"A COMPUTATIONAL FRAMEWORK FOR MODERN HERBAL DRUG DEVELOPMENT": ARTIFICIAL INTELLIGENCE – DRIVEN PHARMACOVIGILANCE

Herbal medicines are extensively utilized across the globe and constitute an important component of traditional and complementary healthcare practices. However, their safety is often inadequately monitored due to variability in composition, lack of standardization, contamination and insufficient pharmacovigilance systems. Herbal products may cause adverse drug reactions such as hepatotoxicity, nephrotoxicity, allergic reactions and clinically significant herb–drug interactions. Conventional pharmacovigilance methods face challenges in monitoring herbal medicines because of complex multi-component formulations and underreporting of adverse events. Recent advancements in artificial intelligence and machine learning offer effective solutions by enabling large-scale analysis of biomedical literature, adverse event databases and clinical records. AI-based tools such as BioBERT, OpenVigil, CLAMP, Hugging Face Transformers, and DeepChem can support automated detection of safety signals and prediction of potential herb–drug interactions. Integrating AI technologies with pharmacovigilance systems can improve Timely identification of adverse effects and improvement of overall safety. evaluation of herbal medicines. Strengthening pharmacovigilance frameworks and adopting advanced analytical tools are therefore essential to promote the safe and appropriate use of herbal medicines.