vToxiNet: a biologically constrained deep learning framework for interpretable prediction of drug-induced hepatotoxicity.

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Bibliographic Details
Title: vToxiNet: a biologically constrained deep learning framework for interpretable prediction of drug-induced hepatotoxicity.
Authors: Jia X; Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, Louisiana, USA., Wang T; Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, Louisiana, USA., Russo DP; Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey, USA., Aleksunes LM; Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey, USA., Xiao S; Department of Pharmacology and Toxicology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey, USA.; Environmental and Occupational Health Sciences Institute (EOHSI), Rutgers University, Piscataway, New Jersey, USA.; Center for Environmental Exposures and Disease (CEED), Rutgers University, Piscataway, New Jersey, USA., Zhu H; Center for Biomedical Informatics and Genomics, School of Medicine, Tulane University, New Orleans, Louisiana, USA.; Department of Chemistry and Biochemistry, Rowan University, Glassboro, New Jersey, USA.
Source: BioRxiv : the preprint server for biology [bioRxiv] 2026 Mar 02. Date of Electronic Publication: 2026 Mar 02.
Publication Type: Journal Article; Preprint
Journal Info: Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet ISSN: 2692-8205 (Electronic) Linking ISSN: 26928205 NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2692-8205
DOI:10.64898/2026.02.26.708259