OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands

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...

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Enregistré dans:
Détails bibliographiques
Auteur principal: Rajkumar R (22674565) (author)
Autres auteurs: Anju Sharma (2900093) (author), Anuradha Badade (22674568) (author), Tanmaykumar Varma (17013628) (author), Prabha Garg (1625956) (author)
Publié: 2025
Sujets:
Biochemistry
Molecular Biology
Cancer
Biological Sciences not elsewhere classified
Chemical Sciences not elsewhere classified
Information Systems not elsewhere classified
recursive feature elimination
machine learning tool
identify molecular determinants
http :// github
curated data set
protein versus β
six classifiers
selective modulation
remains challenging
opioid ligands
freely available
encompassing physicochemical
enables pathway
coupled receptors
characterize experimentally
biased agonism
best performance
based features
3800 descriptors
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