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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Bibliografski detalji
Glavni autor:	Rajkumar R (22674565) (author)
Daljnji autori:	Anju Sharma (2900093) (author), Anuradha Badade (22674568) (author), Tanmaykumar Varma (17013628) (author), Prabha Garg (1625956) (author)
Izdano:	2025
Teme:	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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OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands
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OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands
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OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands
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OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands
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