Artificial Intelligence in Pharmaceutical Sciences: Principles and Applications

Artificial Intelligence in Pharmaceutical Sciences examines the transformation reshaping how medicines are discovered, developed, manufactured, and evaluated in an increasingly data-driven world. As biological systems grow more complex and traditional analytical approaches reach their limits, artificial intelligence is emerging as a foundational scientific framework within pharmaceutical research.

This book presents AI not as a collection of tools, but as a discipline that redefines how knowledge is generated and decisions are made under uncertainty. It connects core pharmaceutical principles with computational reasoning, illustrating how chemical, biological, clinical, and real-world data are integrated across the pharmaceutical lifecycle.

Emphasizing conceptual clarity over narrow application, the text explores pharmaceutical data ecosystems, learning paradigms, model development, and governance within real scientific and regulatory constraints. It explains why linear research models are no longer sufficient and how intelligent systems enable earlier risk identification and more reliable evidence generation.

Importantly, the book addresses the boundaries of artificial intelligence in healthcare, reinforcing the essential role of human judgment, explainability, and accountability. Written for postgraduate students, researchers, and professionals, it offers a rigorous and accessible foundation for understanding AI as a transformative force in pharmaceutical sciences.