ArticleBMC bioinformatics2023
PredAOT: a computational framework for prediction of acute oral toxicity based on multiple random forest models.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Machine learning prediction of bile salt export pump inhibition with Boruta feature selection in imbalanced data.Frontiers in pharmacology · 2026Article
- Computational toxicology in drug discovery: applications of artificial intelligence in ADMET and toxicity prediction.Briefings in bioinformatics · 2025Review
- Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.ADMET & DMPK · 2025Review
- Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction.Chemical research in toxicology · 2024Review
- Bioinformatics and machine learning driven key genes screening for hepatocellular carcinoma.Biochemistry and biophysics reports · 2024Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundAcute oral toxicity of drug candidates can lead to drug development failure; thus, predicting the acute oral toxicity of small compounds is important for successful drug development. However, evaluation of the acute oral toxicity of small compounds considered in the early stages of drug discovery is limited because of cost and time. Here, we developed a computational framework, PredAOT, that predicts the acute oral toxicity of small compounds in mice and rats.
methodsPredAOT is based on multiple random forest models for the accurate prediction of acute oral toxicity. A total of 6226 and 6238 compounds evaluated in mice and rats, respectively, were used to train the models.
resultsPredAOT has the advantage of predicting acute oral toxicity in mice and rats simultaneously, and its prediction performance is similar to or better than that of existing tools.
conclusionPredAOT will be a useful tool for the quick and accurate prediction of the acute oral toxicity of small compounds in mice and rats during drug development.
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Registered trials
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