ArticleScience advances2024
Transformers enable accurate prediction of acute and chronic chemical toxicity in aquatic organisms.
Article in Science advances, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed.
- Leveraging Artificial Intelligence in Allergy, Asthma, and Immunology With Environmental Exposures.Allergy · 2026Review
- Planetary Boundary for Novel Entities: Time for a Reboot.Environmental science & technology · 2026Review
- Toxicokinetic-Informed Evidential Learning for Applicability-Domain-Aware QSAR/QSPR Prediction of Environmental Contaminant Toxicity.Molecules (Basel, Switzerland) · 2026Article
- Advancing the Discovery of Emerging Contaminants: A Leap in Technology and Data.Environmental science & technology · 2026Review
- Ecotoxicology in the Context of Biodiversity Loss: Lessons from Seven Decades of Chemical Impacts and Paths Forward.Environmental science & technology · 2026Review
- Multi-Task Cascade Forest Framework for Predicting Acute Toxicity across Species.Research (Washington, D.C.) · 2026Article
- Metasurface Vision Transformer: A Generic AI Model for Metasurface Inverse Design.Nanophotonics (Berlin, Germany) · 2026Article
- Review
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Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.