As AI systems grow more capable in modeling complex biological systems, drug discovery is entering a new era — one where machine learning models don't just assist researchers, but actively generate hypotheses, design molecules, and predict therapeutic outcomes. This session explores the shift from traditional trial-and-error approaches to AI-driven methodologies that can integrate biological data at unprecedented scale and speed. What does it mean to have AI as a true collaborator in science? Can these systems unlock treatments for diseases that have eluded medicine for decades? As AI takes on a greater role in healthcare decision-making, how do we ensure that its use remains transparent, equitable, and ethically sound?