Chemometric versus Random Forest Predictors of Ionic Liquid Toxicity

Abstract

The objective of this work was a comparative analysis of the standard chemometric and decision tree(s) models for prediction of biological impact of ionic liquids (ILs) for various combinations of cations and anions. The models are based on molecular descriptors for combinations of the following cations: imidazole, pyridinium, quinolinium, ammonium, phosphonium; and anions: BF 4 , Cl, PF 6 , Br, CFNOS, NCN 2 , C 6 F 18 PBF 4 , C 6 F 18 P. The derived data matrix is decomposed by singular value decomposition of the cation and anion matrices into corresponding first ten components, each accounting for 99.5 % of the corresponding total variances. Biological impact data, i.e. molecular level toxicity, are based on acetylcholinestarase inhibition experimental data provided in MERCK Ionic Liquids Biological Effects Database. Applied were the following models: Principal component regression (PCR), partial least squares (PLS), and decision tree(s) model. The model performances were compared by ten-fold validation. Obtained were the following Pearson regression coefficients R 2 : PCR 0.62, PLS 0.64, and for decision tree forest RFDT 0.992. The decision tree(s) models significantly outperformed chemometric models for numerical predictions of EC 50 concentrations and the classification of ILs into four levels of toxicities.

Chemometrics for Selection, Prediction, and Classification of Sustainable Solutions for Green Chemistry—A Review

“…Moreover, it sometimes happens that one technique is applied to select appropriate descriptors; then another one is used for the prediction of a particular feature. In some cases, the applications of several chemometric methods are compared, as presented with the example of carbon dioxide solubility [67], electric conductivity [70], density [71], and toxicity [74]. In first case, nonlinear models, such as RB (radial basis network) and MLP (multilayer perceptron) turned out to be more adequate when the mathematical complexity of the model is not important or a high accuracy is necessary.…”

“…Some examples of studies concerning the prediction of toxicity for selected chemicals as potential pollutants are summarized in Table 3. One of the most frequently predicted environmental parameters is toxicity, which may be noticed due to the visible trend in IL properties' prediction analysis as summarized above [74][75][76][77]. It is expressed by different endpoints towards various organisms.…”

“…Reproduced from Ref. Chemometric versus Random Forest Predictors of Ionic Liquid Toxicity (Kurtanjek[74]).…”