newsletter

Today in AI for life science: the AlphaFold Database opens millions of protein-complex predictions toward a human interactome, interpretable foundation models start mapping the cell’s regulatory wiring, and honest tests ask whether the diagrams are real.

Today in AI for life science: AI drug discovery hits its reckoning year — billions committed, zero approvals — tougher economics reshape the field, and the human cell reference map matures underneath it all.

Today in AI for life science: the big models get plumbed into real scientific data via connectors, AI moves into RNA drug design, and machine learning starts designing the CRISPR edit itself.

Today in AI for life science: multi-agent ‘AI co-scientists’ land in Nature with real biomedical targets, an AI agent takes over the cryo-EM pipeline and finds a new structural state, and validation stays the moat.

Today in AI for life science: scientists simulate an entire minimal cell’s life cycle in 4D, the field debates mechanistic models versus learned foundation models, and open-weight agent models get cheap enough to run science on.

Today in AI for life science: genome foundation models learn to read and write DNA, an AI infers a spatial protein map from an ordinary pathology slide, and machine learning moves into 3D organoids — three layers of the virtual cell.

Today in AI for life science: industry pours billions into agentic labs and biology foundation models, virtual-cell models keep scaling while simple baselines stay stubbornly competitive, and image-based profiling races toward open, assay-agnostic foundation models.

Today in AI for life science: Biohub’s protein world model designs cancer binders that work in the wet lab, ’thinking microscopes’ turn instruments into co-scientists, and a sharp reminder that predictions still have to survive the bench.

Today in AI for life science: CZI’s rBio learns to reason about cells with a virtual cell as its verifier, self-driving labs cross into industrial infrastructure, and a sharp reality check on what virtual-cell models still can’t do.

Today in AI for life science: agent benchmarks converge on a lesson we felt firsthand — the system around the model matters as much as the model — virtual-cell ‘world models’ arrive with a push for reproducibility standards, and the lab runs its first live agent-driven experiment.