1. Large Dataset-Based Regression Model of Chemical Toxicity to Vibrio fischeri.
- Author
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Yu, Xinliang, He, Minghui, and Su, Limin
- Subjects
VIBRIO fischeri ,CHEMICAL testing ,CHEMICAL models ,REGRESSION analysis ,RANDOM forest algorithms - Abstract
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 R
2 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. [ABSTRACT FROM AUTHOR]- Published
- 2023
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