ArticleNature communications2023
A generative adversarial network model alternative to animal studies for clinical pathology assessment.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
What it found
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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
30 citing papers in PubMed, 1 synthesis or guideline pooled it, 47 citations in OpenAlex.
- Exploring the potential of computer simulation models in drug testing and biomedical research: a systematic review.Frontiers in pharmacology · 2025Pooled it
- ToxCompl completion of the DrugMatrix toxicogenomics database: an integrated resource for hypothesis generation.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Explainable data generation from observed small samples by matrix tri-factorization.Nature communications · 2026Article
- GanCtrl: a generative AI approach to derive study-aligned synthetic controls for reducing concurrent control animal use.Toxicological sciences : an official journal of the Society of Toxicology · 2026Article
- Predicting Aquatic Species Sensitivity Distributions Using Machine Learning in a Regulatory Setting.Chemical research in toxicology · 2026Article
- Animal models of spinal cord injury in neuro-urological research.Nature reviews. Urology · 2026Review
- GANomics: bridging legacy and modern transcriptomic platforms for clinical applications.NPJ genomic medicine · 2026Article
- Building trust in the integration of artificial intelligence into chemical risk assessment: findings from the 2024 ECETOC workshop.Archives of toxicology · 2026Article
- Sharing approaches in predictive genomics across animals, plants and humans.Nature genetics · 2026Review
- Big Data for Drug Discovery: Some Historical Landscape, Considerations, and Applications for a Medicinal Chemist.ACS omega · 2026Review
- Applications of AI/ML in accelerating the development of pulmonary drug delivery system.Acta pharmaceutica Sinica. B · 2026Review
- Article
- Evolution of artificial intelligence and machine learning in DILI toxicogenomics: from descriptive profiling to mechanistic insights.Frontiers in pharmacology · 2026Review
- Navigating the AI Frontier in Toxicology: Trends, Trust, and Transformation.Current environmental health reports · 2025Review
- Beyond QSARs: Quantitative Knowledge-Activity Relationships (QKARs) for enhanced drug toxicity prediction.Toxicological sciences : an official journal of the Society of Toxicology · 2025Article
- Article
- Alternative Models for Anticancer Drug Discovery From Natural Products Using Binary Tumor-Microenvironment-on-a-Chip.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- VitroBert: modeling DILI by pretraining BERT on in vitro data.Journal of cheminformatics · 2025Article
- Modernizing Preclinical Drug Development: The Role of New Approach Methodologies.ACS pharmacology & translational science · 2025Review
- Advances in medical devices using nanomaterials and nanotechnology: Innovation and regulatory science.Bioactive materials · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Animal studies are unavoidable in evaluating chemical and drug safety. Generative Adversarial Networks (GANs) can generate synthetic animal data by learning from the legacy animal study results, thus may serve as an alternative approach to assess untested chemicals. AnimalGAN, a GAN method to simulate 38 rat clinical pathology measures, was developed with significant robustness even for the drugs that vary significantly from these used during training, both in terms of chemical structure, drug class, and the year of FDA approval. AnimalGAN showed comparable results in hepatotoxicity assessment as using the real animal data and outperformed 12 conventional quantitative structure-activity relationship approaches. Using AnimalGAN, a virtual experiment of 100,000 rats ranked hepatotoxicity of three structurally similar drugs in a similar trend that has been observed in human population. AnimalGAN represented a significant step with artificial intelligence towards the global effort in replacement, reduction, and refinement (3Rs) of animal use.
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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.