Computational study of the effects of cations and anions to the cytotoxicity of diverse ionic liquids by supervised machine learning

Abstract

Ionic liquids (ILs) have been widely used in many fields due to their unique physicochemical properties, and even they were considered as green solvents. However, the recent researches showed that ILs might bring potential risk to environment and humans. In this work, genetic function approximation (GFA) and least squares support vector machine (LSSVM) models were developed for predicting the cytotoxicity of a great variety of ILs, including 9 types of cations and 44 types of anions, to Leukemia Rat Cell Line (IPC-81) based on the structural descriptors calculated from the combination of cations and anions. Seven descriptors were selected by GFA to develop the linear QSAR model. According to the discussion of descriptors, the cation structure was the main factor of the toxicity which mainly depends on the hydrophobicity and space structure of cations. The LSSVM model was built to predict accurately the cytotoxicity of ILs to capture the nonlinear nature. The rigorous internal and external validation and applicability domain (AD) were performed to verify the reliability and predictability for GFA and LSSVM models. The results indicated that both models could be used for estimating the cytotoxicity of new ILs to IPC-81, and the discovered key structural characteristics could provide reference information for designing and synthesizing safer ILs.