Multitarget Natural Compounds for Ischemic Stroke Treatment: Integration of Deep Learning Prediction and Experimental Validation

mRNA levels. Notably, mulberrin and ellagic acid showed superior efficacy in modulating oxidative stress, inflammation, and neurotrophic signaling. This study establishes a robust deep learning-driven framework for identifying multitarget natural therapeutics for ischemic stroke. The validated compounds, particularly mulberrin and ellagic acid, are promising for stroke treatment development. Our findings demonstrate the effectiveness of integrating computational prediction with experimental validation in accelerating drug discovery for complex neurological disorders.