ArticleEnvironmental science & technology2025
Improving the Ecotoxicological Hazard Assessment of Chemicals by Pairwise Learning.
Article in Environmental science & technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Planetary Boundary for Novel Entities: Time for a Reboot.Environmental science & technology · 2026Review
- Predicting Aquatic Species Sensitivity Distributions Using Machine Learning in a Regulatory Setting.Chemical research in toxicology · 2026Article
- Advancing the Discovery of Emerging Contaminants: A Leap in Technology and Data.Environmental science & technology · 2026Review
- Challenges and Initiatives to Operationalize the Planetary Boundaries for Novel Entities.Environmental science & technology · 2026Article
- Framework for Function-Based Pesticide Substitution.Environmental science & technology · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study demonstrates how machine learning techniques can bridge data gaps in the ecotoxicological hazard assessment of chemical pollutants and illustrates how the results can be used in practice. The innovation herein consists of the prediction of the sensitivity of all species that were tested for at least one chemical for all chemicals based on all available data. As proof of concept, pairwise learning was applied to 3295 × 1267 (chemical,species) pairs of Observed LC50 data, where only 0.5% of the pairs have experimental data. This yielded more than four million Predicted LC50s for separate exposure durations. These were used to create (1) a novel Hazard Heatmap of Predicted LC50s, (2) Species Sensitivity Distributions (SSD) for all chemicals based on 1267 species each, as well as (3) for taxonomic groups separately, and (4) newly defined Chemical Hazard Distributions (CHD) for all species based on 3295 chemicals each. Validation results and graphical examples illustrate the utility of the results and highlight species and compound selection biases in the input data. The results are broadly applicable, ranging from
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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.