Biology Latest dispatch
Biohub's open-source ESM models learned the rules of protein biology from evolution alone — and designed working drug binders in days. Here's what the release means, and where it falls short.
Recent work in this line of enquiry.
Eli Lilly just wagered up to $2.75 billion on AI-designed medicines, yet not one AI-discovered drug has full FDA approval. Here's why 2026 is the year the field finally gets tested against clinical reality.
Insilico Medicine has pushed rentosertib, a drug whose target and molecule were both generated by AI, into a pivotal Phase III trial. It's the clearest test yet of whether AI drug discovery can produce medicines that work, not just headlines.
VirTues, a new foundation model published in Nature, learns one shared representation of spatial proteomics data across marker panels and imaging platforms—and finds biomarkers that predict immunotherapy response.
The 2026 Bundibugyo epidemic is now the largest Ebola outbreak ever recorded in the DRC, against a virus for which no licensed vaccine or therapy exists. It is also the first serious field test of AI-era biology's central promise — and the results are more sobering, and more interesting, than the hype allows.
The US government's July 28 policy prohibits dangerous gain-of-function research outright. But purely computational design of novel biological agents is explicitly not prohibited — it gets an interagency monitoring group instead. Here's what the policy says, why the split matters, and what it doesn't tell us.
A new peer-reviewed study cut AI-redesigned toxin genes into fragments and fed them to four commercial DNA screening tools. Two caught fragments as short as 50 nucleotides. Here's what that means ten weeks before a US screening deadline — and why the authors are still worried.
Genome sequencing published this week places the virus behind Central Africa's fastest-growing Ebola outbreak on a distinct branch of the Bundibugyo family tree — with an evolutionary signal nobody expected. Here is what that means for diagnostics built on the wrong prior, and where AI-assisted sequencing genuinely helps.
For about three years, the standard frontier-lab position on biology was defensive. Biology was the scary quadrant of the risk matrix.
