ReviewFrontiers in toxicology2023
Computational models for predicting liver toxicity in the deep learning era.
Review in Frontiers in toxicology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 19 citations in OpenAlex.
- Introduction to the 3RsC's cross-platform microphysiological systems evaluation for drug-induced liver injury in collaboration with FDA-CDER.Regulatory toxicology and pharmacology : RTP · 2026Article
- Comparative evaluation and selection of optimal QSAR-based machine learning model for liver toxicity prediction.Molecular diversity · 2026Article
- Machine learning prediction of bile salt export pump inhibition with Boruta feature selection in imbalanced data.Frontiers in pharmacology · 2026Article
- Multiscale modeling of drug-induced liver injury from organ to lobule.NPJ digital medicine · 2025Article
- Review
- Nanomedicines Targeting Metabolic Pathways in the Tumor Microenvironment: Future Perspectives and the Role of AI.Metabolites · 2025Review
- Article
- A virtual scalable model of the Hepatic Lobule for acetaminophen hepatotoxicity prediction.NPJ digital medicine · 2024Article
- Recent Advances in Omics, Computational Models, and Advanced Screening Methods for Drug Safety and Efficacy.Toxics · 2024Review
- Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation.International journal of molecular sciences · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-induced liver injury (DILI) is a severe adverse reaction caused by drugs and may result in acute liver failure and even death. Many efforts have centered on mitigating risks associated with potential DILI in humans. Among these, quantitative structure-activity relationship (QSAR) was proven to be a valuable tool for early-stage hepatotoxicity screening. Its advantages include no requirement for physical substances and rapid delivery of results. Deep learning (DL) made rapid advancements recently and has been used for developing QSAR models. This review discusses the use of DL in predicting DILI, focusing on the development of QSAR models employing extensive chemical structure datasets alongside their corresponding DILI outcomes. We undertake a comprehensive evaluation of various DL methods, comparing with those of traditional machine learning (ML) approaches, and explore the strengths and limitations of DL techniques regarding their interpretability, scalability, and generalization. Overall, our review underscores the potential of DL methodologies to enhance DILI prediction and provides insights into future avenues for developing predictive models to mitigate DILI risk in humans.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.