ArticleToxicology and applied pharmacology2022
Prediction of drug-induced liver injury and cardiotoxicity using chemical structure and in vitro assay data.
Article in Toxicology and applied pharmacology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Semantic knowledge improves molecular machine learning for chemical toxicity prediction.iScience · 2026Article
- Expanded Tox21 Biological Assay Panel for the Prediction of Drug-Induced Liver Injury and Cardiotoxicity.Environmental health perspectives · 2026Article
- Perspective on applicability of data-driven machine learning computational new approach methodologies for hazard identification in chemicals risk assessment.Journal of cheminformatics · 2026Review
- Hierarchical Mechanistic Modeling of Complex Toxicity Endpoints from Public Concentration-Response Data.Environmental science & technology · 2026Article
- Review
- Systematic evaluation of Tox21 compounds that target β-adrenergic receptors and their role in cardiotoxicity.Toxicology and applied pharmacology · 2025Article
- A Benchmark Set of Bioactive Molecules for Diversity Analysis of Compound Libraries and Combinatorial Chemical Spaces.Journal of chemical information and modeling · 2025Article
- Improving drug-induced liver injury prediction using graph neural networks with augmented graph features from molecular optimisation.Journal of cheminformatics · 2025Article
- Impact of halogenation on scaffold toxicity assessed using HD-GEM machine learning model.Briefings in bioinformatics · 2025Article
- Leveraging viral genome sequences and machine learning models for identification of potentially selective antiviral agents.Communications chemistry · 2025Article
- Article
- Deep Learning Prediction of Drug-Induced Liver Toxicity by Manifold Embedding of Quantum Information of Drug Molecules.Pharmaceutical research · 2025Article
- QSAR Classification Modeling Using Machine Learning with a Consensus-Based Approach for Multivariate Chemical Hazard End Points.ACS omega · 2024Article
- Prediction of chemical-induced acute toxicity using in vitro assay data and chemical structure.Toxicology and applied pharmacology · 2024Article
- Natural products: A potential immunomodulators against inflammatory-related diseases.Inflammopharmacology · 2024Review
- The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges.Molecular therapy. Nucleic acids · 2024Review
- 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
- Interpretable machine learning in predicting drug-induced liver injury among tuberculosis patients: model development and validation study.BMC medical research methodology · 2024Article
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
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Authors and funding
11 authors.
Funding
Abstract
Drug-induced liver injury (DILI) and cardiotoxicity (DICT) are major adverse effects triggered by many clinically important drugs. To provide an alternative to in vivo toxicity testing, the U.S. Tox21 consortium has screened a collection of ∼10K compounds, including drugs in clinical use, against >70 cell-based assays in a quantitative high-throughput screening (qHTS) format. In this study, we compiled reference compound lists for DILI and DICT and compared the potential of Tox21 assay data with chemical structure information in building prediction models for human in vivo hepatotoxicity and cardiotoxicity. Models were built with four different machine learning algorithms (e.g., Random Forest, Naïve Bayes, eXtreme Gradient Boosting, and Support Vector Machine) and model performance was evaluated by calculating the area under the receiver operating characteristic curve (AUC-ROC). Chemical structure-based models showed reasonable predictive power for DILI (best AUC-ROC = 0.75 ± 0.03) and DICT (best AUC-ROC = 0.83 ± 0.03), while Tox21 assay data alone only showed better than random performance. DILI and DICT prediction models built using a combination of assay data and chemical structure information did not have a positive impact on model performance. The suboptimal predictive performance of the assay data is likely due to insufficient coverage of an adequately predictive number of toxicity mechanisms. The Tox21 consortium is currently expanding coverage of biological response space with additional assays that probe toxicologically important targets and under-represented pathways that may improve the prediction of in vivo toxicity such as DILI and DICT.
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