Random forest algorithm-based accurate prediction of chemical toxicity to Tetrahymena pyriformis. (October 2022)

• Record Type: Journal Article
• Authors: Fang, Zhengjun; Yu, Xinliang; Zeng, Qun
• Is Part Of: Toxicology. Volume 480(2022)
• Publication Date: 2022-10
• Subjects: Molecular descriptor -- QSTR -- QSAR -- Random forest -- Tetrahymena pyriformis -- Toxicity; Toxicology -- Periodicals; Chemicals -- Physiological effect -- Periodicals; 615.9005
• DOI: 10.1016/j.tox.2022.153325
• Languages: English
• ISSNs: 0300-483X

Abstract

Abstract: 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 pIGC50 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 pIGC50 towards Tetrahymena pyriformis has been verified.