ArticleMolecular diversity2019
Classification of thyroid hormone receptor agonists and antagonists using statistical learning approaches.
Article in Molecular diversity, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 12 citations in OpenAlex.
- A Machine Learning-Guided Approach for Identifying Potential HCAR1 Antagonists in Lactate-Driven Cancers.ACS omega · 2026Article
- Machine Learning and Large Language Models for Modeling Complex Toxicity Pathways and Predicting Steroidogenesis.Environmental science & technology · 2025Article
- Identification of estrogen receptor agonists among hydroxylated polychlorinated biphenyls using classification-based quantitative structure-activity relationship models.Current research in toxicology · 2024Article
- Review ofFrontiers in endocrinology · 2022Review
- Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In silico models are presented for modeling and predicting thyroid hormone receptor (TR) agonists and antagonists. A data set consisting of 258 compounds is used in the present work. The C4.5, random forest (RF) and support vector machine (SVM) statistical methods were used for evaluation. The performance of the quantitative structure-activity relationships was further validated with fivefold cross-validation and an independent external test set. The C4.5 model is slightly weak, and the prediction accuracies of the agonists and antagonists are 93.2 and 57.8% for cross-validation, respectively, averaging 83.1% of correctly classified compounds in the test set. The RF model possesses an average prediction accuracy of 84.0 and 87.1% for the cross-validation and external validation, respectively. Furthermore, the overall prediction accuracy and the external prediction accuracy are 96.6 and 97.2%, respectively, for the SVM model. The results would validate the reliability of the derived models, further demonstrating that RF and SVM models are useful tools capable of classifying TR-binding ligands as agonists or antagonists.
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Registered trials
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