ArticleNature communications2025
ToxACoL: an endpoint-aware and task-focused compound representation learning paradigm for acute toxicity assessment.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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
6 citing papers in PubMed.
- Baicalin attenuates angiotensin II-induced vascular endothelial injury via targeted activation of the Nrf2 signaling pathway: Integrated computational and in vitro evidence.Molecular diversity · 2026Article
- Essentiality-driven prediction of anticancer drug responses in preclinical and clinical contexts.iScience · 2026Article
- Artificial Intelligence Across the Drug Development Lifecycle.Medical sciences (Basel, Switzerland) · 2026Review
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- Beyond animal testing: An integrated framework for modern chemical hazard identification and risk assessment.Environmental science and ecotechnology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multi-species acute toxicity assessment forms the basis for chemical classification, labelling and risk management. Existing deep learning methods struggle with diverse experimental conditions, imbalanced data, and scarce target data, hindering their ability to reveal endpoint associations and accurately predict data-scarce endpoints. Here we propose a machine learning paradigm, Adjoint Correlation Learning, for multi-condition acute toxicity assessment (ToxACoL) to address these challenges. ToxACoL models endpoint associations via graph topology and achieves knowledge transfer via graph convolution. The adjoint correlation mechanism encodes compounds and endpoints synchronously, yielding endpoint-aware and task-focused representations. Comprehensive analyses demonstrate that ToxACoL yields 43%-87% improvements for data-scarce human endpoints, while reducing training data by 70% to 80%. Visualization of the learned top-level representation interprets structural alert mechanisms. Filled-in toxicity values highlight potential for extrapolating animal results to humans. Finally, we deploy ToxACoL as a free web platform for rapid prediction of multi-condition acute toxicities.
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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.