In silico prediction of chronic toxicity with chemical category approaches
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Chemical chronic toxicity, referring to the toxic effect of a chemical following long-term or repeated sub lethal exposures, is an important toxicological end point in drug design and environmental risk assessment.
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Li et al. used RF, SVM, and k-NN to predict oral LOAEL of rats; k-NN obtained best performance with AUC up to 0.814.
“In 2017, Li et al. used machine learning models, such as RF, SVM, and k-NN to predict the oral LOAEL (lowest observed adverse effect level) of rats. The method k-NN obtained the best performance, yielding AUC values up to 0.814 [128].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [128]
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- Authors
- Li, X., Zhang, Y., Chen, H., Li, H., & Zhao, Y.
- Affiliations
- Beijing Computing Center (CN)
- Beijing Beike Deyuan Bio-Pharm Technology Co.Ltd.
- Hangzhou Tigermed Consulting Co., Ltd. (China) (CN)
- Corresponding author
- Li, Xiao
- Zhao, Yong
- Venue
- RSC Advances
- Publisher
- Royal Society of Chemistry (RSC)
- Published
- 2017
- Volume
- 7
- Issue
- 66
- Pages
- 41330-41338
- ISSN
- 2046-2069 (ISSN-L)
- OpenAlex
- W2749210830
- Cited by
- 33
- References
- 35
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 5 · 2023: 4 · 2022: 6 · 2021: 5 · 2019: 1 · 2018: 2 · 2017: 1
- FWCI
- 0.82
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc
- Locations
- RSC Advances (publishedVersion, cc-by-nc)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Environmental Toxicology and Ecotoxicology
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Keywords
- chronic toxicity, in silico prediction, environmental risk assessment, drug design, chemical toxicity
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