ArticleInternational journal of molecular sciences2019
An In Silico Model for Predicting Drug-Induced Hepatotoxicity.
Article in International journal of molecular sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
46 citing papers in PubMed.
- Context-Aware Multilevel Classification of Semantic Relations in Drug-Adverse Drug Reaction (ADR) Networks-Predicting Drug-Induced Liver Injury (DILI) as a Case Study.Chemical research in toxicology · 2026Article
- Machine Learning-Based Models to Predict Drug-Induced Liver Injury (DILI) to Assist Medicinal Chemistry.Journal of medicinal chemistry · 2026Review
- Comparative evaluation and selection of optimal QSAR-based machine learning model for liver toxicity prediction.Molecular diversity · 2026Article
- HP-MoleQ: An Effective Predictive Model for High-Throughput Screening of Food-Derived Hepatoprotective Compounds.Interdisciplinary sciences, computational life sciences · 2026Article
- BiGraph-DTA: Predicting drug-target interactions of hepatoprotective agents with graph convolutional networks.Quantitative biology (Beijing, China) · 2026Article
- Comprehensive Review on the Toxicity of Five Main AQ Constituents from Rhubarb: Mechanisms, Challenges and Future Perspectives.Drug design, development and therapy · 2026Review
- Evaluation of New Coumarins for Anti-Cancer Activity in HL-60 Cell Line Supported by Molecular Docking, MD Simulation, and Binding Free Energy Calculations.OncoTargets and therapy · 2026Article
- A scoping review of artificial intelligence applications in clinical trial risk assessment.NPJ digital medicine · 2025Article
- Advancing hepatotoxicity assessment: current advances and future directions.Toxicological research · 2025Review
- Mechanisms of drug induced liver injury.Cellular and molecular life sciences : CMLS · 2025Review
- HepatoToxicity Portal (HTP): an integrated database of drug-induced hepatotoxicity knowledgebase and graph neural network-based prediction model.Journal of cheminformatics · 2025Article
- Article
- 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
- A computational framework to in silico screen for drug-induced hepatocellular toxicity.Toxicological sciences : an official journal of the Society of Toxicology · 2024Article
- Improved Detection of Drug-Induced Liver Injury by Integrating PredictedChemical research in toxicology · 2024Article
- Improved Detection of Drug-Induced Liver Injury by Integrating PredictedbioRxiv : the preprint server for biology · 2024Article
- Enhancing property and activity prediction and interpretation using multiple molecular graph representations with MMGX.Communications chemistry · 2024Article
- A Comparison of Machine Learning Approaches for predicting Hepatotoxicity potential using Chemical Structure and Targeted Transcriptomic Data.Computational toxicology (Amsterdam, Netherlands) · 2024Article
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Therapeutic Potential of the GenusSouth African journal of botany : official journal of the South African Association of Botanists = Suid-Afrikaanse tydskrif vir plantkunde : amptelike tydskrif van die Suid-Afrikaanse Genootskap van Plantkundiges · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
As one of the leading causes of drug failure in clinical trials, drug-induced liver injury (DILI) seriously impeded the development of new drugs. Assessing the DILI risk of drug candidates in advance has been considered as an effective strategy to decrease the rate of attrition in drug discovery. Recently, there have been continuous attempts in the prediction of DILI. However, it indeed remains a huge challenge to predict DILI successfully. There is an urgent need to develop a quantitative structure-activity relationship (QSAR) model for predicting DILI with satisfactory performance. In this work, we reported a high-quality QSAR model for predicting the DILI risk of xenobiotics by incorporating the use of eight effective classifiers and molecular descriptors provided by Marvin. In model development, a large-scale and diverse dataset consisting of 1254 compounds for DILI was built through a comprehensive literature retrieval. The optimal model was attained by an ensemble method, averaging the probabilities from eight classifiers, with accuracy (ACC) of 0.783, sensitivity (SE) of 0.818, specificity (SP) of 0.748, and area under the receiver operating characteristic curve (AUC) of 0.859. For further validation, three external test sets and a large negative dataset were utilized. Consequently, both the internal and external validation indicated that our model outperformed prior studies significantly. Data provided by the current study will also be a valuable source for modeling/data mining in the future.
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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.