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...

全面介绍

Saved in:

书目详细资料
主要作者:	Rajkumar R (22674565) (author)
其他作者:	Anju Sharma (2900093) (author), Anuradha Badade (22674568) (author), Tanmaykumar Varma (17013628) (author), Prabha Garg (1625956) (author)
出版:	2025
主题:	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
标签:	添加标签没有标签, 成为第一个标记此记录!

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

相似书籍