Prediction of chemical toxicity to Tetrahymena pyriformis with four-descriptor models

Abstract

A quantitative structure-toxicity relationship (QSTR) model based on four descriptors was successfully developed for 1163 chemical toxicants against Tetrahymena pyriformis by applying general regression neural network (GRNN). The training set consisting of 600 organic compounds was used to train GRNN models that were evaluated with the test set of 563 compounds. For the optimal GRNN model, the training set possesses the coefficient of determination R² of 0.86 and root mean square (rms) error of 0.41, and the test set has R² of 0.80 and rms of 0.41. Investigated results indicate that the optimal GRNN model is accurate, although the GRNN model has only four descriptor and more samples in the test set.

Keywords: General regression neural network; Molecular descriptor; Structure–property relationship; Tetrahymena pyriformis; Toxicity.