Artificial Intelligence is transforming pharmaceutical sciences by enabling data-driven decision-making across drug discovery, formulation development, manufacturing, and clinical practice. As biomedical data expand in scale and complexity, traditional approaches are increasingly inadequate, creating a critical need for intelligent, integrated methodologies.
Artificial Intelligence in Pharmaceutical Sciences: Methods and Applications provides a structured and mechanistic understanding of AI within a pharmaceutical context, bridging computational techniques with real-world applications.
The book connects foundational principles with advanced methods including machine learning, deep learning, natural language processing, and generative modeling, with strong emphasis on interpretability, validation, and regulatory relevance. Spanning the full pharmaceutical lifecycle from molecular modeling and virtual screening to process optimization and clinical decision systems, it presents a clear progression from fundamentals to translational application.
Designed for students, researchers, and professionals in pharmacy and healthcare, this work serves as a rigorous and forward-looking reference for modern pharmaceutical innovation.
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