ArticleToxicological sciences : an official journal of the Society of Toxicology2018
Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility.
Article in Toxicological sciences : an official journal of the Society of Toxicology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 103 papers, 1 of them a synthesis that pooled it.
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Who cites it
103 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on the Hazard Assessment of Amorphous Silica Based on the Literature From 2013 to 2018.Frontiers in public health · 2022Pooled it
- Causality analysis of toxicological mechanisms in networked systems such as adverse outcome pathway networks.Archives of toxicology · 2026Review
- Artificial intelligence in toxicology: current advances, challenges and future directions.Archives of toxicology · 2026Review
- PPAOT predictor: ARKA-RASTR strategy-driven rodent acute oral toxicity prediction for pyrazole and pyrrolidine scaffolds-based chemicals with intelligent mechanistic interpretation.Archives of toxicology · 2026Article
- Repurposing of Clomiphene Citrate and Its Antileishmanial and Antifungal Activities:Biomolecules · 2026Article
- Predicting Aquatic Species Sensitivity Distributions Using Machine Learning in a Regulatory Setting.Chemical research in toxicology · 2026Article
- From structure to strategy: chemometric modeling for the prediction of terminal half-life of pharmaceuticals and its role in future therapeutics.Molecular diversity · 2026Article
- Integrating Multiview Information for Enhanced Deep Learning-Based Acute Dermal Toxicity Prediction.Journal of chemical information and modeling · 2026Article
- AI redefine untargeted metabolomics: estimating chemical amounts for a Human Exposome Project.Frontiers in public health · 2026Review
- Towards the One Exposome: harnessing artificial intelligence and digital health technologies across One Health toxicology, new approach methodologies, and environmental public health.Frontiers in public health · 2026Review
- All things are active … new approach methods for excipient safety.Frontiers in pharmacology · 2026Article
- Navigating the AI Frontier in Toxicology: Trends, Trust, and Transformation.Current environmental health reports · 2025Review
- New approach methodologies gain momentum, providing human-relevant alternatives to animal models.Lab animal · 2025Article
- ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment.Nature communications · 2025Article
- Article
- AI: the Apollo guidance computer of the Exposome moonshot.Frontiers in artificial intelligence · 2025Article
- Molecular Similarity in Predictive Toxicology with a Focus on the q-RASAR Technique.Methods in molecular biology (Clifton, N.J.) · 2025Article
- BioBricks.ai: a versioned data registry for life sciences data assets.Frontiers in artificial intelligence · 2025Article
- AI, agentic models and lab automation for scientific discovery - the beginning of scAInce.Frontiers in artificial intelligence · 2025Review
- Mechanistic read-across comes of age: a comparative appraisal of EFSA 2025 guidance, ECHA's RAAF, and good read-across practice.Frontiers in toxicology · 2025Review
43 more citing papers are in PubMed but not listed here.
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Abstract
Earlier we created a chemical hazard database via natural language processing of dossiers submitted to the European Chemical Agency with approximately 10 000 chemicals. We identified repeat OECD guideline tests to establish reproducibility of acute oral and dermal toxicity, eye and skin irritation, mutagenicity and skin sensitization. Based on 350-700+ chemicals each, the probability that an OECD guideline animal test would output the same result in a repeat test was 78%-96% (sensitivity 50%-87%). An expanded database with more than 866 000 chemical properties/hazards was used as training data and to model health hazards and chemical properties. The constructed models automate and extend the read-across method of chemical classification. The novel models called RASARs (read-across structure activity relationship) use binary fingerprints and Jaccard distance to define chemical similarity. A large chemical similarity adjacency matrix is constructed from this similarity metric and is used to derive feature vectors for supervised learning. We show results on 9 health hazards from 2 kinds of RASARs-"Simple" and "Data Fusion". The "Simple" RASAR seeks to duplicate the traditional read-across method, predicting hazard from chemical analogs with known hazard data. The "Data Fusion" RASAR extends this concept by creating large feature vectors from all available property data rather than only the modeled hazard. Simple RASAR models tested in cross-validation achieve 70%-80% balanced accuracies with constraints on tested compounds. Cross validation of data fusion RASARs show balanced accuracies in the 80%-95% range across 9 health hazards with no constraints on tested compounds.
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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.