Can autonomous Agentic-AI systems accelerate drug discovery and development in the next decade?

Abstract

The pharmaceutical industry is experiencing a profound transformation driven by rapid advancements in artificial intelligence (AI), increasing research and development complexity, and the expiration of key patents. Agentic-AI systems, which enable autonomous reasoning, decision-making, and adaptive learning beyond traditional data-driven automation, represent a paradigm shift in this context. This review examines the impact of Agentic-AI across the pharmaceutical value chain, including supply chain orchestration, manufacturing optimization, early-stage drug discovery, process development, and regulatory intelligence. These systems enhance operational resilience, reduce development timelines, and accelerate the identification of new therapeutic candidates by fostering dynamic human-AI collaboration. However, the widespread adoption of Agentic-AI introduces significant challenges related to workforce integration, ethical accountability, governance, and regulatory compliance. Addressing these challenges is essential for reliable and scalable deployment. This study identifies key factors shaping Agentic-AI’s influence over the next decade and provides a forward-looking perspective on its evolving role in pharmaceutical innovation.