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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\u2019s 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.<\/jats:p>","DOI":"10.1186\/s12859-025-06132-1","type":"journal-article","created":{"date-parts":[[2025,5,2]],"date-time":"2025-05-02T11:09:35Z","timestamp":1746184175000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy"],"prefix":"10.1186","volume":"26","author":[{"given":"Nalini","family":"Schaduangrat","sequence":"first","affiliation":[]},{"given":"Hathaichanok","family":"Chuntakaruk","sequence":"additional","affiliation":[]},{"given":"Thanyada","family":"Rungrotmongkol","sequence":"additional","affiliation":[]},{"given":"Pakpoom","family":"Mookdarsanit","sequence":"additional","affiliation":[]},{"given":"Watshara","family":"Shoombuatong","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,4,30]]},"reference":[{"issue":"8","key":"6132_CR1","doi-asserted-by":"publisher","first-page":"3405","DOI":"10.1021\/acs.jmedchem.7b00162","volume":"60","author":"HJ Hunt","year":"2017","unstructured":"Hunt HJ, et al. 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