Biology has long relied on a limited molecular vocabulary shaped by natural evolution. Today, that alphabet is expanding. Advances in expanded genetic codes, non-canonical amino acids, macrocycles, de novo design, and AI-guided protein engineering are enabling scientists to create biomolecules with properties and functions that nature never evolved. This session explores the rise of programmable biomolecules at the intersection of biology, chemistry, and computation. Rather than simply optimizing existing proteins, researchers are building entirely new classes of functional molecules with novel architectures, chemistries, and therapeutic potential. From next-generation biologics to hybrid molecular scaffolds, the discussion will examine how the field is moving beyond nature’s defaults and toward a future where biomolecules can be designed with increasing precision, flexibility, and intent.

AI-enabled, self-driving labs are still emerging, but their foundations are already transforming how teams design, run, and interpret experiments. This session offers a practical guide for scientists and R&D leaders who want to understand what can be done today — from tightening design–test–learn loops to reducing manual error and capturing early benefits of autonomous experimentation. Rather than presenting an unrealized future, speakers will focus on practical, real-world steps that give organizations a competitive edge as SDL capabilities evolve and mature. Speakers will explore what’s working, what’s not, and how autonomous lab systems are reshaping protein engineering, pathway optimization, and therapeutic design.