Predicting the ecotoxicity of ionic liquids towards Vibrio fischeri using genetic function approximation and least squares support vector machine

Abstract

Ionic liquids (ILs) are widely used in industrial production for their unique physicochemical properties, and they are even regarded as green solvents. However, the recent study showed ILs might pose a potential risk to aquatic ecosystems. In the present work, the quantitative structure-activity relationship (QSAR) models, including genetic function approximation (GFA) and least squares support vector machine (LSSVM) were developed for predicting the ecotoxicity of ILs towards the marine bacterium Vibrio fischeri based on the descriptors calculated from cations and anions. Five descriptors were selected by GFA and used to develop the linear model. From the discussion of descriptors, the cation structure was the main factor to the toxicity, which mainly depended on the size, lipophilic, and 3D molecular structure of cations. In order to capture the nonlinear nature, the LSSVM model was also built for more accurately predicting the ecotoxicity. The GFA and LSSVM models were performed the rigorous internal and external validation, further verifying these models with excellent robustness and predictive ability. Therefore, both of models can be used for the prediction of the ecotoxicity of newly synthesized and untested ILs, and can provide reference information and theoretical guidance for designing and synthesizing safer and more eco-friendly ILs.

Keywords: Ecotoxicity; GFA; Ionic liquids; LSSVM; Vibrio fischeri.