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How is artificial intelligence reshaping the way medicines are discovered, developed, manufactured, and delivered? Can algorithms identify safer drug candidates faster than traditional pipelines? Will clinical trials become adaptive, decentralized, and data-driven by design? How can AI prevent manufacturing failures, detect adverse drug reactions earlier, and secure pharmaceutical supply chains against disruption and counterfeiting?
In AI in Pharmaceutical Sciences: Concepts, Methods, and Real-World Applications, Exper authors present a rigorous yet accessible exploration of the technologies transforming modern pharmacy. From intelligent drug discovery and predictive toxicology to AI-optimized formulation design, smart manufacturing, regulatory automation, and precision therapeutics, this book maps the full pharmaceutical lifecycle through the lens of advanced analytics and machine learning.
What does explainable AI mean for regulatory approval? How can real-world data redefine pharmacovigilance? In what ways will digital twins and generative models accelerate innovation while preserving quality and compliance?
Bridging academic foundations with industrial case studies, the authors offer clarity on concepts, methods, infrastructure, governance, and implementation strategy. Designed for students, researchers, regulatory professionals, and industry leaders, this volume equips readers to understand not only how AI works, but why it matters now.
