MetaAMPK: Accurate Prediction of Adenosine Monophosphate-Activated Protein Kinase Activators Using a Meta-Learner Neural Network

Adenosine monophosphate (AMP)-activated protein kinase (AMPK) regulates cellular metabolism and is a promising target for metabolic disorders. The activation of AMPK represents a promising therapeutic target for chronic metabolic diseases such as type 2 diabetes and nonalcoholic fatty liver disease. However, accurately predicting AMPK activators remains challenging due to the complexity of its biological data. Given the high global prevalence of chronic metabolic diseases, accelerating the discovery of novel AMPK modulators while reducing time and development costs is cruciala goal that can be effectively addressed through an in silico drug discovery pipeline. This study developed a novel, highly accurate deep learning model, called MetaAMPK, utilizing meta-learners with bidirectional long-short-term memory (BiLSTM) and the convolutional neural network (CNN) to improve the prediction of AMPK activity. This framework encoded multifeature layers including 12 molecular fingerprints and probability features that enable the meta-learners to achieve an accuracy of 0.91, an area under the curve (AUC) of 0.96, and a Matthews correlation coefficient (MCC) of 0.82, ensuring that these models are highly accurate and robust. To further validate the prediction outcome, the meta-learners were tested with <i>Y</i>-randomization, permutation importance, and the applicability domain. Structural importance analysis was elucidated from the test compounds, confirming that the models were able to classify the AMPK activators based on their structure. A generalization test on the 53 independent compounds was done to validate the meta-learners with 0.96 (96%) accuracy, confirming the real-world application of the developed models. Finally, molecular docking studies provide further biological validation of the predicted AMPK activators. The docking results indicate that pseudoberberine, beta-lapachone, and donepezil from predicted AMPK activators exhibit stronger AMPK binding affinities (-8.205, -7.585, and -7.484 kcal/mol, respectively) than metformin (-5.387 kcal/mol), emphasizing the model's capability to identify novel AMPK activators. Thus, these results prove that our MetaAMPK framework provides highly accurate predictions of AMPK activators, potentially enhancing the computational drug development pipeline.