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...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	BMC bioinformatics Jg. 26; H. 1; S. 117 - 24
Hauptverfasser:	Schaduangrat, Nalini, Chuntakaruk, Hathaichanok, Rungrotmongkol, Thanyada, Mookdarsanit, Pakpoom, Shoombuatong, Watshara
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 30.04.2025 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	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 Thailand Cheminformatics Feature selection QSAR Multi-view feature Glucocorticoid receptor Machine learning Cushing’s syndrome
ISSN:	1471-2105, 1471-2105
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	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.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-025-06132-1