Weekly intelligence on AI reshaping pharma and biotech — for the people making the decisions. Short, opinionated, written from inside the field.
The UK's OpenBind initiative takes aim at the structural data gap that's quietly limited AI drug discovery since AlphaFold. Also: the FDA's AI-triage inspection pilot, Novo's enterprise OpenAI bet, and Insilico's empirical case against general-purpose models.
OpenBind releases 800 protein-ligand binding measurements and a free predictive model — the first serious public-sector attempt to build the PDB equivalent for drug-protein interactions. Also: the FDA ran 46 AI-triage one-day inspections before anyone announced the program, Novo Nordisk bets its recovery on an enterprise-wide OpenAI partnership, and Insilico's MMAI data makes the clearest empirical case yet that general LLMs simply don't work for drug discovery.
Isomorphic Labs gears up for human trials, Owkin's "AI scientist" lands at three top-ten pharmas, and Bessemer makes the case that biology-native data is the only durable advantage.
Isomorphic Labs starts moving AI-designed molecules into humans, and the read on the timeline tells you more about pharma's feedback loops than the model itself. Plus Owkin going agentic at scale and a Bessemer thesis worth taking seriously.
Eli Lilly's record partnership with Insilico Medicine is the largest AI drug-discovery deal in history. Why now, what it actually buys, and what the second-derivative effects are.
Lilly committed $2.75 billion to Insilico Medicine — the biggest AI drug-discovery deal so far. Insilico's platform has compressed certain discovery timelines from years to months, validated by rentosertib's clinical readouts in pulmonary fibrosis. The deal also reframes who gets to call themselves AI-first.