Despite rapid advances in structural techniques, computational models, and biophysical tools, many discovery programs still operate in disciplinary silos. This panel will explore how to weave together structural biology, biophysics, computational chemistry, and CADD to generate a holistic, data-driven view of targets and ligands, ultimately enabling more potent, selective, and developable candidates.

• How can structural insights, computational modeling, and experimental validation be better integrated into a single design loop?
• What are the practical barriers, technical, organizational, cultural, that prevent true cross-functional integration, and how can we overcome them?
• Where has integration already delivered measurable improvements in candidate quality (potency, selectivity, developability)?
• How can AI and machine learning act as a “bridge” between disciplines, and where are the limits of that approach?
• What does an ideal, multidisciplinary discovery team look like in practice?