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...

Full description

Saved in:
Bibliographic Details
Published in:BMC bioinformatics Vol. 26; no. 1; pp. 117 - 24
Main Authors: Schaduangrat, Nalini, Chuntakaruk, Hathaichanok, Rungrotmongkol, Thanyada, Mookdarsanit, Pakpoom, Shoombuatong, Watshara
Format: Journal Article
Language:English
Published: London BioMed Central 30.04.2025
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
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 Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography: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