Authors : Praveen Kumar D.; Keerthana M.; Gowtham A.; Priyadharshini M.

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/bp6u3kfz

DOI : https://doi.org/10.38124/ijisrt/26aug996

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Artificial intelligence (AI) has become an important driver in the pharmaceutical value chain and is transforming the way new medicines were discovered, developed, tested and regulated. Machine learning, deep learning, natural language processing, graph neural networks and generative AI are now embedded in target identification and virtual screening, lead optimization, pre-clinical toxicity prediction, clinical trial design and post-market pharmacovigilance. Simultaneously, regulatory authorities worldwide such as the United States Food and Drug Administration (FDA), the European Medicines Agency (EMA), Japan’s Pharmaceuticals and Medical Devices Agency (PMDA), and the International Council for Harmonization (ICH)- have started to develop the frameworks that govern the credibility, transparency, and lifecycle management of artificial intelligence (AI) systems applied to generate regulatory evidence. This review synthesizes current literature to provide an integrated global view of AI-driven drug development and regulatory submission.

Keywords : Artificial Intelligence; Machine Learning; Deep Learning; Drug Discovery; Drug Development; Regulatory Science; FDA; EMA; NMPA; PMDA; Pharmacovigilance; Global Harmonization.

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