M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy
Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse effects. While experimental methods can accurately id...
Saved in:
| Published in: | BMC bioinformatics Vol. 26; no. 1; pp. 117 - 24 |
|---|---|
| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
London
BioMed Central
30.04.2025
BioMed Central Ltd Springer Nature B.V BMC |
| Subjects: | |
| ISSN: | 1471-2105, 1471-2105 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse effects. While experimental methods can accurately identify GR antagonists, they are often not cost-effective for large-scale drug discovery. Thus, computational approaches leveraging SMILES information for precise in silico identification of GR antagonists are crucial, enabling efficient and scalable drug discovery. Here, we develop a new ensemble learning approach using a multi-step stacking strategy (M3S), termed M3S-GRPred, aimed at rapidly and accurately discovering novel GR antagonists. To the best of our knowledge, M3S-GRPred is the first SMILES-based predictor designed to identify GR antagonists without the use of 3D structural information. In M3S-GRPred, we first constructed different balanced subsets using an under-sampling approach. Using these balanced subsets, we explored and evaluated heterogeneous base-classifiers trained with a variety of SMILES-based feature descriptors coupled with popular ML algorithms. Finally, M3S-GRPred was constructed by integrating probabilistic feature from the selected base-classifiers derived from a two-step feature selection technique. Our comparative experiments demonstrate that M3S-GRPred can precisely identify GR antagonists and effectively address the imbalanced dataset. Compared to traditional ML classifiers, M3S-GRPred attained superior performance in terms of both the training and independent test datasets. Additionally, M3S-GRPred was applied to identify potential GR antagonists among FDA-approved drugs confirmed through molecular docking, followed by detailed MD simulation studies for drug repurposing in Cushing’s syndrome. We anticipate that M3S-GRPred will serve as an efficient screening tool for discovering novel GR antagonists from vast libraries of unknown compounds in a cost-effective manner. |
|---|---|
| AbstractList | Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse effects. While experimental methods can accurately identify GR antagonists, they are often not cost-effective for large-scale drug discovery. Thus, computational approaches leveraging SMILES information for precise in silico identification of GR antagonists are crucial, enabling efficient and scalable drug discovery. Here, we develop a new ensemble learning approach using a multi-step stacking strategy (M3S), termed M3S-GRPred, aimed at rapidly and accurately discovering novel GR antagonists. To the best of our knowledge, M3S-GRPred is the first SMILES-based predictor designed to identify GR antagonists without the use of 3D structural information. In M3S-GRPred, we first constructed different balanced subsets using an under-sampling approach. Using these balanced subsets, we explored and evaluated heterogeneous base-classifiers trained with a variety of SMILES-based feature descriptors coupled with popular ML algorithms. Finally, M3S-GRPred was constructed by integrating probabilistic feature from the selected base-classifiers derived from a two-step feature selection technique. Our comparative experiments demonstrate that M3S-GRPred can precisely identify GR antagonists and effectively address the imbalanced dataset. Compared to traditional ML classifiers, M3S-GRPred attained superior performance in terms of both the training and independent test datasets. Additionally, M3S-GRPred was applied to identify potential GR antagonists among FDA-approved drugs confirmed through molecular docking, followed by detailed MD simulation studies for drug repurposing in Cushing's syndrome. We anticipate that M3S-GRPred will serve as an efficient screening tool for discovering novel GR antagonists from vast libraries of unknown compounds in a cost-effective manner. Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse effects. While experimental methods can accurately identify GR antagonists, they are often not cost-effective for large-scale drug discovery. Thus, computational approaches leveraging SMILES information for precise in silico identification of GR antagonists are crucial, enabling efficient and scalable drug discovery. Here, we develop a new ensemble learning approach using a multi-step stacking strategy (M3S), termed M3S-GRPred, aimed at rapidly and accurately discovering novel GR antagonists. To the best of our knowledge, M3S-GRPred is the first SMILES-based predictor designed to identify GR antagonists without the use of 3D structural information. In M3S-GRPred, we first constructed different balanced subsets using an under-sampling approach. Using these balanced subsets, we explored and evaluated heterogeneous base-classifiers trained with a variety of SMILES-based feature descriptors coupled with popular ML algorithms. Finally, M3S-GRPred was constructed by integrating probabilistic feature from the selected base-classifiers derived from a two-step feature selection technique. Our comparative experiments demonstrate that M3S-GRPred can precisely identify GR antagonists and effectively address the imbalanced dataset. Compared to traditional ML classifiers, M3S-GRPred attained superior performance in terms of both the training and independent test