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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2025
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| _version_ | 1849927644852781056 |
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| author | Rajkumar R (22674565) |
| author2 | Anju Sharma (2900093) Anuradha Badade (22674568) Tanmaykumar Varma (17013628) Prabha Garg (1625956) |
| author2_role | author author author author |
| author_facet | Rajkumar R (22674565) Anju Sharma (2900093) Anuradha Badade (22674568) Tanmaykumar Varma (17013628) Prabha Garg (1625956) |
| author_role | author |
| dc.creator.none.fl_str_mv | Rajkumar R (22674565) Anju Sharma (2900093) Anuradha Badade (22674568) Tanmaykumar Varma (17013628) Prabha Garg (1625956) |
| dc.date.none.fl_str_mv | 2025-11-24T12:38:05Z |
| dc.identifier.none.fl_str_mv | 10.1021/acsmedchemlett.5c00539.s003 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/OpioidBias_A_Machine_Learning_Tool_for_Predicting_the_Biased_Agonism_of_Opioid_Ligands/30694564 |
| dc.rights.none.fl_str_mv | CC BY-NC 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | 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 |
| dc.title.none.fl_str_mv | OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | 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. |
| eu_rights_str_mv | openAccess |
| id | Manara_2f8631a037c13004df30c1c843bdd884 |
| identifier_str_mv | 10.1021/acsmedchemlett.5c00539.s003 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/30694564 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY-NC 4.0 |
| spelling | OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid LigandsRajkumar R (22674565)Anju Sharma (2900093)Anuradha Badade (22674568)Tanmaykumar Varma (17013628)Prabha Garg (1625956)BiochemistryMolecular BiologyCancerBiological Sciences not elsewhere classifiedChemical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedrecursive feature eliminationmachine learning toolidentify molecular determinantshttp :// githubcurated data setprotein versus βsix classifiersselective modulationremains challengingopioid ligandsfreely availableencompassing physicochemicalenables pathwaycoupled receptorscharacterize experimentallybiased agonismbest performancebased features3800 descriptorsBiased 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.2025-11-24T12:38:05ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1021/acsmedchemlett.5c00539.s003https://figshare.com/articles/dataset/OpioidBias_A_Machine_Learning_Tool_for_Predicting_the_Biased_Agonism_of_Opioid_Ligands/30694564CC BY-NC 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/306945642025-11-24T12:38:05Z |
| spellingShingle | OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands Rajkumar R (22674565) 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 |
| status_str | publishedVersion |
| title | OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands |
| title_full | OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands |
| title_fullStr | OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands |
| title_full_unstemmed | OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands |
| title_short | OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands |
| title_sort | OpioidBias: A Machine Learning Tool for Predicting the Biased Agonism of Opioid Ligands |
| topic | 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 |