Prediction of toxicity of Ionic Liquids based on GC-COSMO method
Daili Peng, Francesco Picchioni
Abstract
In order to evaluate the toxicity of several different ionic liquids (ILs) towards the leukemia rat cell line (ICP-81), an efficient and reliable quantitative structure-activity relationships (QSAR) model is developed based on descriptors from COSMO-SAC (conductor-like screening model for segment activity coefficient) model. The distribution of screen charge density (σ-profile) of 127 ILs is calculated by GC-COSMO (group contribution based COSMO) method. Two segmentation methods toward σ-profile are used to find out the appropriate descriptors for the QSAR model. The optimal subset of descriptors is obtained by enhanced replacement method (ERM). A multiple linear regression (MLR) and multilayer perceptron technique (MLP) are used to build the linear and nonlinear models, respectively, and the applicability domain of the models is assessed by the Williams plot. It turns out that the nonlinear model based the second segmentation method (MLP-2) is the best QSAR model with an R2=0.975, MSE=0.019 for the training set and R2=0.938, MSE=0.037 for the test set. The reliability and robustness of the presented QSAR models are confirmed by Leave-One-Out (LOO) cross and external validations.