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<p>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.</p>

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M3S-GRPred: a novel ensemble learning approach for the interpretable prediction of glucocorticoid receptor antagonists using a multi-step stacking strategy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年4月30日

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  • Nalini Schaduangrat,
  • Hathaichanok Chuntakaruk,
  • Thanyada Rungrotmongkol,
  • Pakpoom Mookdarsanit,
  • Watshara Shoombuatong

摘要

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.