Surfactants (the molecules that power cleaning, foaming, emulsifying, and conditioning in everyday products) are entering a new era of innovation. Advances in bio-engineering are enabling the design of next-generation surfactants with enhanced performance and reduced environmental impact. By leveraging biologically derived and bio-inspired approaches, researchers and product developers are rethinking how these essential ingredients are produced and formulated. New pathways in fermentation, enzyme engineering, and green chemistry are opening opportunities to create surfactants that are not only more effective, but also better aligned with circularity and nature-positive design principles. This session will explore how bio-engineered surfactants are reshaping formulation science: from new production platforms to novel functional properties, and what it will take to scale these innovations into the next generation of sustainable consumer products.

Biologics and engineered proteins have traditionally evolved through cycles of intuition, screening, and incremental optimization. Today, AI is transforming proteins into programmable systems; governed by learnable patterns across activity, stability, expression, specificity, manufacturability, and environmental performance. This shift is unlocking a new generation of biomolecules, from next-generation therapeutics to sustainable enzymes and functional biological systems, that would have been impossible to design by hand. In this session, leaders from biopharma, industrial biotech, machine learning, and protein engineering will explore how multiparameter optimization, generative modeling, and closed-loop experimental validation are reshaping biomolecular design across domains. From clinical biologics to planetary-scale applications, we examine the shift from trial-and-error to predictive, constraint-driven design, and what it means for R&D timelines, scalability, and real-world impact.

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.