Exploring the Functional Food Potential of Zea mays Using Machine Learning–Based QSAR and Network Biology to Identify Anti-Diabetic and Anti-Inflammatory Phytochemicals

Traditional medicinal plants such as Zea mays (maize) display promising anti-diabetic and anti-inflammatory effects, yet their molecular mechanisms remain unclear. This study investigated the structure–activity relationships of Zea mays phytochemicals using a polypharmacology framework. Network pharmacology analyses, including protein-protein interaction mapping, KEGG pathway enrichment, and gene annotation, identified AKT1 as central mediator of the observed effects. A machine learning–guided QSAR model was constructed using PubChem substructure fingerprints and deployed as a predictive web application (https://akt1-pred.streamlit.app/). The model demonstrated strong performance, validated by ROC analysis and applicability domain assessment. Molecular docking and dynamics simulations further supported the predicted binding interactions. Delta-tocopherol, thiamine, and 9-ribosyl-trans-zeatin emerged as the most potent phytochemicals, in some cases showing higher predicted activity than FDA-approved drugs. These findings provide a mechanistic basis for Zea mays bioactivity, highlight the therapeutic relevance of its phytochemicals, and offer a computational tool to accelerate natural product–based drug discovery.