Random forest algorithm-based accurate prediction of rat acute oral toxicity

Predicting acute oral toxicity LD50 of chemicals in rats is a challenge since many factors affect toxicity data. In this paper, 40 descriptors were successfully used to develop a quantitative structure-activity relationship model for 8448 rat acute oral toxicity logLD50 by applying the random forest (RF) algorithm. To develop the optimal RF model, a training set (5914 chemicals) was used to establish models, a validation set (1267 chemicals) used to tune RF parameters and a test set (1267 chemicals) used to assess the performance of RF models. It yielded correlation coefficients R of 0.9695 and rms errors (log unit) of 0.3171 for the training set, R = 0.8322 and rms = 0.2889 for the validation set and R = 0.8335 and rms = 0.3060 for the test set. More than 99% of rat acute oral toxicity logLD50 in the dataset can be accurately predicted, although the dataset is large.