OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands
Biased signaling at G-protein-coupled receptors (GPCRs) enables pathway-selective modulation but remains challenging to characterize experimentally. We present OpioidBias, a machine learning tool for predicting G-protein versus β-arrestin bias in opioid ligands. A curated data set of opioid ligands...
Saved in:
| 主要作者: | |
|---|---|
| 其他作者: | , , , |
| 出版: |
2025
|
| 主題: | |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | Biased signaling at G-protein-coupled receptors (GPCRs) enables pathway-selective modulation but remains challenging to characterize experimentally. We present OpioidBias, a machine learning tool for predicting G-protein versus β-arrestin bias in opioid ligands. A curated data set of opioid ligands was represented with >3800 descriptors from RDKit and Mordred, encompassing physicochemical, topological, and fingerprint-based features. Feature selection using Boruta and recursive feature elimination (RFE) guided the training of six classifiers. A random forest model incorporating combined RDKit and Mordred descriptors, fingerprints, and RFE showed the best performance and was further interpreted using feature analysis to identify molecular determinants of bias. OpioidBias is freely available (http://github.com/PGlab-NIPER/OpioidBias) to support biased ligand discovery across opioid pharmacology. |
|---|