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The integration of artificial intelligence (AI) into drug discovery has revolutionized the pharmaceutical
industry by significantly accelerating the identification of viable drug candidates, reducing development
costs, and improving clinical success rates. This paper examines the multifaceted influence of AI-driven
drug discovery on pharmaceutical supply chain management, exploring how advances in machine
learning (ML), deep learning, and natural language processing (NLP) are reshaping sourcing,
manufacturing, regulatory compliance, and distribution frameworks. A comprehensive narrative review
methodology was employed, drawing on peer-reviewed literature, industry reports, and case studies
published between 2015 and 2024. Findings indicate that AI tools such as generative models, predictive
analytics, and digital twin technologies are not only expediting the pre-clinical and clinical phases of drug
development but are also creating downstream ripple effects that demand adaptive supply chain
architectures. Key implications include the need for dynamic inventory management systems, AI-
enhanced regulatory compliance workflows, and resilient supplier networks capable of accommodating
the accelerated pace of drug approvals. The paper concludes with recommendations for pharmaceutical
managers and policymakers aimed at aligning supply chain infrastructure with the transformative pace of
AI-mediated pharmaceutical innovation.
Artificial Intelligence, Drug Discovery, Pharmaceutical Supply Chain, Machine Learning, Healthcare Management, Predictive Analytics