Development of classification and regression models for Vibrio fischeri toxicity of ionic liquids: green solvents for the future

Abstract

Ionic liquids (ILs) have important industrial applications due to their unconventional and encouraging properties making them unique chemical species with extremely lowered vapour pressure, high thermal stability, and enhanced solvation characteristics. They are considered as “green solvents” chiefly due to their task-specificity and minimal release into the environment. The assessment of toxicity of ILs to living ecosystems has received considerable attention in recent years. Development of predictive quantitative structure–toxicity relationship (QSTR) models for ionic liquids can help in designing derivatives with a reduced toxicity profile, thereby making them greener and eco-friendlier. The present study attempts to develop a classification model as well as a regression model to capture specific structural information of ionic liquids responsible for their toxic manifestation to Vibrio fischeri. The models were developed using various two-dimensional chemical descriptors along with dummy variables and subjected to rigorous statistical validation employing multiple strategies, whereas other models of ILs for Vibrio fischeri toxicity reported so far have not employed such strict tools. The classification model has been characterized by acceptable Wilk's λ statistics, pharmacological distribution diagram assessment, and receiver operating characteristics (ROC) analysis parameters. The regression models have been judged according to the OECD guidelines, and the best model showed encouraging external predictivity (R2pred = 0.739). The toxicity of ionic liquids to V. fischeri was found to be related to branching, molecular size, and solvation entropy of cations along with a lipophilicity contribution of the anions. The present work aims at developing predictive classification and regression models to predict toxicity of ILs to V. fischeri.