ArticleScientific reports2017
Development of Decision Forest Models for Prediction of Drug-Induced Liver Injury in Humans Using A Large Set of FDA-approved Drugs.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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Who cites it
44 citing papers in PubMed.
- Machine Learning-Based Models to Predict Drug-Induced Liver Injury (DILI) to Assist Medicinal Chemistry.Journal of medicinal chemistry · 2026Review
- DILIConsult: A Multi-Agent Large Language Model Framework for Evaluating Drug-Induced Liver Injury in ICU Settings.Pharmacotherapy · 2026Article
- Artificial intelligence and digital transformation of gastroenterology and hepatology: A critical review of clinical applications and future challenges.World journal of hepatology · 2026Review
- Machine learning prediction of bile salt export pump inhibition with Boruta feature selection in imbalanced data.Frontiers in pharmacology · 2026Article
- Integrating Molecular Dynamics, Molecular Docking, and Machine Learning for Predicting SARS-CoV-2 Papain-like Protease Binders.Molecules (Basel, Switzerland) · 2025Article
- Interpretable machine learning models for predicting the antitumor effects of metal and metal oxide nanomaterials.RSC advances · 2025Article
- HepatoToxicity Portal (HTP): an integrated database of drug-induced hepatotoxicity knowledgebase and graph neural network-based prediction model.Journal of cheminformatics · 2025Article
- Deep Learning Prediction of Drug-Induced Liver Toxicity by Manifold Embedding of Quantum Information of Drug Molecules.Pharmaceutical research · 2025Article
- Developing predictive models for µ opioid receptor binding using machine learning and deep learning techniques.Experimental biology and medicine (Maywood, N.J.) · 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
- Machine Learning to Predict Drug-Induced Liver Injury and Its Validation on Failed Drug Candidates in Development.Toxics · 2024Article
- Advancing Adverse Drug Reaction Prediction with Deep Chemical Language Model for Drug Safety Evaluation.International journal of molecular sciences · 2024Article
- Comprehensive hepatotoxicity prediction: ensemble model integrating machine learning and deep learning.Frontiers in pharmacology · 2024Article
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Estimating drug-induced liver injury risk by in vitro molecular initiation response and pharmacokinetic parameters for during early drug development.Toxicology research · 2023Article
- Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials.Nanomaterials (Basel, Switzerland) · 2022Article
- Machine learning models for rat multigeneration reproductive toxicity prediction.Frontiers in pharmacology · 2022Article
- Machine Learning Models for Predicting Liver Toxicity.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Preclinical models of idiosyncratic drug-induced liver injury (iDILI): Moving towards prediction.Acta pharmaceutica Sinica. B · 2021Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Drug-induced liver injury (DILI) presents a significant challenge to drug development and regulatory science. The FDA's Liver Toxicity Knowledge Base (LTKB) evaluated >1000 drugs for their likelihood of causing DILI in humans, of which >700 drugs were classified into three categories (most-DILI, less-DILI, and no-DILI). Based on this dataset, we developed and compared 2-class and 3-class DILI prediction models using the machine learning algorithm of Decision Forest (DF) with Mold2 structural descriptors. The models were evaluated through 1000 iterations of 5-fold cross-validations, 1000 bootstrapping validations and 1000 permutation tests (that assessed the chance correlation). Furthermore, prediction confidence analysis was conducted, which provides an additional parameter for proper interpretation of prediction results. We revealed that the 3-class model not only had a higher resolution to estimate DILI risk but also showed an improved capability to differentiate most-DILI drugs from no-DILI drugs in comparison with the 2-class DILI model. We demonstrated the utility of the models for drug ingredients with warnings very recently issued by the FDA. Moreover, we identified informative molecular features important for assessing DILI risk. Our results suggested that the 3-class model presents a better option than the binary model (which most publications are focused on) for drug safety evaluation.
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