Extended Regression Modeling of the Toxicity of Phenol Derivatives to Tetrahymena pyriformis Using the Electronic-Structure Informatics Descriptor

Abstract

The extreme gradient boosting regression (XGBR) method was applied to 245 phenol derivatives in order to establish a regression model to predict their toxicity to Tetrahymena pyriformis. The modeling was done by using the set of “electronic-structure informatics” (ESI) descriptors recently suggested by the present authors. It is shown that the XGBR method is successful in predicting the toxicity of phenols in each class of five modes of action (MOA). A feature importance analysis showed that the different ESI descriptors were found to be important depending on the MOA. Through comparisons with the optimized descriptor set previously suggested by M. T. D. Cronin et al. (Chemosphere., 49, 1201-1221, (2002)), it is shown that the ESI descriptor set, which has been applied to different types of target variables, is of similar quality in regression modeling.