ArticleActa pharmacologica Sinica2011
Classification of 5-HT(1A) receptor agonists and antagonists using GA-SVM method.
Article in Acta pharmacologica Sinica, 2011. 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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5 citing papers in PubMed, 14 citations in OpenAlex.
- A Machine Learning-Guided Approach for Identifying Potential HCAR1 Antagonists in Lactate-Driven Cancers.ACS omega · 2026Article
- Dynamic-GLEP: a dynamics-informed deep learning framework for ligand efficacy prediction in representative Class A GPCRs.Briefings in bioinformatics · 2026Article
- GPCR-A17 MAAP: mapping modulators, agonists, and antagonists to predict the next bioactive target.Journal of cheminformatics · 2025Article
- Leveraging Artificial Intelligence in GPCR Activation Studies: Computational Prediction Methods as Key Drivers of Knowledge.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Curated Database and Preliminary AutoML QSAR Model for 5-HT1A Receptor.Pharmaceutics · 2021Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo construct a reliable computational model for the classification of agonists and antagonists of 5-HT(1A) receptor.
methodsSupport vector machine (SVM), a well-known machine learning method, was employed to build a prediction model, and genetic algorithm (GA) was used to select the most relevant descriptors and to optimize two important parameters, C and r of the SVM model. The overall dataset used in this study comprised 284 ligands of the 5-HT(1A) receptor with diverse structures reported in the literatures.
resultsA SVM model was successfully developed that could be used to predict the probability of a ligand being an agonist or antagonist of the 5-HT(1A) receptor. The predictive accuracy for training and test sets was 0.942 and 0.865, respectively. For compounds with probability estimate higher than 0.7, the predictive accuracy of the model for training and test sets was 0.954 and 0.927, respectively. To further validate our model, the receiver operating characteristic (ROC) curve was plotted, and the Area-Under-the-ROC- Curve (AUC) value was calculated to be 0.883 for training set and 0.906 for test set.
conclusionA reliable SVM model was successfully developed that could effectively distinguish agonists and antagonists among the ligands of the 5-HT(1A) receptor. To our knowledge, this is the first effort for the classification of 5-HT(1A) receptor agonists and antagonists based on a diverse dataset. This method may be used to classify the ligands of other members of the GPCR family.
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