Muhammad Yasir, Jin-Hee Han, Jongseon Choe, Wanjoo Chun
Epilepsy affects approximately 50 million people worldwide, with nearly one-third of patients experiencing drug-resistant seizures despite available antiepileptic drugs (AEDs). Natural products remain an important source of bioactive scaffolds for drug discovery, offering diverse chemical structures capable of modulating key pathological pathways in epilepsy. This review examines major classes of natural compounds, including alkaloids, flavonoids, terpenoids, and phenolic compounds, and their activity against validated targets such as GABAergic and glutamatergic systems, voltage-gated ion channels, and neuroinflammatory pathways. Advances in computational drug discovery have significantly accelerated the identification and optimization of these compounds. Approaches such as virtual screening, molecular docking, molecular dynamics simulations, and machine learning models, particularly graph neural networks (GNNs), enable the efficient prediction of compound target interactions, binding stability, and pharmacokinetic properties, including blood-brain barrier (BBB) penetration and ADMET profiles. These methods support the prioritization and rational modification of natural product leads from large chemical libraries. Notable clinical approval of cannabidiol (Epidiolex) highlights the translational potential of natural product-based therapeutics. However, challenges such as limited bioavailability, pharmacokinetic constraints, and variability in natural sources continue to hinder development. This review provides an integrated perspective on natural product scaffolds, their molecular targets, and the computational strategies driving their advancement toward novel antiepileptic therapies.