Drug discovery has long meant screening vast libraries and optimizing whatever survives. Semiconductors and aircraft broke from this pattern, designed computationally before fabrication. Biologics may now be crossing the same threshold. Generative AI enables antibodies to be designed from first principles, rather than found. Latent-X2 generates drug-like candidates with developability and low ex vivo immunogenicity from the first generation, properties that previously required years of optimization, matching or exceeding approved therapeutics in head-to-head comparisons. Latent-Y extends this into fully autonomous campaigns: from text prompt through target analysis, candidate design, and computational validation, to lab-ready sequences. Across nine targets, it produced confirmed binders against six, a 67% success rate at single-digit nanomolar affinities, completed 56 times faster than expert estimates. In this keynote, Simon Kohl (CEO, Latent Labs) will share what this shift looks like in practice: what works, what remains hard, and what it means when the starting point is no longer a library, but a prompt.

For the first time, AI is enabling us to imagine medicines that “think” - turning on only inside diseased cells or under specific physiological conditions. Neural networks trained on RNA, protein, and cellular data are unlocking a new generation of programmable therapies with unprecedented precision, from cancer drugs that remain inert until encountering tumor signals to RNA medicines capable of adapting to dynamic biological environments. But designing intelligent molecules is only part of the challenge. As AI expands the space of possible therapeutics, the field must also confront a critical question: how do we reliably build, test, and manufacture increasingly complex biological designs? This session explores the emerging continuum from AI-designed molecules to manufacturable programmable therapeutics, examining how advances in sequence design, synthesis, delivery, and validation are translating computational insight into real-world medicines. The future of medicine isn’t static molecules - it’s intelligent, adaptive therapeutics engineered across the full stack, from algorithm to clinic.