Most small molecule drugs and biologics miss a large group of important receptors in the body. These receptors, known as GPCRs, play big roles in major diseases, but they are unusually hard to target in a precise and safe way. Skape Bio is trying to change that by using artificial intelligence to design an emerging class of therapies known as mini proteins that can fit these receptors snugly and control them in ways traditional drugs often can’t. Christoffer Norn, co‑founder and CEO of Skape Bio, discusses how mini proteins share strengths of both small molecules and biologics while avoiding some of their weaknesses, why the company’s approach could open up new therapeutic opportunities, and how Skape Bio is building a broad pipeline of differentiated GPCR‑targeting therapies.

AI’s promise in drug discovery is being held back by a simple but stubborn problem: the field doesn’t have enough of the right kind of lab-generated data to train good models. Public databases and scattered studies give a patchy view of how proteins interact, mostly showing successful interactions and using different methods and conditions, which makes it hard for AI systems to learn broad rules or understand what doesn’t work. A-Alpha Bio tackles this data gap with AlphaSeq, a lab platform that can test around a million protein pairs in one experiment under the same conditions, producing rich, consistent information on both hits and misses that’s well suited for AI. David Younger, co-founder and CEO of A-Alpha Bio, talks about why far more lab data and infrastructure will be needed than most people expect, why current public datasets fall short, and why the company’s business model is built around providing data and services rather than developing its own drugs.

Multi-specific antibodies promise to unlock complex biology that conventional monoclonals can’t touch, but their added mechanisms of action also introduce safety and developability risks. These antibodies—especially T‑cell engagers—behave differently from traditional monospecific antibodies, and seemingly minor architectural tweaks can cause disproportionate shifts in potency, selectivity, and cytokine release. LabGenius is trying to turn multi-specific design from an intuition-driven art into a genuine engineering discipline by generating proprietary data at scale and feeding them back into machine learning models. Angus Sinclair, chief scientific officer of LabGenius, discusses why many safety failures in early solid-tumor T‑cell engagers were effectively locked in at design, how the company’s AI platform engineers multi-specific T‑cell engagers that are both potent and selective in solid tumors, and where AI is actually adding value in multi-specific design today.