Abstract: Drug discovery and development face high attrition, limited cross-model translatability, heterogeneous evaluation standards, and long timelines and costs. To improve efficiency, new methods are required. This special issue, “Digital and Intelligent Pharmacy—AI-driven Drug Discovery and Development”, presents seven studies spanning the full pipeline from target identification, analytical assessment, and mechanistic elucidation to implementation. The collection demonstrates: AI algorithms for prioritizing targets and candidates; computer vision and spectroscopic analytics to strengthen quality control; multi-source data integration to clarify pharmacological mechanisms; and organization-level pathway analysis to inform implementation. Together, these studies will provide practical directions and technical support for robust, generalizable translation in drug research and development.