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@article{202425,
        author = {DR. GAJANAN S SAWRATE and DR. PRABHAVATI D NADRE},
        title = {THE USE OF ARTIFICIAL INTELLIGENCE IN AYURVEDIC DRUG DEVELOPMENT: A REVIEW},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {6716-6718},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=202425},
        abstract = {Ayurveda, one of the world’s oldest systems of medicine, has gained global recognition for its holistic and personalized approach to healthcare. However, Ayurvedic drug development faces challenges such as lack of standardization, limited pharmacological validation, variability in herbal formulations, and difficulties in integrating traditional knowledge with modern scientific methodologies. Artificial Intelligence (AI), including machine learning and deep learning technologies, has emerged as a transformative tool in pharmaceutical research and offers promising opportunities for advancing Ayurvedic drug discovery and development. This review critically evaluates the role of AI in various stages of Ayurvedic drug development, including phytochemical screening, molecular docking, formulation optimization, target identification, network pharmacology, and clinical validation. Literature published between 2018 and 2024 was reviewed from databases such as PubMed, Scopus, Web of Science, and Google Scholar. Studies indicate that AI-based approaches can efficiently predict pharmacological activities of phytoconstituents, identify herb–protein interactions, and explain synergistic effects of polyherbal formulations such as Triphala and Chyawanprash. AI-driven analytics also support evidence-based validation and personalized Ayurvedic therapeutics. Furthermore, computational tools have accelerated herbal drug repurposing and biomarker discovery. Despite these advancements, challenges including insufficient structured datasets, quality variability of herbal drugs, and limited interdisciplinary collaboration remain significant barriers. The integration of AI with Ayurveda has the potential to establish a robust evidence-based framework for Ayurvedic medicine and improve its global acceptance. Future perspectives include development of AI-powered Ayurvedic databases, integration with omics technologies, and collaborative research between Ayurvedic scholars, data scientists, and pharmaceutical researchers.},
        keywords = {Artificial Intelligence; Ayurveda; Machine Learning; Drug Discovery; Herbal Medicine; Deep Learning; Phytochemicals; Network Pharmacology; Personalized Medicine; Ayurvedic Drug Development.},
        month = {May},
        }