Jiwon Kim

Drug discovery is not limited by models. It is limited by data. While AI is accelerating molecular design and target discovery, the real bottleneck remains the generation, integration, and interpretation of biological datasets that are complex, heterogeneous, and often not yet predictive. Pharma-scale discovery requires more than algorithms. It requires new approaches to building and operationalizing data itself. This session explores how next-generation DNA synthesis, high-throughput experimentation, and integrated data infrastructures are enabling a new biology data flywheel. From experimental datasets that inform translational decisions to emerging standards for capturing real-world and preclinical signals, leaders will discuss how data generation strategies are reshaping discovery workflows. Speakers from pharma, AI-native biotech, and platform providers will examine how biology is becoming a programmable data layer, enabling faster biologics development, more informed portfolio decisions, and new collaborative models that connect experimental systems, computational tools, and pharma-scale discovery pipelines.