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MetaAMPK: Accurate Prediction of Adenosine Monophosphate-Activated Protein Kinase Activators Using a Meta-Learner
Andi Endang Kusuma Intan1, Darlene Nabila Zetta2, Kanokwan Jarukamjorn3
1Graduate School in the Program of Research and Development in Pharmaceuticals, Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Khon Kaen University, Khon Kaen 40002, Thailand.
ACS Omega
|October 6, 2025
Summary
This study introduces MetaAMPK, a deep learning model that accurately predicts activators of Adenosine monophosphate (AMP)-activated protein kinase (AMPK). This computational approach accelerates the discovery of new AMPK modulators for metabolic disorders.
Area of Science:
- Computational chemistry and cheminformatics
- Biomedical data science
- Drug discovery and development
Background:
- Adenosine monophosphate (AMP)-activated protein kinase (AMPK) is a key regulator of cellular metabolism and a significant therapeutic target for metabolic diseases like type 2 diabetes and nonalcoholic fatty liver disease.
- Predicting AMPK activators is challenging due to complex biological data, necessitating advanced computational methods to accelerate drug discovery and reduce costs.
Purpose of the Study:
- To develop a highly accurate in silico drug discovery pipeline for predicting Adenosine monophosphate (AMP)-activated protein kinase (AMPK) activators.
- To create a novel deep learning model, MetaAMPK, leveraging meta-learners with bidirectional long-short-term memory (BiLSTM) and convolutional neural network (CNN) architectures.
Main Methods:
- Developed the MetaAMPK deep learning framework incorporating meta-learners with BiLSTM and CNN.
- Encoded multifeature layers, including 12 molecular fingerprints and probability features, to enhance prediction accuracy.
- Validated model performance using Y-randomization, permutation importance, applicability domain analysis, and generalization tests on independent compounds.
Main Results:
- The MetaAMPK model achieved high accuracy (0.91), AUC (0.96), and MCC (0.82), demonstrating robust prediction of AMPK activity.
- Structural importance analysis confirmed the model's ability to classify AMPK activators based on molecular structure.
- Molecular docking studies identified pseudoberberine, beta-lapachone, and donepezil as potent AMPK activators with superior binding affinities compared to metformin.
Conclusions:
- The MetaAMPK framework provides a highly accurate and robust computational tool for predicting AMPK activators.
- This approach significantly enhances the efficiency of the drug discovery pipeline for metabolic disorders.
- The identified compounds show potential as novel therapeutic agents targeting AMPK pathways.
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