title: Toxicity of ionic liquids: database and prediction via quantitative structure-activity relationship method.
abstract: A comprehensive database on toxicity of ionic liquids (ILs) is established. The database includes over 4000 pieces of data. Based on the database, the relationship between IL's structure and its toxicity has been analyzed qualitatively. Furthermore, Quantitative Structure-Activity relationships (QSAR) model is conducted to predict the toxicities (EC50 values) of various ILs toward the Leukemia rat cell line IPC-81. Four parameters selected by the heuristic method (HM) are used to perform the studies of multiple linear regression (MLR) and support vector machine (SVM). The squared correlation coefficient (R(2)) and the root mean square error (RMSE) of training sets by two QSAR models are 0.918 and 0.959, 0.258 and 0.179, respectively. The prediction R(2) and RMSE of QSAR test sets by MLR model are 0.892 and 0.329, by SVM model are 0.958 and 0.234, respectively. The nonlinear model developed by SVM algorithm is much outperformed MLR, which indicates that SVM model is more reliable in the prediction of toxicity of ILs. This study shows that increasing the relative number of O atoms of molecules leads to decrease in the toxicity of ILs.
authors.affiliation: Beijing Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory of Multiphase Complex Systems, Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 100190 Beijing, China; School of Material and Chemical Engineering, Zhengzhou University of Light Industry, 450001 Zhengzhou, China.
authors.forename: Yongsheng
authors.initials: Y
authors.lastname: Zhao
authors.affiliation: School of Material and Chemical Engineering, Zhengzhou University of Light Industry, 450001 Zhengzhou, China.
authors.forename: Jihong
authors.initials: J
authors.lastname: Zhao
authors.affiliation: Beijing Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory of Multiphase Complex Systems, Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 100190 Beijing, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Huang
authors.affiliation: Beijing Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory of Multiphase Complex Systems, Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 100190 Beijing, China.
authors.forename: Qing
authors.initials: Q
authors.lastname: Zhou
authors.affiliation: Beijing Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory of Multiphase Complex Systems, Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 100190 Beijing, China. Electronic address: xpzhang@home.ipe.ac.cn.
authors.forename: Xiangping
authors.initials: X
authors.lastname: Zhang
authors.affiliation: Beijing Key Laboratory of Ionic Liquids Clean Process, State Key Laboratory of Multiphase Complex Systems, Key Laboratory of Green Process and Engineering, Institute of Process Engineering, Chinese Academy of Sciences, 100190 Beijing, China. Electronic address: sjzhang@home.ipe.ac.cn.
authors.forename: Suojiang
authors.initials: S
authors.lastname: Zhang
mesh_terms: D000818:Animals; D045744:Cell Line, Tumor; D016208:Databases, Factual; D052578:Ionic Liquids / Q000737:chemistry* / Q000633:toxicity*; D016014:Linear Models; D008962:Models, Theoretical; D021281:Quantitative Structure-Activity Relationship; D051381:Rats; D060388:Support Vector Machine
chemical_list: D052578:Ionic Liquids
keywords: Ionic liquids; Multiple linear regression (MLR); QSAR; Support vector machine (SVM); Toxicity