Estimation of acute oral toxicity in rat using local lazy learning
Open accessAbstract
BACKGROUND: Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, LD50, is frequently used as a general indicator of a substance's acute toxicity, and there is a high demand on developing non-animal-based prediction of LD50. Unfortunately, it is difficult to accurately predict compound LD50 using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes. RESULTS: In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop LD50 prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients R(2) of 0.712 on a test set containing 2,896 compounds. CONCLUSION: Encouraged by the promising results, we expect that our consensus LLL model of LD50 would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus.
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Lu et al. used k-NN combined linear regression model to predict acute oral toxicity in rats and achieved an R square value of 0.712, using local chemical structure represented by molecular fingerprints.
“in 2011, Lu et al. used k-NN combined linear regression model to predict acute oral toxicity in rats and achieved a R square value of 0.712, for which they utilized the local chemical structure that was represented by molecular fingerprints [31].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [31]
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- Authors
- Lu, J., Peng, J., Wang, J., Shen, Q., Bi, Y., Gong, L., Zheng, M., Luo, X., Zhu, W., Jiang, H., & Chen, K.
- Affiliations
- Yantai University (CN)
- Chinese Academy of Sciences (CN)
- East China University of Science and Technology (CN)
- Corresponding author
- Zheng, Mingyue
- Luo, Xiaomin
- ORCID
- Venue
- Journal of Cheminformatics
- Published
- 2014-05-16
- Volume
- 6
- Issue
- 1
- Pages
- 26-26
- ISSN
- 1758-2946 (ISSN-L)
- PMC
- PMC4047767
- OpenAlex
- W2160344370
- Cited by
- 42
- References
- 24
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 1 · 2022: 6 · 2021: 3 · 2020: 5 · 2019: 5 · 2018: 6 · 2017: 2 · 2016: 5 · 2015: 3
- FWCI
- 3.44
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Cheminformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (2013ZX09507001, 81001399, 81220108025, 2012ZX09301-001-002, 2012AA020308)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Health Sciences › Veterinary › Small Animals › Animal testing and alternatives
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- acute oral toxicity, k-nearest neighbors, QSAR, applicability domain, consensus model, structure-toxicity relationship, rat toxicity
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