newsletter

Today in AI for life science: AI-designed proteins become new tools to see inside living cells, generative models write genomes on demand, and a synthesis breakthrough finally lets labs build what AI dreams up.

Today in AI for life science: models move past static structures to predict proteins as moving ensembles, that frontier hits a data wall, and self-evolving agents repurpose drugs for rare diseases — where the wet lab both confirms and refutes.

Today in AI for life science: vision-language models turn microscopes into conversational instruments, a reality check on the gap between seeing and understanding an image, and multimodal pathology models fusing slides with clinical text at scale.

Today in AI for life science: generative AI can finally design proficient enzymes, autonomous biofoundries close the design-build-test loop, and a census of 1,196 biology-AI models finds safeguards remain the rare exception.

Today in AI for life science: cell-segmentation foundation models reach a superhuman generalist in Cellpose-SAM, a 2026 benchmark shows no single model wins everywhere, and the frontier pushes into 3D light-sheet microscopy.

Today in AI for life science: the Virtual Cell Challenge’s verdict is in — hybrids and curated data beat pure scale — foundation models reach the brain, and the field’s quiet lesson is that interoperable data, not model size, is the bottleneck.

Today in AI for life science: a new protocol lets AI agents safely drive lab instruments, autonomous labs run experiments at a scale that outpaces the rules, and foundation models reach a new frontier — the microbiome.

Today in AI for life science: a temporal AI model predicts how cells age and finds validated aging drivers, chemical reprogramming reverses senescence without the cancer risk, and aging emerges as the ultimate dynamic virtual-cell problem.

Today in AI for life science: a general-purpose ‘AI biologist’ matches human researchers across 25 subfields, cell models generalize across species, and the newest frontier model ships bio-tuned — and on a government leash.

Today in AI for life science: a cell-language model proposes a cancer lead that holds up in the wet lab, agents turn plain-language requests into reproducible image analysis, and microscopes start deciding what to image themselves.