Predicting the cytotoxicity of ionic liquids using QSAR model based on SMILES optimal descriptors

Abstract

In recent years, ionic liquids (ILs) have attracted great attention due to their remarkable physico-chemical properties. In spite of all advantages of ILs, these compounds probably cause persistent contaminations and constitute environmental risks. Cytotoxicity (Log10(CE50)) data 225 ILs were used in the creation of quantitative structure–activity relationship (QSAR) models by CORrelation And Logic (CORAL) software (http://www.insilico.eu/coral) in which molecular structures are indicated by the simplified molecular input line entry system (SMILES) notation. These global SMILES descriptors are illustrating the presence of some chemical elements and various types of chemical bonds (double, triple, and stereochemical). The balance of correlations (BC) of constructing a quantitative structure–property/activity relationships and the classic scheme were compared. Results from three random splits displayed reliable models for predicting the external test set with the correlation coefficient (R2) and cross validated correlation coefficient (Q2) in ranges of 0.7315–0.8760 and 0.7062–0.8490, respectively. The best predictions obtained by a classic scheme along with the global SMILES descriptors are included in the modeling process. The average statistical characteristics of the external test set are the following: n=44, R2=0.8760, Q2=0.8540, standard error of estimation (s)=0.529, mean absolute error (MAE)=0.400 and Fischer F-ratio (F)=297.