Large Dataset-Based Regression Model of Chemical Toxicity to Vibrio fischeri

Arch Environ Contam Toxicol. 2023 Jul 5. doi: 10.1007/s00244-023-01010-4. Online ahead of print.ABSTRACTFor the first time, a global regression quantitative structure-toxicity/activity relationship (QSTR/QSAR) model was developed for the toxicity of a large data set including 1236 chemicals towards Vibrio fischeri, by using random forest (RF) regression algorithm. The optimal RF model with RF parameters of mtry = 3, ntree = 150 and nodesize = 5 was based on 13 molecular descriptors. It can achieve accurate prediction for the toxicity of 99.1% of 1236 chemicals, and yield coefficients of determination R2 of 0.893 for 930 log(Mw/IBC50) in the training set, 0.723 for 306 log(Mw/IBC50) in the test se, and 0.865 for 1236 toxicity log(Mw/IBC50) in the total set. The optimal RF global model proposed in this work is comparable to other published local QSTR models on small datasets of the toxicity to Vibrio fischeri.PMID:37407875 | DOI:10.1007/s00244-023-01010-4

Source: Archives of Environmental Contamination and Toxicology - July 5, 2023 Category: Environmental Health Authors: Xinliang Yu Minghui He Limin Su Source Type: research