datasets. Additionally, M3S-GRPred was applied to identify potential GR antagonists among FDA-approved drugs confirmed through molecular docking, followed by detailed MD simulation studies for drug repurposing in Cushing's syndrome. We anticipate that M3S-GRPred will serve as an efficient screening tool for discovering novel GR antagonists from vast libraries of unknown compounds in a cost-effective manner. Keywords: Cushing's syndrome, Glucocorticoid receptor, QSAR, Cheminformatics, Machine learning, Feature selection, Multi-view feature Abstract Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse effects. While experimental methods can accurately identify GR antagonists, they are often not cost-effective for large-scale drug discovery. Thus, computational approaches leveraging SMILES information for precise in silico identification of GR antagonists are crucial, enabling efficient and scalable drug discovery. Here, we develop a new ensemble learning approach using a multi-step stacking strategy (M3S), termed M3S-GRPred, aimed at rapidly and accurately discovering novel GR antagonists. To the best of our knowledge, M3S-GRPred is the first SMILES-based predictor designed to identify GR antagonists without the use of 3D structural information. In M3S-GRPred, we first constructed different balanced subsets using an under-sampling approach. Using these balanced subsets, we explored and evaluated heterogeneous base-classifiers trained with a variety of SMILES-based feature descriptors coupled with popular ML algorithms. Finally, M3S-GRPred was constructed by integrating probabilistic feature from the selected base-classifiers derived from a two-step feature selection technique. Our comparative experiments demonstrate that M3S-GRPred can precisely identify GR antagonists and effectively address the imbalanced dataset. Compared to traditional ML classifiers, M3S-GRPred attained superior performance in terms of both the training and independent test datasets. Additionally, M3S-GRPred was applied to identify potential GR antagonists among FDA-approved drugs confirmed through molecular docking, followed by detailed MD simulation studies for drug repurposing in Cushing’s syndrome. We anticipate that M3S-GRPred will serve as an efficient screening tool for discovering novel GR antagonists from vast libraries of unknown compounds in a cost-effective manner. Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse effects. While experimental methods can accurately identify GR antagonists, they are often not cost-effective for large-scale drug discovery. Thus, computational approaches leveraging SMILES information for precise in silico identification of GR antagonists are crucial, enabling efficient and scalable drug discovery. Here, we develop a new ensemble learning approach using a multi-step stacking strategy (M3S), termed M3S-GRPred, aimed at rapidly and accurately discovering novel GR antagonists. To the best of our knowledge, M3S-GRPred is the first SMILES-based predictor designed to identify GR antagonists without the use of 3D structural information. In M3S-GRPred, we first constructed different balanced subsets using an under-sampling approach. Using these balanced subsets, we explored and evaluated heterogeneous base-classifiers trained with a variety of SMILES-based feature descriptors coupled with popular ML algorithms. Finally, M3S-GRPred was constructed by integrating probabilistic feature from the selected base-classifiers derived from a two-step feature selection technique. Our comparative experiments demonstrate that M3S-GRPred can precisely identify GR antagonists and effectively address the imbalanced dataset. Compared to traditional ML classifiers, M3S-GRPred attained superior performance in terms of both the training and independent test datasets. Additionally, M3S-GRPred was applied to identify potential GR antagonists among FDA-approved drugs confirmed through molecular docking, followed by detailed MD simulation studies for drug repurposing in Cushing's syndrome. We anticipate that M3S-GRPred will serve as an efficient screening tool for discovering novel GR antagonists from vast libraries of unknown compounds in a cost-effective manner.Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in advancing therapeutic development, optimizing treatment efficacy, and mitigating adverse effects. While experimental methods can accurately identify GR antagonists, they are often not cost-effective for large-scale drug discovery. Thus, computational approaches leveraging SMILES information for precise in silico identification of GR antagonists are crucial, enabling efficient and scalable drug discovery. Here, we develop a new ensemble learning approach using a multi-step stacking strategy (M3S), termed M3S-GRPred, aimed at rapidly and accurately discovering novel GR antagonists. To the best of our knowledge, M3S-GRPred is the first SMILES-based predictor designed to identify GR antagonists without the use of 3D structural information. In M3S-GRPred, we first constructed different balanced subsets using an under-sampling approach. Using these balanced subsets, we explored and evaluated heterogeneous base-classifiers trained with a variety of SMILES-based feature descriptors coupled with popular ML algorithms. Finally, M3S-GRPred was constructed by integrating probabilistic feature from the selected base-classifiers derived from a two-step feature selection technique. Our comparative experiments demonstrate that M3S-GRPred can precisely identify GR antagonists and effectively address the imbalanced dataset. Compared to traditional ML classifiers, M3S-GRPred attained superior performance in terms of both the training and independent test datasets. Additionally, M3S-GRPred was applied to identify potential GR antagonists among FDA-approved drugs confirmed through molecular docking, followed by detailed MD simulation studies for drug repurposing in Cushing's syndrome. We anticipate that M3S-GRPred will serve as an efficient screening tool for discovering novel GR antagonists from vast libraries of unknown compounds in a cost-effective manner. |
