Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms
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Abstract
Environmental hazard assessments are reliant on toxicity data that cover multiple organism groups. Generating experimental toxicity data is, however, resource-intensive and time-consuming. Computational methods are fast and cost-efficient alternatives, but the low accuracy and narrow applicability domains have made their adaptation slow. Here, we present a AI-based model for predicting chemical toxicity. The model uses transformers to capture toxicity-specific features directly from the chemical structures and deep neural networks to predict effect concentrations. The model showed high predictive performance for all tested organism groups—algae, aquatic invertebrates and fish—and has, in comparison to commonly used QSAR methods, a larger applicability domain and a considerably lower error. When the model was trained on data with multiple effect concentrations (EC 50 /EC 10 ), the performance was further improved. We conclude that deep learning and transformers have the potential to markedly advance computational prediction of chemical toxicity.
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- Authors
- Gustavsson, M., Käll, S., Svedberg, P., Inda-Diaz, J. S., Molander, S., Coria, J., Backhaus, T., & Kristiansson, E.
- Affiliations
- University of Gothenburg (SE)
- Chalmers University of Technology (SE)
- Department of Mathematical Sciences, Chalmers University of Technology/University of Gothenburg, Gothenburg, Sweden.
- Corresponding author
- Kristiansson, Erik
- ORCID
- Venue
- Science Advances
- Published
- 2024-03-08
- Volume
- 10
- Issue
- 10
- Pages
- eadk6669-eadk6669
- ISSN
- 2375-2548 (ISSN-L)
- PMC
- PMC10917336
- OpenAlex
- W4392529738
- Cited by
- 82
- References
- 8
- Citations by year
- 2026: 38 · 2025: 35 · 2024: 9
- FWCI
- 17.35
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Science Advances (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Chalmers Research (Chalmers University of Technology) (submittedVersion)
- Funders
- Vetenskapsrådet
- Göteborgs Universitet
- Framsenteret
- 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
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- chemical toxicity prediction, transformer models, deep neural network, aquatic organisms, acute toxicity, chronic toxicity, effect concentrations, applicability domain, algae, aquatic invertebrates, fish, environmental hazard assessment
- MeSH terms
- Animals, Electric Power Supplies, Neural Networks, Computer, Aquatic Organisms
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