ArticleiScience2023
Supervised chemical graph mining improves drug-induced liver injury prediction.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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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
15 citing papers in PubMed, 20 citations in OpenAlex.
- Integrating multi-type features and knowledge graph for graded prediction of drug-induced liver injury in humans.PLoS computational biology · 2026Article
- Perspective on applicability of data-driven machine learning computational new approach methodologies for hazard identification in chemicals risk assessment.Journal of cheminformatics · 2026Review
- BiGraph-DTA: Predicting drug-target interactions of hepatoprotective agents with graph convolutional networks.Quantitative biology (Beijing, China) · 2026Article
- Unified and explainable molecular representation learning for imperfectly annotated data from the hypergraph view.Nature communications · 2025Article
- Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation.Journal of cheminformatics · 2025Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Development of an AI model for DILI-level prediction using liver organoid brightfield images.Communications biology · 2025Article
- Fate-tox: fragment attention transformer for E(3)-equivariant multi-organ toxicity prediction.Journal of cheminformatics · 2025Article
- Machine Learning-Enabled Drug-Induced Toxicity Prediction.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- ChemAP: predicting drug approval with chemical structures before clinical trial phase by leveraging multi-modal embedding space and knowledge distillation.Scientific reports · 2024Article
- Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation.Molecules (Basel, Switzerland) · 2024Article
- Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Identifying chemicals based on receptor binding/bioactivation/mechanistic explanation associated with potential to elicit hepatotoxicity and to support structure activity relationship-based read-across.Current research in toxicology · 2023Article
- Advances in the study of acetaminophen-induced liver injury.Frontiers in pharmacology · 2023Review
- Computational models for predicting liver toxicity in the deep learning era.Frontiers in toxicology · 2023Review
Corrections and comments
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-induced liver injury (DILI) is the main cause of drug failure in clinical trials. The characterization of toxic compounds in terms of chemical structure is important because compounds can be metabolized to toxic substances in the liver. Traditional machine learning approaches have had limited success in predicting DILI, and emerging deep graph neural network (GNN) models are yet powerful enough to predict DILI. In this study, we developed a completely different approach, supervised subgraph mining (SSM), a strategy to mine explicit subgraph features by iteratively updating individual graph transitions to maximize DILI fidelity. Our method outperformed previous methods including state-of-the-art GNN tools in classifying DILI on two different datasets: DILIst and TDC-benchmark. We also combined the subgraph features by using SMARTS-based frequent structural pattern matching and associated them with drugs' ATC code.
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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.