Random forest algorithm-based accurate prediction of chemical toxicity to Tetrahymena pyriformis

The random forest (RF) algorithm, together with ten Dragon descriptors, was used to develop a quantitative structure-toxicity/activity relationship (QSTR/QSAR) model for a larger data set of 1792 chemical toxicity pIGC(50) towards Tetrahymena pyriformis. The optimal RF (ntree =300 and mtry =3) model yielded root mean square (rms) errors of 0.261 for the training set (1434 chemicals) and 0.348 for the test set (358 chemicals). Compared with other QSTR models reported in the literature, the optimal RF model in this paper is more accurate. The feasibility of applying the RF algorithm to predict chemical toxicity pIGC(50) towards Tetrahymena pyriformis has been verified.

Automated summary

The random forest algorithm combined with ten Dragon descriptors was employed to develop a quantitative structure-toxicity/activity relationship model for predicting the toxicity pIGC(50) of chemical compounds towards Tetrahymena pyriformis. The optimal model achieved low root mean square errors on both the training and test sets, indicating its higher accuracy compared to other existing models. This study confirms the feasibility of using the random forest algorithm for predicting chemical toxicity towards Tetrahymena pyriformis.