Prediction of pesticide acute toxicity using two-dimensional chemical descriptors and target species classification
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T. M. Martina*, C. R. Lilavoisb & M. G. Barronba US Environmental Protection Agency, Office of Research and Development, Sustainable Technology Division, Cincinnati, USAb US Environmental Protection Agency, Office of Research and Development Gulf Ecology Division, Gulf Breeze, USA* Corresponding author. Email: martin.todd@epa.govAbstractPrevious modelling of the median lethal dose (oral rat LD50) has indicated that local class-based models yield better correlations than global models. We evaluated the hypothesis that dividing the dataset by pesticidal mechanisms would improve prediction accuracy. A linear discriminant analysis (LDA) based-approach was utilized to assign indicators such as the pesticide target species, mode of action, or target species - mode of action combination. LDA models were able to predict these indicators with about 87% accuracy. Toxicity is predicted utilizing the QSAR model fit to chemicals with that indicator. Toxicity was also predicted using a global hierarchical clustering (HC) approach which divides data set into clusters based on molecular similarity. At a comparable prediction coverage (~94%), the global HC method yielded slightly higher prediction accuracy (r2 = 0.50) than the LDA method (r2 ~ 0.47). A single model fit to the entire training set yielded the poorest results (r2 = 0.38), indicating that there is an advantage to clustering the dataset to predict acute toxicity. Finally, this study shows that whilst dividing the training set into subsets (i.e. clusters) improves prediction accuracy, it may not matter which method (expert based or purely machine learning) is used to divide the dataset into subsets.
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Martin et al. used global hierarchical clustering to predict acute toxicity of pesticides and obtained better results than linear regression.
“Martin et al. used the global hierarchical clustering method to predict acute toxicity of pesticides and obtained better results than linear regression [125].”
From Machine Learning Based Toxicity Prediction: From Chemical Structural Description to Transcriptome Analysis (2018) · cites this paper as [125]
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- Authors
- Martin, T. M., Lilavois, C. R., & Barron, M. G.
- Affiliations
- Environmental Protection Agency (US)
- Corresponding author
- Martin, T. M.
- Venue
- SAR and QSAR in environmental research
- Publisher
- Informa UK Limited
- Published
- 2017-06-03
- Volume
- 28
- Issue
- 6
- Pages
- 525-539
- ISSN
- 1026-776X (ISSN-L)
- 1062-936X
- 1029-046X
- PMC
- PMC5796665
- OpenAlex
- W2735218314
- Cited by
- 34
- References
- 16
- Citations by year
- 2026: 5 · 2024: 4 · 2023: 4 · 2022: 4 · 2021: 5 · 2020: 5 · 2019: 2 · 2018: 5
- FWCI
- 2.46
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- SAR and QSAR in environmental research (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Environmental Protection Agency
- U.S. National Library of Medicine
- 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
- Life Sciences › Agricultural and Biological Sciences › Food Science › Agricultural safety and regulations
- Keywords
- median lethal dose, QSAR, mechanism of action, linear discriminant analysis, hierarchical clustering, data partitioning, molecular similarity
- MeSH terms
- Animals, Fungi, Insecta, Lethal Dose 50, Pesticides, Plants, Rodentia, Discriminant Analysis, Quantitative Structure-Activity Relationship
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