| ArticleNumber | 117 |
| Audience | Academic |
| Author | Mookdarsanit, Pakpoom Shoombuatong, Watshara Chuntakaruk, Hathaichanok Rungrotmongkol, Thanyada Schaduangrat, Nalini |
| Author_xml | – sequence: 1 givenname: Nalini surname: Schaduangrat fullname: Schaduangrat, Nalini organization: Faculty of Medical Technology, Center for Research Innovation and Biomedical Informatics, Mahidol University – sequence: 2 givenname: Hathaichanok surname: Chuntakaruk fullname: Chuntakaruk, Hathaichanok organization: Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Faculty of Science, Center of Excellence in Structural and Computational Biology, Chulalongkorn University, Faculty of Medicine, Center for Artificial Intelligence in Medicine, Chulalongkorn University, Bangkok – sequence: 3 givenname: Thanyada surname: Rungrotmongkol fullname: Rungrotmongkol, Thanyada organization: Program in Bioinformatics and Computational Biology, Graduate School, Chulalongkorn University, Faculty of Science, Center of Excellence in Structural and Computational Biology, Chulalongkorn University – sequence: 4 givenname: Pakpoom surname: Mookdarsanit fullname: Mookdarsanit, Pakpoom organization: Faculty of Science, Computer Science and Artificial Intelligence, Chandrakasem Rajabhat University – sequence: 5 givenname: Watshara surname: Shoombuatong fullname: Shoombuatong, Watshara email: watshara.sho@mahidol.ac.th organization: Faculty of Medical Technology, Center for Research Innovation and Biomedical Informatics, Mahidol University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40307679$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9ks1u1DAUhSNURH_gBVggS2xgkeKfOE7YoKqCMlIRqIW15Tg3GQ8ZO9hORZ-Fl8WZaWmnQlUWsW6-c27u9TnM9qyzkGUvCT4mpCrfBUIrXueY8hyXhNGcPMkOSCFITgnme_fO-9lhCCuMiagwf5btF5hhUYr6IPvzhV3mZxffPLTvkULWXcGAwAZYNwOgAZS3xvZIjaN3Si9R5zyKS0DGRvCjh6hmLh1ao6NxFrkO9cOknXY-Gu1MizxoGGPSKRtV76wJMaApbGzRehqiyUOEEYWo9M-5GqJXEfrr59nTTg0BXty8j7Ifnz5-P_2cn389W5yenOe6LIqYl6oVqUVZle28DxA173jdKqabSqVBWw6kEh0UoKgWpGwqQXiqsaKFpus0O8oWW9_WqZUcvVkrfy2dMnJTcL6Xah5mANlximvOMQhRFqrkDSXQ6JpqoIpWBUteH7Ze49SsodVg0zDDjunuF2uWsndXklBcFHVRJIc3Nw7e_ZogRLk2QcMwKAtuCpKRumKEV3hu9voBunKTt2lXklFM-Hzd5R3VqzSBsZ1LjfVsKk8qJkSNCROJOv4PlZ4W1ukeLXQm1XcEb3cEiYnwO_ZqCkEuLi922Vf3t_JvHbc5TEC1BbR3IXjopDZRzYFKf2EGSbCcIy-3kZcp8nITeUmSlD6Q3ro_KmJbUUiw7cHfbe4R1V-EfhRM |
| CitedBy_id | crossref_primary_10_1038_s41598_025_08510_4 |
| Cites_doi | 10.1021/jm9602928 10.1016/j.tips.2019.04.015 10.1016/j.physbeh.2014.03.004 10.1186/s40842-020-00105-4 10.1093/bib/bbab172 10.1016/S0960-0760(00)00121-7 10.21037/gs.2019.11.03 10.1016/j.bmcl.2020.127656 10.1007/s11095-009-9975-2 10.1210/jc.2012-3582 10.1021/ci049885e 10.1016/j.chemosphere.2023.139147 10.1016/j.bmc.2021.116212 10.1038/s41598-022-20143-5 10.1186/1750-1172-7-41 10.1101/gad.9.13.1608 10.1016/S2213-8587(21)00235-7 10.1080/07391102.2021.1960608 10.1186/s13321-023-00721-z 10.1093/nar/gkm276 10.3390/molecules25122764 10.1038/s41598-022-08173-5 10.1093/nar/gkv951 10.1016/j.jsbmb.2018.10.007 10.1021/jm901452y 10.1080/07391102.2022.2123392 10.1039/D2FO03466B 10.1038/s41598-023-50393-w 10.1016/j.ejmech.2022.114382 10.1517/17460441.2015.1032936 10.1096/fj.12-208330 10.1002/jcc.21707 10.1080/07391102.2024.2318482 10.1016/j.bmcl.2007.06.036 10.1016/j.str.2005.01.010 10.1210/en.2008-1355 10.1016/j.compbiomed.2022.105704 10.1038/s41401-021-00855-6 10.1146/annurev.med.48.1.129 10.1093/nar/gky1075 10.7717/peerj.11716 10.1016/j.future.2024.07.033 10.1021/ct400341p 10.1210/clinem/dgac492 10.1021/jp003020w 10.1021/ci00046a002 10.1121/1.4865840 10.1038/s41598-022-11897-z 10.1016/j.compbiomed.2023.106784 10.1016/j.canlet.2008.10.050 10.1159/000314297 10.1021/acs.jmedchem.7b00162 10.1016/j.isci.2022.104883 10.1080/01480545.2019.1658768 10.1074/jbc.M212711200 10.1016/j.ymeth.2021.12.001 10.1093/bioinformatics/btaa702 10.1016/S1056-8719(00)00107-6 10.1002/advs.202102435 10.1038/s41598-024-55160-z 10.1210/jc.2011-3350 10.2174/1389557519666191119144100 10.1021/ci025584y 10.1002/jcc.26223 10.1093/protein/14.8.565 10.3390/ijms23042141 10.1186/s13321-016-0185-8 10.1002/cmdc.200800274 10.1093/pnasnexus/pgac198 10.1152/ajplung.00136.2022 10.1021/acs.jcim.9b00776 10.1002/jcc.20084 10.1016/j.bmcl.2015.10.097 10.1016/0021-9991(77)90098-5 10.1002/open.202100248 10.1111/bph.15254 10.1021/ci010132r |
| ContentType | Journal Article |
| Copyright | The Author(s) 2025 2025. The Author(s). COPYRIGHT 2025 BioMed Central Ltd. 2025. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2025 2025 |
| Copyright_xml | – notice: The Author(s) 2025 – notice: 2025. The Author(s). – notice: COPYRIGHT 2025 BioMed Central Ltd. – notice: 2025. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2025 2025 |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 3V. 7QO 7SC 7X7 7XB 88E 8AL 8AO 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABUWG AEUYN AFKRA ARAPS AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU COVID DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ JQ2 K7- K9. L7M LK8 L~C L~D M0N M0S M1P M7P P5Z P62 P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS Q9U 7X8 5PM DOA |
