. 50 Twenty theoretically active synthesis compounds of 1,3-dialkylimidazolium ionic liquids were assayed. It was observed that, for asymmetric compounds, only those with at least one radical containing a 12-carbon alkyl chain showed high antibacterial activity.…”

Quantitative Structure-Activity Relationships to Estimate Toxicity of Ionic Liquids (ILs)

“…Later, several researchers developed QSAR models for predicting bacterial toxicity values based on linear regression methods using different types of molecular descriptors [5, 13, 20]. Hodyna et al [31] developed classification and regression QSAR models with good accuracies, among them the classification method led to better results than the latter [31]. In 2018, He et al [30] presented a QSAR model to predict MIC and MBC values of ILs toward S. aureus.…”