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Briefings and ideas about Protein design from AI podcast discussions and the research they cite, with links to the original sources.
Biohub's Alex Rives expects pretraining, elicitation and RLVR to carry over from language models to biology, but says verification must happen in the lab. He is unsure whether biology and language models will share one latent space.
Biohub science head Alex Rives argues that no lab animal can ever be a perfect stand-in for people. He says an AI model trained across many species, as protein models were trained across evolution, could predict human biology far better.
Ian Quigley says co-folding models still place a molecule where a pocket was digitally destroyed, so Leash Bio engineers protein mutants for negative data. In a 20-protein exploratory analysis, its model's attention spread more evenly after retraining on mutants plus about 40% more ordinary data.
Google DeepMind's Pushmeet Kohli said scientists need models with calibrated confidence and known limits more than human-readable inner workings. Biohub's Sal Candido said protein models hold knowledge not yet unlocked.
Biohub's Sal Candido and Google DeepMind's Pushmeet Kohli argued on a panel that scaling laws in biology must be found, not assumed, and that the problem should decide whether to build better models or gather new data.
Google DeepMind's Pushmeet Kohli said AlphaFold reproduces structures deposited in the Protein Data Bank, not proteins' shifting shapes. He sees raw cryo-EM images as one possible route to that motion, though his own attempt needs more work.
Author Garrison Lovely argues that building AI to replace all human labor is a choice that can be stopped. He says medical benefits can be pursued directly through drug prizes and Warp Speed-style programs.
MIT's Andrew McAfee said he would back legal limits on AI if AI hijacked San Francisco's Waymos, crashed them into people and couldn't be shut down for a week or a month. Panelists seeking a halt said by then it may be too late.
Google DeepMind's AlphaGenome Atlas precomputes predicted molecular effects of every possible single-letter change in the human genome. A Moonshots panelist cautioned that what combinations of variants mean still needs outcome data.
David Friedberg argued AI can screen a million protein ideas on computers to find ones that might reverse cell aging, without a full theory of the cell. The eye work he cited has reached a first safety trial in people, not restored sight.