| DOI | 10.1186/s12859-025-06132-1 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science ProQuest Central (Corporate) Biotechnology Research Abstracts Computer and Information Systems Abstracts Health & Medical Collection (ProQuest) ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Computing Database (Alumni Edition) ProQuest Pharma Collection Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central UK/Ireland Advanced Technologies & Computer Science Collection ProQuest Central Essentials Biological Science Collection ProQuest Central ProQuest Technology Collection Natural Science Collection ProQuest One Community College Coronavirus Research Database ProQuest Central Korea Engineering Research Database Proquest Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Computer Science Collection Computer Science Database ProQuest Health & Medical Complete (Alumni) Advanced Technologies Database with Aerospace ProQuest Biological Science Collection Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Computing Database ProQuest Health & Medical Collection Medical Database Biological Science Database (ProQuest) ProQuest advanced technologies & aerospace journals ProQuest Advanced Technologies & Aerospace Collection Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China ProQuest Central Basic MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database Computer Science Database ProQuest Central Student ProQuest Advanced Technologies & Aerospace Collection ProQuest Central Essentials ProQuest Computer Science Collection Computer and Information Systems Abstracts SciTech Premium Collection ProQuest Central China ProQuest One Applied & Life Sciences ProQuest One Sustainability Health Research Premium Collection Natural Science Collection Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Advanced Technologies & Aerospace Collection ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest One Academic UKI Edition Engineering Research Database ProQuest One Academic ProQuest One Academic (New) Technology Collection Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest One Academic Middle East (New) ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Pharma Collection ProQuest Central ProQuest Health & Medical Research Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) ProQuest Central Korea Advanced Technologies Database with Aerospace ProQuest Computing ProQuest Central Basic ProQuest Computing (Alumni Edition) ProQuest SciTech Collection Computer and Information Systems Abstracts Professional Advanced Technologies & Aerospace Database ProQuest Medical Library ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE MEDLINE - Academic Publicly Available Content Database CrossRef |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 3 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology |
| EISSN | 1471-2105 |
| EndPage | 24 |
| ExternalDocumentID | oai_doaj_org_article_f5209550e7764a65b21ebc92ce2a2843 PMC12044944 A837790137 40307679 10_1186_s12859_025_06132_1 |
| Genre | Journal Article |
| GeographicLocations | Thailand |
| GeographicLocations_xml | – name: Thailand |
| GrantInformation_xml | – fundername: National Research Council of Thailand and Mahidol University grantid: N42A660380 – fundername: MU-KMUTT Biomedical Engineering & Biomaterials Research Consortium – fundername: Mahidol University |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5VS 6J9 7X7 88E 8AO 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AAKPC AASML ABDBF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADUKV AEAQA AENEX AEUYN AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS ARAPS AZQEC BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK DU5 DWQXO E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FYUFA GNUQQ GROUPED_DOAJ GX1 HCIFZ HMCUK HYE IAO ICD IHR INH INR ISR ITC K6V K7- KQ8 LK8 M1P M7P MK~ ML0 M~E O5R O5S OK1 OVT P2P P62 PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PUEGO RBZ RNS ROL RPM RSV SBL SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XH6 XSB AAYXX AFFHD CITATION ALIPV CGR CUY CVF ECM EIF NPM 3V. 7QO 7SC 7XB 8AL 8FD 8FK COVID FR3 JQ2 K9. L7M L~C L~D M0N M48 P64 PKEHL PQEST PQUKI PRINS Q9U 7X8 5PM |
| ID | FETCH-LOGICAL-c644t-6ad7ece686d2859e795f59da3cb8a030d5e187fe4ea2c716b87155e134debffc3 |
| IEDL.DBID | M7P |
| ISICitedReferencesCount | 2 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001479698700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1471-2105 |
| IngestDate | Mon Nov 10 04:28:26 EST 2025 Tue Nov 04 02:03:35 EST 2025 Fri Sep 05 17:20:03 EDT 2025 Tue Oct 07 05:20:25 EDT 2025 Tue Nov 11 10:47:53 EST 2025 Tue Nov 04 18:13:00 EST 2025 Thu Nov 13 15:58:19 EST 2025 Mon Jul 21 05:30:58 EDT 2025 Sat Nov 29 07:57:19 EST 2025 Tue Nov 18 22:20:05 EST 2025 Sat Sep 06 07:27:29 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Cheminformatics Feature selection QSAR Multi-view feature Glucocorticoid receptor Machine learning Cushing’s syndrome |
| Language | English |
| License | 2025. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c644t-6ad7ece686d2859e795f59da3cb8a030d5e187fe4ea2c716b87155e134debffc3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://www.proquest.com/docview/3201517806?pq-origsite=%requestingapplication% |
| PMID | 40307679 |
| PQID | 3201517806 |
| PQPubID | 44065 |
