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This contributed volume presents a comprehensive overview of how artificial intelligence (AI), machine learning (ML), and traditional computational methods are being integrated and applied across various stages of pharmaceutical research and drug discovery. It covers a wide range of topics, including generative AI for novel compound design, deep learning in molecular modeling, ADMET prediction, and data curation strategies essential for effective AI applications. The book also discusses disease-specific case studies, such as AI-driven approaches for Alzheimer's disease, diabetes, cancer, and bacterial infections, as well as applications in drug repositioning, cosmetic ingredient design, and the analysis of natural compounds using density functional theory (DFT). By combining advanced computational strategies with real-world pharmaceutical challenges, the book offers valuable insights into current capabilities and future directions in the field. This work is a great resource for researchers, practitioners, and graduate students in pharmaceutical sciences, computational chemistry, bioinformatics, and related disciplines.