AI is transforming biology into a fully integrated computational discipline, where discovery depends on the seamless interaction between models, compute infrastructure, and experimental systems. As foundation models for proteins, genomes, and cellular systems mature, the challenge is no longer prediction alone. It is building a unified stack that connects generative design, large-scale computation, and rapid experimental feedback into continuous learning loops. This session explores how the next generation of computational biology platforms is emerging at the intersection of cloud computing, GPU-accelerated modeling, advanced simulation, and high-throughput experimental infrastructure. Leaders across AI, biotech, and technology will discuss how tightly integrated design-build-test-learn cycles are reshaping therapeutic discovery, enabling adaptive model refinement, and accelerating the transition from in silico hypotheses to real-world biological outcomes.