| PageCount | 24 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_f5209550e7764a65b21ebc92ce2a2843 pubmedcentral_primary_oai_pubmedcentral_nih_gov_12044944 proquest_miscellaneous_3198315803 proquest_journals_3201517806 gale_infotracmisc_A837790137 gale_infotracacademiconefile_A837790137 gale_incontextgauss_ISR_A837790137 pubmed_primary_40307679 crossref_citationtrail_10_1186_s12859_025_06132_1 crossref_primary_10_1186_s12859_025_06132_1 springer_journals_10_1186_s12859_025_06132_1 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-04-30 |
| PublicationDateYYYYMMDD | 2025-04-30 |
| PublicationDate_xml | – month: 04 year: 2025 text: 2025-04-30 day: 30 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | BMC bioinformatics |
| PublicationTitleAbbrev | BMC Bioinformatics |
| PublicationTitleAlternate | BMC Bioinformatics |
| PublicationYear | 2025 |
| Publisher | BioMed Central BioMed Central Ltd Springer Nature B.V BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: Springer Nature B.V – name: BMC |
| References | JP Pang (6132_CR21) 2022; 43 F Cadepond (6132_CR12) 1997; 48 P Charoenkwan (6132_CR54) 2023; 158 S Ahmad (6132_CR51) 2022; 12 6132_CR58 N Krishnamurthy (6132_CR83) 2012; 26 N Schaduangrat (6132_CR44) 2023; 15 TJ Dolinsky (6132_CR64) 2007; 35 M Cazzola (6132_CR77) 2019; 40 MJ Weiser (6132_CR80) 2009; 150 DR Brown (6132_CR7) 2020; 6 V Onnis (6132_CR27) 2010; 53 EF Pettersen (6132_CR56) 2004; 25 RE Carhart (6132_CR36) 1985; 25 X Hu (6132_CR22) 2022; 237 N Schaduangrat (6132_CR32) 2023; 15 VC Yan (6132_CR85) 2020; 30 B Kauppi (6132_CR59) 2003; 278 TJ Cole (6132_CR2) 1995; 9 CW Yap (6132_CR35) 2011; 32 F Zare (6132_CR26) 2023; 41 G Wolber (6132_CR71) 2005; 45 M Spreafico (6132_CR14) 2009; 4 AR Pfaff (6132_CR76) 2020; 20 6132_CR63 M Azadpour (6132_CR52) 2014; 135 F Castinetti (6132_CR8) 2012; 7 S Kim (6132_CR38) 2016; 44 N Schaduangrat (6132_CR33) 2023; 13 S Genheden (6132_CR69) 2015; 10 E Motylewska (6132_CR84) 2009; 276 M Savas (6132_CR4) 2022; 107 SH Shin (6132_CR16) 2023; 14 C Potamitis (6132_CR28) 2019; 186 AA Kazi (6132_CR86) 2021; 41 6132_CR60 N Schaduangrat (6132_CR34) 2021; 9 P Charoenkwan (6132_CR48) 2022; 204 M Fleseriu (6132_CR5) 2012; 97 DF Lewis (6132_CR15) 2000; 74 HJ Hunt (6132_CR1) 2017; 60 D Li (6132_CR9) 2020; 9 6132_CR30 6132_CR74 D Mendez (6132_CR29) 2019; 47 6132_CR79 Y Matsuzaka (6132_CR17) 2022; 23 6132_CR39 P Charoenkwan (6132_CR41) 2021; 22 MV Yelshanskaya (6132_CR62) 2022; 179 P Mark (6132_CR65) 2001; 105 D Zhang (6132_CR53) 2021; 37 JL Durant (6132_CR37) 2002; 42 NC Ray (6132_CR20) 2007; 17 P Charoenkwan (6132_CR55) 2022; 25 X Hu (6132_CR24) 2022; 9 N Schaduangrat (6132_CR45) 2023; 13 R Dey (6132_CR19) 2001; 14 C Steinbeck (6132_CR40) 2003; 43 C Steffensen (6132_CR3) 2010; 92 DR Roe (6132_CR68) 2013; 9 W Shoombuatong (6132_CR50) 2024; 14 M Stanojevic (6132_CR13) 2023; 336 N Schaduangrat (6132_CR31) 2022; 12 6132_CR70 AA Malik (6132_CR43) 2020; 41 M Sencanski (6132_CR61) 2022; 11 CA Lipinski (6132_CR72) 2000; 44 6132_CR42 JP Ryckaert (6132_CR67) 1977; 23 S Simeon (6132_CR75) 2016; 8 MF Sanner (6132_CR57) 2005; 13 GW Bemis (6132_CR73) 1996; 39 6132_CR46 P Charoenkwan (6132_CR49) 2022; 146 AE Kudwa (6132_CR81) 2014; 129 N Suthprasertporn (6132_CR82) 2022; 45 R Metin (6132_CR25) 2022; 40 NRC Alves (6132_CR23) 2020; 60 Y Matsuzaka (6132_CR18) 2020; 25 HJ Hunt (6132_CR6) 2015; 25 M Fleseriu (6132_CR11) 2021; 9 R Chari (6132_CR66) 2009; 26 P Charoenkwan (6132_CR47) 2022; 12 OM Dekkers (6132_CR10) 2013; 98 K Khanna (6132_CR78) 2022; 323 |
| References_xml | – volume: 39 start-page: 2887 issue: 15 year: 1996 ident: 6132_CR73 publication-title: J Med Chem doi: 10.1021/jm9602928 – volume: 40 start-page: 452 issue: 7 year: 2019 ident: 6132_CR77 publication-title: Trends Pharmacol Sci doi: 10.1016/j.tips.2019.04.015 – volume: 129 start-page: 287 year: 2014 ident: 6132_CR81 publication-title: Physiol Behav doi: 10.1016/j.physbeh.2014.03.004 – volume: 6 start-page: 18 issue: 1 year: 2020 ident: 6132_CR7 publication-title: Clin Diabetes Endocrinol doi: 10.1186/s40842-020-00105-4 – volume: 22 start-page: bbab172 issue: 6 year: 2021 ident: 6132_CR41 publication-title: Briefings in Bioinform doi: 10.1093/bib/bbab172 – volume: 74 start-page: 179 issue: 4 year: 2000 ident: 6132_CR15 publication-title: J Steroid Biochem Mol Biol doi: 10.1016/S0960-0760(00)00121-7 – volume: 9 start-page: 43 issue: 1 year: 2020 ident: 6132_CR9 publication-title: Gland Surg doi: 10.21037/gs.2019.11.03 – volume: 30 start-page: 127656 issue: 24 year: 2020 ident: 6132_CR85 publication-title: Bioorg Med Chem Lett doi: 10.1016/j.bmcl.2020.127656 – volume: 26 start-page: 2607 issue: 12 year: 2009 ident: 6132_CR66 publication-title: Pharm Res doi: 10.1007/s11095-009-9975-2 – volume: 98 start-page: 2277 issue: 6 year: 2013 ident: 6132_CR10 publication-title: J Clin Endocrinol Metab doi: 10.1210/jc.2012-3582 – volume: 45 start-page: 160 issue: 1 year: 2005 ident: 6132_CR71 publication-title: J Chem Inf Model doi: 10.1021/ci049885e – volume: 336 start-page: 139147 year: 2023 ident: 6132_CR13 publication-title: Chemosphere doi: 10.1016/j.chemosphere.2023.139147 – volume: 41 start-page: 116212 year: 2021 ident: 6132_CR86 publication-title: Bioorg Med Chem doi: 10.1016/j.bmc.2021.116212 – volume: 12 start-page: 1 issue: 1 year: 2022 ident: 6132_CR31 publication-title: Sci Rep doi: 10.1038/s41598-022-20143-5 – volume: 7 start-page: 41 year: 2012 ident: 6132_CR8 publication-title: Orphanet J Rare Dis doi: 10.1186/1750-1172-7-41 – ident: 6132_CR60 – volume: 9 start-page: 1608 issue: 13 year: 1995 ident: 6132_CR2 publication-title: Genes Dev doi: 