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

For 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. © 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Citation

Xinliang Yu, Minghui He, Limin Su. Large Dataset-Based Regression Model of Chemical Toxicity to Vibrio fischeri. Archives of environmental contamination and toxicology. 2023 Jul;85(1):46-54

Mesh Tags

PMID: 37407875

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