Chemometric versus Random Forest Predictors of Ionic Liquid Toxicity

Ž. Kurtanjek

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: BF4, Cl, PF6, Br, CFNOS, NCN2, C6F18PBF4, C6F18P. 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 R2: 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 EC50 concentrations and the classification of ILs into four levels of toxicities.

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Keywords
ionic liquids, toxicity, chemometrics, decision tree