10.1101/gad.9.13.1608 – ident: 6132_CR70 – volume: 9 start-page: 847 issue: 12 year: 2021 ident: 6132_CR11 publication-title: Lancet Diabetes Endocrinol doi: 10.1016/S2213-8587(21)00235-7 – volume: 40 start-page: 11418 issue: 21 year: 2022 ident: 6132_CR25 publication-title: J Biomol Struct Dyn doi: 10.1080/07391102.2021.1960608 – volume: 15 start-page: 50 issue: 1 year: 2023 ident: 6132_CR44 publication-title: J Cheminform doi: 10.1186/s13321-023-00721-z – volume: 35 start-page: W522 year: 2007 ident: 6132_CR64 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkm276 – volume: 25 start-page: 2764 issue: 12 year: 2020 ident: 6132_CR18 publication-title: Molecules doi: 10.3390/molecules25122764 – volume: 12 start-page: 4106 issue: 1 year: 2022 ident: 6132_CR51 publication-title: Sci Rep doi: 10.1038/s41598-022-08173-5 – volume: 15 start-page: 50 issue: 1 year: 2023 ident: 6132_CR32 publication-title: J Cheminform doi: 10.1186/s13321-023-00721-z – volume: 44 start-page: D1202 issue: D1 year: 2016 ident: 6132_CR38 publication-title: Nucleic Acids Res doi: 10.1093/nar/gkv951 – volume: 186 start-page: 142 year: 2019 ident: 6132_CR28 publication-title: J Steroid Biochem Mol Biol doi: 10.1016/j.jsbmb.2018.10.007 – volume: 53 start-page: 3065 issue: 8 year: 2010 ident: 6132_CR27 publication-title: J Med Chem doi: 10.1021/jm901452y – volume: 41 start-page: 7640 issue: 16 year: 2023 ident: 6132_CR26 publication-title: J Biomol Struct Dyn doi: 10.1080/07391102.2022.2123392 – volume: 14 start-page: 1869 issue: 4 year: 2023 ident: 6132_CR16 publication-title: Food Funct doi: 10.1039/D2FO03466B – volume: 13 start-page: 22994 issue: 1 year: 2023 ident: 6132_CR45 publication-title: Sci Rep doi: 10.1038/s41598-023-50393-w – volume: 237 start-page: 114382 year: 2022 ident: 6132_CR22 publication-title: Eur J Med Chem doi: 10.1016/j.ejmech.2022.114382 – volume: 10 start-page: 449 issue: 5 year: 2015 ident: 6132_CR69 publication-title: Expert Opin Drug Discov doi: 10.1517/17460441.2015.1032936 – volume: 26 start-page: 3993 issue: 10 year: 2012 ident: 6132_CR83 publication-title: FASEB J doi: 10.1096/fj.12-208330 – volume: 32 start-page: 1466 issue: 7 year: 2011 ident: 6132_CR35 publication-title: J Comput Chem doi: 10.1002/jcc.21707 – ident: 6132_CR74 doi: 10.1080/07391102.2024.2318482 – volume: 17 start-page: 4901 issue: 17 year: 2007 ident: 6132_CR20 publication-title: Bioorg Med Chem Lett doi: 10.1016/j.bmcl.2007.06.036 – ident: 6132_CR39 – volume: 13 start-page: 447 issue: 3 year: 2005 ident: 6132_CR57 publication-title: Structure doi: 10.1016/j.str.2005.01.010 – volume: 150 start-page: 1817 issue: 4 year: 2009 ident: 6132_CR80 publication-title: Endocrinology doi: 10.1210/en.2008-1355 – volume: 146 year: 2022 ident: 6132_CR49 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2022.105704 – volume: 43 start-page: 2429 issue: 9 year: 2022 ident: 6132_CR21 publication-title: Acta Pharmacol Sin doi: 10.1038/s41401-021-00855-6 – volume: 48 start-page: 129 year: 1997 ident: 6132_CR12 publication-title: Annu Rev Med doi: 10.1146/annurev.med.48.1.129 – volume: 47 start-page: D930 issue: D1 year: 2019 ident: 6132_CR29 publication-title: Nucleic Acids Res doi: 10.1093/nar/gky1075 – volume: 9 year: 2021 ident: 6132_CR34 publication-title: PeerJ doi: 10.7717/peerj.11716 – ident: 6132_CR42 doi: 10.1016/j.future.2024.07.033 – volume: 9 start-page: 3084 issue: 7 year: 2013 ident: 6132_CR68 publication-title: J Chem Theory Comput doi: 10.1021/ct400341p – volume: 107 start-page: 3162 issue: 11 year: 2022 ident: 6132_CR4 publication-title: J Clin Endocrinol Metab doi: 10.1210/clinem/dgac492 – volume: 105 start-page: 9954 issue: 43 year: 2001 ident: 6132_CR65 publication-title: J Phys Chem A doi: 10.1021/jp003020w – volume: 25 start-page: 64 issue: 2 year: 1985 ident: 6132_CR36 publication-title: J Chem Inf Comput Sci doi: 10.1021/ci00046a002 – ident: 6132_CR46 – volume: 135 start-page: EL40 issue: 3 year: 2014 ident: 6132_CR52 publication-title: J Acoustical Soc Am doi: 10.1121/1.4865840 – volume: 12 start-page: 7697 issue: 1 year: 2022 ident: 6132_CR47 publication-title: Sci Rep doi: 10.1038/s41598-022-11897-z – volume: 158 year: 2023 ident: 6132_CR54 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2023.106784 – volume: 276 start-page: 68 issue: 1 year: 2009 ident: 6132_CR84 publication-title: Cancer Lett doi: 10.1016/j.canlet.2008.10.050 – volume: 92 start-page: 1 issue: Suppl 1 year: 2010 ident: 6132_CR3 publication-title: Neuroendocrinology doi: 10.1159/000314297 – volume: 60 start-page: 3405 issue: 8 year: 2017 ident: 6132_CR1 publication-title: J Med Chem doi: 10.1021/acs.jmedchem.7b00162 – volume: 25 start-page: 104883 issue: 9 year: 2022 ident: 6132_CR55 publication-title: Iscience doi: 10.1016/j.isci.2022.104883 – volume: 45 start-page: 44 issue: 1 year: 2022 ident: 6132_CR82 publication-title: Drug Chem Toxicol doi: 10.1080/01480545.2019.1658768 – volume: 278 start-page: 22748 issue: 25 year: 2003 ident: 6132_CR59 publication-title: J Biol Chem doi: 10.1074/jbc.M212711200 – volume: 204 start-page: 189 year: 2022 ident: 6132_CR48 publication-title: Methods doi: 10.1016/j.ymeth.2021.12.001 – volume: 37 start-page: 171 issue: 2 year: 2021 ident: 6132_CR53 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btaa702 – volume: 44 start-page: 235 issue: 1 year: 2000 ident: 6132_CR72 publication-title: J Pharmacol Toxicol Methods doi: 10.1016/S1056-8719(00)00107-6 – volume: 9 issue: 3 year: 2022 ident: 6132_CR24 publication-title: Adv Sci (Weinh) doi: 10.1002/advs.202102435 – volume: 13 