ArticleScientific data2023
A benchmark dataset for machine learning in ecotoxicology.
Article in Scientific data, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- Review
- Predicting Aquatic Species Sensitivity Distributions Using Machine Learning in a Regulatory Setting.Chemical research in toxicology · 2026Article
- Article
- Enhanced Environmental PFAS Characterization Using a Virtual High-Resolution Mass Spectral Library Generated by Transfer Learning-Based Neural Network.Environmental science & technology · 2026Article
- Persistence Assessment of Chemicals: Trajectories toward New Approach Methodologies (P-NAMs).Environmental science & technology · 2026Review
- Safe-by-redesign guidance for toxic industrial chemicals using explainable artificial intelligence: Introducing the DETOX-QSAR model.Scientific reports · 2026Article
- High-Resolution Colony Images of Clinically Isolated Bacteria for Automated Detection and Deep Learning.Scientific data · 2026Article
- G.AI.A: An Integrated Machine-Learning Platform for Predicting Bioaccumulation and Ecotoxicity of Pharmaceuticals.Journal of chemical information and modeling · 2026Article
- Review
- A Comparative Evaluation of Machine Learning and Deep Graph Learning for Chemical Ecotoxicological Prediction.ACS omega · 2025Article
- Improving the Ecotoxicological Hazard Assessment of Chemicals by Pairwise Learning.Environmental science & technology · 2025Article
- Risk assessment of industrial chemicals towards salmon species amalgamating QSAR, q-RASAR, and ARKA framework.Toxicology reports · 2025Article
- Prioritization of Unknown LC-HRMS Features Based on Predicted Toxicity Categories.Environmental science & technology · 2025Article
- MLinvitroTox reloaded for high-throughput hazard-based prioritization of high-resolution mass spectrometry data.Journal of cheminformatics · 2025Article
- ApisTox: a new benchmark dataset for the classification of small molecules toxicity on honey bees.Scientific data · 2025Article
- Evaluation of interspecies correlation estimation models to increase taxonomic diversity while reducing reliance on animal testing for chemicals evaluated under the Toxic Substances Control Act.Integrated environmental assessment and management · 2025Article
- Progress, applications, and challenges in high-throughput effect-directed analysis for toxicity driver identification - is it time for HT-EDA?Analytical and bioanalytical chemistry · 2025Review
- Overview of Computational Toxicology Methods Applied in Drug and Green Chemical Discovery.Journal of xenobiotics · 2024Review
- Advancing Maternal Transfer of Organic Pollutants across Reptiles for Conservation and Risk Assessment Purposes.Environmental science & technology · 2024Article
- Uncertainty analysis and updated user guidance for interspecies correlation estimation models and low toxicity compounds.Integrated environmental assessment and management · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of machine learning for predicting ecotoxicological outcomes is promising, but underutilized. The curation of data with informative features requires both expertise in machine learning as well as a strong biological and ecotoxicological background, which we consider a barrier of entry for this kind of research. Additionally, model performances can only be compared across studies when the same dataset, cleaning, and splittings were used. Therefore, we provide ADORE, an extensive and well-described dataset on acute aquatic toxicity in three relevant taxonomic groups (fish, crustaceans, and algae). The core dataset describes ecotoxicological experiments and is expanded with phylogenetic and species-specific data on the species as well as chemical properties and molecular representations. Apart from challenging other researchers to try and achieve the best model performances across the whole dataset, we propose specific relevant challenges on subsets of the data and include datasets and splittings corresponding to each of these challenge as well as in-depth characterization and discussion of train-test splitting approaches.
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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.