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 Y-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.