start-page: 22994 issue: 1 year: 2023 ident: 6132_CR33 publication-title: Sci Rep doi: 10.1038/s41598-023-50393-w – volume: 14 start-page: 4463 issue: 1 year: 2024 ident: 6132_CR50 publication-title: Sci Rep doi: 10.1038/s41598-024-55160-z – volume: 97 start-page: 2039 issue: 6 year: 2012 ident: 6132_CR5 publication-title: J Clin Endocrinol Metab doi: 10.1210/jc.2011-3350 – volume: 20 start-page: 513 issue: 6 year: 2020 ident: 6132_CR76 publication-title: Mini Rev Med Chem doi: 10.2174/1389557519666191119144100 – ident: 6132_CR30 – volume: 43 start-page: 493 issue: 2 year: 2003 ident: 6132_CR40 publication-title: J Chem Inf Comput Sci doi: 10.1021/ci025584y – volume: 41 start-page: 1820 issue: 20 year: 2020 ident: 6132_CR43 publication-title: J Comput Chem doi: 10.1002/jcc.26223 – volume: 14 start-page: 565 issue: 8 year: 2001 ident: 6132_CR19 publication-title: Protein Eng doi: 10.1093/protein/14.8.565 – volume: 23 start-page: 2141 issue: 4 year: 2022 ident: 6132_CR17 publication-title: Int J Mol Sci doi: 10.3390/ijms23042141 – volume: 8 start-page: 1 year: 2016 ident: 6132_CR75 publication-title: J Cheminform doi: 10.1186/s13321-016-0185-8 – volume: 4 start-page: 100 issue: 1 year: 2009 ident: 6132_CR14 publication-title: ChemMedChem doi: 10.1002/cmdc.200800274 – ident: 6132_CR63 doi: 10.1093/pnasnexus/pgac198 – volume: 323 start-page: L372 issue: 3 year: 2022 ident: 6132_CR78 publication-title: Am J Physiol Lung Cell Mol Physiol doi: 10.1152/ajplung.00136.2022 – volume: 60 start-page: 794 issue: 2 year: 2020 ident: 6132_CR23 publication-title: J Chem Inf Model doi: 10.1021/acs.jcim.9b00776 – volume: 25 start-page: 1605 issue: 13 year: 2004 ident: 6132_CR56 publication-title: J Comput Chem doi: 10.1002/jcc.20084 – volume: 25 start-page: 5720 issue: 24 year: 2015 ident: 6132_CR6 publication-title: Bioorg Med Chem Lett doi: 10.1016/j.bmcl.2015.10.097 – volume: 23 start-page: 327 issue: 3 year: 1977 ident: 6132_CR67 publication-title: J Comput Phys doi: 10.1016/0021-9991(77)90098-5 – volume: 11 issue: 2 year: 2022 ident: 6132_CR61 publication-title: ChemistryOpen doi: 10.1002/open.202100248 – ident: 6132_CR58 – volume: 179 start-page: 3628 issue: 14 year: 2022 ident: 6132_CR62 publication-title: Br J Pharmacol doi: 10.1111/bph.15254 – volume: 42 start-page: 1273 issue: 6 year: 2002 ident: 6132_CR37 publication-title: J Chem Inf Comput Sci doi: 10.1021/ci010132r – ident: 6132_CR79 |
| SSID | ssj0017805 |
| Score | 2.4818566 |
| Snippet | Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds promise in... Abstract Accelerating drug discovery for glucocorticoid receptor (GR)-related disorders, including innovative machine learning (ML)-based approaches, holds... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 117 |
| SubjectTerms | Algorithms Analysis Bioinformatics Bioinformatics and chemoinformatics in drug discovery Biomedical and Life Sciences Cheminformatics Computational Biology - methods Computational Biology/Bioinformatics Computer Appl. in Life Sciences Cost effectiveness Cushing syndrome Cushing’s syndrome Datasets Diabetes Drug development Drug discovery Drug Discovery - methods Drug therapy Ensemble Learning Experimental methods FDA approval Feature selection Glucocorticoid receptor Glucocorticoid receptors Glucocorticoids Hormones Humans Life Sciences Machine Learning Methods Microarrays Molecular docking Mortality Physiological aspects QSAR R&D Receptors Receptors, Glucocorticoid - antagonists & inhibitors Receptors, Glucocorticoid - chemistry Research & development Surgery Transcription factors |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQBRIXxJtAQQYhcYCocR62w60gChyoqhak3izHnmxXWpLVZrdSfwt_lhknWZoi4MJtFY-z9sx4PBOPv2HsJYBTwlUqVlDZOBcpxNpDEuelc7ghEqKIDcUm1OGhPj0tjy6V-qKcsB4euGfcXk15GuhGg1Iyt7KoUgGVK1MqZIWmNeB8Jqocg6nh_ICQ-scrMlrudYJw2mIq3Ur7VxqLyTYU0Pp_t8mXNqWrCZNXTk3DZnRwm90avEi-34_-DrsGzV12o68reXGP_fiSncQfj49W4N9yy5v2HBYc41X4Xi2AD4UiZnzEE-fouHJ0BPl8m4FIdPjDz8O1B97WPOS2Y6iK_9jOPUdDCUuM1zlKxs5awt_tOCXR42t5yFKMUX-WHJ1PR1_jedfD4F7cZ98OPnx9_ykeqjDEDn2ldSytV_hKqaUnJoIqi7oovc1cpS2aCF-A0KqGHGyKcpcVhmAFPstyD1Vdu-wB22naBh4xntR1AUmJXQpPN3IrLWsBZZVKZb3zKmJiFIpxA0Q5VcpYmBCqaGl6QRoUpAmCNCJir7d9lj1Ax1-p35Gst5QErh0eoMqZQeXMv1QuYi9IUwzBZzSUnzOzm64zn0-Ozb4OAI4iw7m8GojqFufg7HDdATlBiFsTyt0JJa5vN20eFdIM9qUzGfptBWm5jNjzbTP1pJy5BtoN0ohSZ6LQCQ74Ya-_23nnZNulKiOmJ5o9Ycy0pZmfBfRxkSZ5XuZ5xN6Mi-DXuP7M-cf_g_NP2M00LGI6zdtlO-vVBp6y6-58Pe9Wz4IJ-Amcsl_J priority: 102 providerName: Directory of Open Access Journals – databaseName: SpringerLINK Contemporary 1997-Present dbid: RSV link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lj9MwELbQAhIX3o_AggxC4gDR1nnYDrcFscCB1aoLaG-WY09KpZJUTbvS_hb-LDNOUsjykOBWxeM0nswzHn_D2FMAp4QrVaygtHEmEoi1h0mcFc6hQyREERuaTajDQ31yUhz1h8Laodp92JIMljqotZZ7rSCstZjar5IPSmLMeS6iu9PUsGF6_Hm7d0Ao_cPxmN_OG7mggNT_qz3-ySGdL5Y8t2MaHNHBtf9bwnV2tQ88-X4nKTfYBahvsstdK8qzW-zbh_Q4fjs9WoF_yS2vm1NYcExx4Wu5AN73lpjxAYKcY6zLMXbk823RItHhDz8PJyV4U_FQDo_ZLf5jM_ccbSssMcXn-DLtrCHI3pZT3T3elofCxhhFbskxXnX0AZ-3HXLu2W326eDNx9fv4r5xQ-wwvFrH0nqFt5RaelowqCKv8sLb1JXaolXxOQitKsjAJigqssSsLcdraeahrCqX3mE7dVPDPcYnVZXDpMApuadDvKWWlYCiTKSy3nkVMTG8S-N6VHNqrrEwIbvR0nRMN8h0E5huRMSeb-csO0yPv1K_IhHZUhIed7jQrGamV29TUTURJnuglMyszMtEQOmKhNqtYQCQRuwJCZghxI2aSnpmdtO25v3x1OzrgPkoUlzLs56oanANzvYnJJATBNI1otwdUaJJcOPhQY5Nb5Jak2Kol5NyyIg93g7TTCqzq6HZII0odCpyPcEHvtuJ_XbdGbkDqYqI6ZFCjBgzHqnnXwJguUgmWVZkWcReDHrx47n-zPn7_0b-gF1JgmrRVt8u21mvNvCQXXKn63m7ehRsxHdTWmSd priority: 102 providerName: Springer Nature |
| Title | M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy |
