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