ArticleACS omega2024
QSAR Classification Modeling Using Machine Learning with a Consensus-Based Approach for Multivariate Chemical Hazard End Points.
Article in ACS omega, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- Improving In Silico Cardiac Safety Prediction by Consensus Averaging of Transmural Ventricular Cell Models.Annals of biomedical engineering · 2026Article
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Network toxicology deciphers micro- and nanoplastics-mediated mixture hazard, predictive risk assessment, and regulatory translation.Archives of toxicology · 2026Review
- A Reproducible Hierarchical Virtual Screening Framework Integrating Scaffold-Aware Machine Learning, Ensemble Docking, and Molecular Dynamics: Application to IDO1.Journal of chemical information and modeling · 2026Article
- Ensemble learning-guided discovery of anti-tuberculosis phytochemicals: computational prediction and mechanistic insights.Journal of computer-aided molecular design · 2026Article
- Interpretable QSAR models for acute oral toxicity via tuned XGBoost and hybrid data sampling techniques.Scientific reports · 2026Article
- Safe-by-redesign guidance for toxic industrial chemicals using explainable artificial intelligence: Introducing the DETOX-QSAR model.Scientific reports · 2026Article
- Integrating Multiview Information for Enhanced Deep Learning-Based Acute Dermal Toxicity Prediction.Journal of chemical information and modeling · 2026Article
- Advancements in toxicological risk assessment: integrating Ferguson's principle, computational models, and drug safety guidelines, a comprehensive framework for improving risk assessment and resource management in toxicology.Toxicology research · 2025Article
- Machine Learning-Driven Consensus Modeling for Activity Ranking and Chemical Landscape Analysis of HIV-1 Inhibitors.Pharmaceuticals (Basel, Switzerland) · 2025Article
- Article
- SbD4Skin by EosCloud: Integrating multi-view molecular representation for predicting skin sensitization, irritation, and acute dermal toxicity.Computational and structural biotechnology journal · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study introduces an innovative computational approach using hybrid machine learning models to predict toxicity across eight critical end points: cardiac toxicity, inhalation toxicity, dermal toxicity, oral toxicity, skin irritation, skin sensitization, eye irritation, and respiratory irritation. Leveraging advanced cheminformatics tools, we extracted relevant features from curated data sets, incorporating a range of descriptors such as Morgan circular fingerprints, MACCS keys, Mordred calculation descriptors, and physicochemical properties. The consensus model was developed by selecting the best-performing classifier-Random Forest (RF), eXtreme Gradient Boosting (XGBoost), or Support Vector Machines (SVM)-for each descriptor, optimizing predictive accuracy and robustness across the end points. The model obtained strong predictive performance, with area under the curve (AUC) scores ranging from 0.78 to 0.90. This framework offers a reliable, ethical, and effective in silico approach to chemical safety assessment, underscoring the potential of advanced computational methods to support both regulatory and research applications in toxicity prediction.
Identifiers
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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.