| URI | https://link.springer.com/article/10.1186/s12859-025-06132-1 https://www.ncbi.nlm.nih.gov/pubmed/40307679 https://www.proquest.com/docview/3201517806 https://www.proquest.com/docview/3198315803 https://pubmed.ncbi.nlm.nih.gov/PMC12044944 https://doaj.org/article/f5209550e7764a65b21ebc92ce2a2843 |
| Volume | 26 |
| WOSCitedRecordID | wos001479698700001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVADU databaseName: BioMed Central Open Access Free customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RBZ dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.biomedcentral.com/search/ providerName: BioMedCentral – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: DOA dateStart: 20000101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M~E dateStart: 20000101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Biological Science Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: M7P dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/biologicalscijournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: K7- dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: Health & Medical Collection customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: 7X7 dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/healthcomplete providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest advanced technologies & aerospace journals customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: P5Z dateStart: 20090101 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: BENPR dateStart: 20090101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: PIMPY dateStart: 20090101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest – providerCode: PRVAVX databaseName: SpringerLINK Contemporary 1997-Present customDbUrl: eissn: 1471-2105 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017805 issn: 1471-2105 databaseCode: RSV dateStart: 20001201 isFulltext: true titleUrlDefault: https://link.springer.com/search?facet-content-type=%22Journal%22 providerName: Springer Nature |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1bb9MwFLbYBhIv3C-BURmExANYq3Ozwwva0AYTWhW1gDperMR2SqWSlKadtN_Cn-UcN-nIEHvhJWrj4yS2j8_FPv4OIS-t1YLrXDBh84yF3LdMGttnYaI1KEREFMlcsgkxGMjxOEmbBbe6CatsZaIT1KbSuEa-F4CmiriQ_fjd_CfDrFG4u9qk0NgiO4iS4LvQvXSzi4B4_e1BGRnv1RzR2hgmcEUt5jPeUUYOs_9vyfyHarocNnlp79SppKPb_9uYO-RWY4zS_TX33CXXbHmP3Finpzy_T36dBCP2YZgurHlLM1pWZ3ZGwe21P_KZpU2-iQltYckp2L8U7Ek63QQyIh38MFN3eoJWBXUh8uDxwhurqaEgb-0c3H4KA5xNKoTxrSnG4sNjqQt2ZMCGcwo2rMZFfVqv0XTPH5AvR4ef339kTTIHpsHkWrI4MwIeGcvY4ChYkURFlJgs0LnMQNKYyHIpChvazAf2iXPw5CK4F4TG5kWhg4dku6xK-5jQflFEtp9Alcjgwd5cxgW3Se7HIjPaCI_wdlSVbpDOMeHGTDmPR8ZqzQkKOEE5TlDcI683deZrnI8rqQ-QWTaUiNHtblSLiWqmvCowwggcQCtEHGZxlPvc5jrxMQUbGAWBR14gqylE4SgxzGeSrepaHY-Gal86HEgeQFteNURFBW3QWXNqAnoCgbs6lLsdShATulvcsqJqxFStLvjQI883xVgTQ-9KW62Ahicy4JHswwc_Wk-ATbtDVBGxSDwiO1Oj0zHdknL63YGYc78fhkkYeuRNO4suvuvfPf_k6mY8JTd9N79xu2-XbC8XK_uMXNdny2m96JEtMRbuKntk5-BwkA57bhEGrp8E6znpAdc0-gbl6fFJegr_hqOvvwHCY3do |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Zb9QwELZKAcEL97FQwCBQH8DqOpcdJITKUbrqoaotUt9M4kyWlUqybHaL-lv4D_xGZpxkS4roWx94W8XjJPbOGc98w9hzAKukTZVQkCYikB4InUFfBLG1aBAJUSRxzSbU9rY-OIh3FtivthaG0ipbnegUdVZa-ka-4qOlCqXS_ejt-LugrlF0utq20KjZYgOOf2DIVr0ZfMD_94XnrX3cf78umq4CwqLtn4ooyRRYiHSUEXgbqDjMwzhLfJvqBFk-C0FqlUMAiYfriFIMKUK85gcZpHlufbzvBXYx8LUiudpQYn5qQf0B2sIcHa1Ukh4gqGEsWU1PyI7xcz0C_rYEf5jC02map85qnQlcu_6_bd4Ndq1xtvlqLR032QIUt9jluv3m8W32c8vfE592dyaQveYJL8ojOOQY1sO39BB4009jyFvYdY7-PUd_mY_miZpEhz-ykasO4WXOXQkARvT4xHKUcbQnMJ7iPGTgZFgSTHHFqdYAb8tdMqdAMRtz9NEtHVrwqkYLPr7DPp_Lztxli0VZwH3G-3keQj_GKWFGhcupjnIJcepFKslspnpMtlxkbIPkTg1FDo2L6HRkas4zyHnGcZ6RPfZyPmdc45icSf2OmHNOSRjk7kI5GZpGpZmcMqgwwAWloiCJwtSTkNrYoxZz6PT4PfaMWNsQykhBaUzDZFZVZrC3a1a1w7mUPq5luSHKS1yDTZqqENwJAibrUC51KFEN2u5wy_qmUcOVOeH7Hns6H6aZlFpYQDlDGhlrX4a6jy98rxa4-boDMoGRintMd0SxszHdkWL01YG0S68fBHEQ9NirVmpP3uvfO__g7GU8YVfW97c2zeZge-Mhu-o53UJHm0tscTqZwSN2yR5NR9XksdNMnH05b2n-DefCzV0 |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Zj9MwELbQcogX7iOwgEFIPEC0dQ7b4W05FlZAVW1htW-WYzulUkmqpl1pfwt_lhnnYLMcEuKtisdpPJkZz8Qz3xDy1DkjmMlFKFyuw4RFLpTWjcIkMwY2REQU0b7ZhBiP5dFRNjlVxe-z3bsjyaamAVGayvXO0haNiku-UzPEXQuxFSvuR1EI8c_5BBPpMV6fHvbnCIjY35XK_HbeYDvyqP2_2uZTm9PZxMkzp6d-U9q7-v_LuUautA4p3W0k6Do558ob5GLTovLkJvn-KZ6G7w4mK2dfUk3L6tgtKIS-7lu-cLTtOTGjHTQ5BR-Ygk9J530yI9LBDzv3FRS0KqhPk4eoF_6xmlsKNtctIfSn8JL1rEIo35piPj7clvqExxBEcUnBjzX4YZ_WDaLuyS3yZe_t59fvw7ahQ2jA7VqHXFsBt-SSW1ywE1lapJnVscmlBmtjU8ekKFzidAQixHOI5lK4FifW5UVh4ttkq6xKd5fQUVGkbpTBlNRicW8uecFclkdcaGusCAjr3qsyLdo5Nt1YKB_1SK4apitguvJMVywgz_s5ywbr46_Ur1BcekrE6fYXqtVMtWqvCswygiDQCcETzdM8Yi43WYRt2MAxiAPyBIVNIRJHiak-M72pa7U_PVC70mNBshjW8qwlKipYg9Ft5QRwAsG7BpTbA0owFWY43Mm0ak1VrWJwAVNUFB6Qx_0wzsT0u9JVG6BhmYxZKkfwwHcaFejXneA2wUUWEDlQjgFjhiPl_KsHMmfRKEmyJAnIi05Hfj7Xnzl_79_IH5FLkzd76uP--MN9cjnyWoangdtka73auAfkgjlez-vVQ286fgAf_HBl |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=M3S-GRPred%3A+a+novel+ensemble+learning+approach+for+the+interpretable+prediction+of+glucocorticoid+receptor+antagonists+using+a+multi-step+stacking+strategy&rft.jtitle=BMC+bioinformatics&rft.au=Schaduangrat%2C+Nalini&rft.au=Chuntakaruk%2C+Hathaichanok&rft.au=Rungrotmongkol%2C+Thanyada&rft.au=Mookdarsanit%2C+Pakpoom&rft.date=2025-04-30&rft.pub=BioMed+Central+Ltd&rft.issn=1471-2105&rft.eissn=1471-2105&rft.volume=26&rft.issue=1&rft_id=info:doi/10.1186%2Fs12859-025-06132-1&rft.externalDocID=A837790137 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1471-2105&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1471-2105&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1471-2105&client=summon |