ArticleToxicology2017
Predicting drug-induced liver injury: The importance of data curation.
Article in Toxicology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 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
26 citing papers in PubMed, 88 citations in OpenAlex.
- Machine Learning-Based Models to Predict Drug-Induced Liver Injury (DILI) to Assist Medicinal Chemistry.Journal of medicinal chemistry · 2026Review
- A Multimodal deep learning method for predicting drug-induced liver injury using structural and sequential molecular representations.Journal of computer-aided molecular design · 2026Article
- Predicting Liver-Related In Vitro Endpoints with Machine Learning to Support Early Detection of Drug-Induced Liver Injury.Chemical research in toxicology · 2025Article
- The application of chemical similarity measures in an unconventional modeling framework c-RASAR along with dimensionality reduction techniques to a representative hepatotoxicity dataset.Scientific reports · 2024Article
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis.Chemical research in toxicology · 2023Article
- Chemistry-Based Modeling on Phenotype-Based Drug-Induced Liver Injury Annotation: From Public to Proprietary Data.Chemical research in toxicology · 2023Article
- Inhibition of canalicular and sinusoidal taurocholate efflux by cholestatic drugs in human hepatoma HepaRG cells.Biopharmaceutics & drug disposition · 2022Article
- Machine Learning Models for Predicting Liver Toxicity.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Predictive Model for Drug-Induced Liver Injury Using Deep Neural Networks Based on Substructure Space.Molecules (Basel, Switzerland) · 2021Article
- Article
- Prediction of Drug-Induced Liver Toxicity Using SVM and Optimal Descriptor Sets.International journal of molecular sciences · 2021Article
- CombiningChemical research in toxicology · 2021Article
- Prediction and mechanistic analysis of drug-induced liver injury (DILI) based on chemical structure.Biology direct · 2021Article
- The Promise of AI for DILI Prediction.Frontiers in artificial intelligence · 2021Review
- Predicting Drug-Induced Liver Injury Using Convolutional Neural Network and Molecular Fingerprint-Embedded Features.ACS omega · 2020Article
- Comparing Machine Learning Algorithms for Predicting Drug-Induced Liver Injury (DILI).Molecular pharmaceutics · 2020Article
- Hepatotoxicity Modeling Using Counter-Propagation Artificial Neural Networks: Handling an Imbalanced Classification Problem.Molecules (Basel, Switzerland) · 2020Article
- Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches.Biotechnology and bioprocess engineering : BBE · 2020Review
- A Summary Report of FSCJ Workshop "Future Challenges and Opportunities in Developing Methodologies for Improved Human Risk Assessments".Food safety (Tokyo, Japan) · 2019Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Drug-induced liver injury (DILI) is a major issue for both patients and pharmaceutical industry due to insufficient means of prevention/prediction. In the current work we present a 2-class classification model for DILI, generated with Random Forest and 2D molecular descriptors on a dataset of 966 compounds. In addition, predicted transporter inhibition profiles were also included into the models. The initially compiled dataset of 1773 compounds was reduced via a 2-step approach to 966 compounds, resulting in a significant increase (p-value<0.05) in model performance. The models have been validated via 10-fold cross-validation and against three external test sets of 921, 341 and 96 compounds, respectively. The final model showed an accuracy of 64% (AUC 68%) for 10-fold cross-validation (average of 50 iterations) and comparable values for two test sets (AUC 59%, 71% and 66%, respectively). In the study we also examined whether the predictions of our in-house transporter inhibition models for BSEP, BCRP, P-glycoprotein, and OATP1B1 and 1B3 contributed in improvement of the DILI mode. Finally, the model was implemented with open-source 2D RDKit descriptors in order to be provided to the community as a Python script.
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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.