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 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.
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Funding
This work was supported by the Open Project Program of Hunan Provincial Key Laboratory of Environmental Catalysis & Waste Regeneration (Hunan Institute of Engineering) (No. 2018KF11) and the Hunan Provincial Natural Science Foundation (Nos. 2020JJ6013, 2021JJ50111).
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XY provided conceptualization, methodology, software, validation, writing—original draft, writing—review and editing. MH and LS performed data curation and validation.
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Xinliang Yu declares that he has not received any research grants or honoraria from any commercial companies. Minghui He declares that she has not received any research grants or honoraria from any commercial companies. Limin Su declares that she has not received any research grants or honoraria from any commercial companies.
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Yu, X., He, M. & Su, L. Large Dataset-Based Regression Model of Chemical Toxicity to Vibrio fischeri. Arch Environ Contam Toxicol 85, 46–54 (2023). https://doi.org/10.1007/s00244-023-01010-4
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DOI: https://doi.org/10.1007/s00244-023-01010-4