Predictive modeling of antibacterial activity of ionic liquids by machine learning methods

Abstract

Structural variation and different bioactivity of ionic liquids (ILs) make them highly promising for the development of novel biocides. Application of computational methods to the evaluation of potential antibacterial activity of chemical compounds is a useful, time- and cost-saving tool replacing numerous experimental syntheses. In the present study, quantitative structure–activity relationship (QSAR) modeling is applied to develop models (based on more than 800 data points) aiming to predict the minimal inhibitory concentration (MIC) of ILs against three types of human pathogens – Staphylococcus aureus, Escherichia coli and Pseudomonas aeruginosa. The random forest model with the AlvaDesc descriptors in general demonstrates the best performance for all the three types of bacteria and is suggested as a final model. To interpret the final model and determine the most significant descriptors, a SHapley Additive exPlanation (SHAP) method was applied. Six amino acid ILs, which were synthesized for the first time, and five halogenide ionic liquids purchased, all based on 1-alkyl-3methylimidozolium cations with different alkyl chain lengths, C10, C12 and C14, are tested in vitro and used to validate the developed QSAR models. The data sets and developed model are available free of charge at http://ochem.eu/article/147386.