In this podcast, we explore the evolving concept of "druggability" in the modern era of drug discovery, emphasizing how artificial intelligence (AI) and diverse therapeutic modalities are expanding the range of treatable biological targets. It details various drug types, including traditional small molecules, biologics (like monoclonal antibodies), RNA-based therapeutics, targeted protein degraders (PROTACs and molecular glues), and conjugates (ADCs, AOCs, RDCs), outlining their mechanisms, strengths, and limitations. The document also highlights AI's transformative role in target identification, structure prediction, lead design, and tractability assessment, citing case studies in chronic diseases like cancer and neurodegeneration to illustrate the impact of these advancements. Finally, it offers strategic recommendations for integrating AI and modality-aware approaches into drug development pipelines to address previously "undruggable" diseases. Produced by Dr. Jake Chen.