Antibacterial Activity of Imidazolium‐Based Ionic Liquids Investigated by QSAR Modeling and Experimental Studies

• DOI: 10.1111/cbdd.12770
• Publication Date: 2016-04-17T07:52:39Z
• Authors: Diana Hodyna; Vasyl Kovalishyn; Sergiy Rogalsky; Volodymyr Blagoda...; Kirill Petko; Larisa Metelytsia
• Keywords: Imidazoles; Ionic Liquids; Quantitative Structure-Activity Relationship; Neural Networks, Computer; Models, Theoretical; 01 natural sciences; Anti-Bacterial Agents; 0104 chemical sciences

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 q2 = 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.