acevToxiNet: An interpretable deep learning framework for hepatotoxicity prediction and mechanism exploration
Hepatotoxicity remains a leading cause of drug attrition and post-marketing withdrawal, resulting from diverse and complex toxicity mechanisms. Traditional in vitro models can only capture a limited subset of toxicity pathways, and animal studies face translational and ethical limitations. Regulatory agencies have therefore promoted new approach methodologies, including human-relevant assays, omics technologies, and computational models to improve predictive toxicology and support evidence-based decision-making.…
Embedding pathway hierarchy into a neural network lets the model predict liver toxicity and attribute predictions to genes and pathways, supporting mechanism-informed chemical prioritization.
The abstract describes performance only as promising and presents the work as support for further development, with no quantitative results or evidence of regulatory or clinical use.
Evidence
- Peer-reviewedJournal of Hazardous Materials2026-10-12
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Truvace Impact Record TRV-2026-2051, v1: “vToxiNet: An interpretable deep learning framework for hepatotoxicity prediction and mechanism exploration.” Truvace, 2026-10-20. /record/TRV-2026-2051 (accessed at citation time). sha256 b926fdd75660b03d…
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