BGATT-GR: accurate identification of glucocorticoid receptor antagonists based on data augmentation combined with BiGRU-attention
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Issued Date
2025-12-01
Resource Type
eISSN
20452322
Scopus ID
2-s2.0-105009876358
Journal Title
Scientific Reports
Volume
15
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
Scientific Reports Vol.15 No.1 (2025)
Suggested Citation
Shoombuatong W., Mookdarsanit P., Schaduangrat N., Mookdarsanit L. BGATT-GR: accurate identification of glucocorticoid receptor antagonists based on data augmentation combined with BiGRU-attention. Scientific Reports Vol.15 No.1 (2025). doi:10.1038/s41598-025-05839-8 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/111222
Title
BGATT-GR: accurate identification of glucocorticoid receptor antagonists based on data augmentation combined with BiGRU-attention
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Corresponding Author(s)
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Abstract
The glucocorticoid receptor (GR) is a critical nuclear receptor that regulates a broad spectrum of physiological functions, including stress adaptation, immune response, and metabolism. Given the association between aberrant GR signaling and various pathological conditions, this pathway represents a promising therapeutic target. Several GR antagonists have been developed to block glucocorticoid binding to the receptor, showing therapeutic potential in disorders characterized by heightened or dysregulated glucocorticoid signaling. Therefore, this study proposes an innovative deep learning-based hybrid framework (termed BGATT-GR) that leverages a data augmentation method, a bidirectional gated recurrent unit (BiGRU), and a self-attention mechanism (ATT) to attain more accurate identification of GR antagonists. In BGATT-GR, we first employed AP2D, CDKExt, KR, Morgan, and RDKIT to extract molecular descriptors of GR antagonists and combined these molecular descriptors to generate multi-view features. Second, we adopted a data augmentation method that combined both random under-sampling (RUS) and the synthetic minority over-sampling technique (SMOTE) to address the issue of class imbalance. Third, the BGATT architecture was constructed to enhance the utility of the multi-view features by generating informative feature embeddings. Finally, we applied principal component analysis (PCA) to reduce the dimensionality of these feature embeddings and fed the processed feature vectors into the final classifier. Extensive experimental results showed that BGATT-GR provided more stable performance in both cross-validation and independent tests. Furthermore, the independent test results revealed that BGATT-GR attained superior predictive performance compared with several conventional ML models, with a balanced accuracy of 0.957, an MCC of 0.853, and an AUPR of 0.962. In summary, our experimental results provide strong evidence to suggest that BGATT-GR is highly accurate and effective for identifying GR antagonists.