ArticleMethods in molecular biology (Clifton, N.J.)2022
Machine Learning Models for Predicting Liver Toxicity.
Article in Methods in molecular biology (Clifton, N.J.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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
8 citing papers in PubMed, 16 citations in OpenAlex.
- Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation.Journal of cheminformatics · 2025Article
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- Real-world data-driven early warning system for risk-stratified liver injury in hospitalized COVID-19 patients-Machine learning models for clinical decision support.Frontiers in public health · 2025Article
- Unlocking the potential of AI: Machine learning and deep learning models for predicting carcinogenicity of chemicals.Journal of environmental science and health. Part C, Toxicology and carcinogenesis · 2025Review
- A refined set of RxNorm drug names for enhancing unstructured data analysis in drug safety surveillance.Experimental biology and medicine (Maywood, N.J.) · 2025Article
- Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.Biomolecules · 2024Review
- BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.Frontiers in public health · 2024Article
- Development and validation of an ensemble machine-learning model for predicting early mortality among patients with bone metastases of hepatocellular carcinoma.Frontiers in oncology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Liver toxicity is a major adverse drug reaction that accounts for drug failure in clinical trials and withdrawal from the market. Therefore, predicting potential liver toxicity at an early stage in drug discovery is crucial to reduce costs and the potential for drug failure. However, current in vivo animal toxicity testing is very expensive and time consuming. As an alternative approach, various machine learning models have been developed to predict potential liver toxicity in humans. This chapter reviews current advances in the development and application of machine learning models for prediction of potential liver toxicity in humans and discusses possible improvements to liver toxicity prediction.
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What OpenQuestion 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 OpenQuestion graph.