science advances are typically preceded by measurement advances. the closer the lab model is to the underlying phenomenon the faster we typically progress. this is a good essay laying out that logic for organoids for alzheimers in particular but i think applies to many brain diseases

inspired by the anthropic post, i made a protein font! how does it work? when you design by differentiating backward through a folding model like esm-fold2 you can ask that the distogram of the folded protein looks like something you want. a little bit of help from @ChatGPT and now we have a protein font (fable, of course, didn't want to help me). this was not fully automated, i had to dial in some details like the protein sizes etc, but jointly me and codex got it working. i put the whole pipeline on my github below so you can mess with it too. bring your own gpus, its kind of expensive.

what is happening here: claude used existing open source tools and orchestrated them together to do a protein design campaign (with a big guide prompt!) this is a win for claude because doing this orchestration requires a lot of biological judgement and knowledge however, the tools are doing A LOT of the lifting here so it is just as important to give credit where it is due to the open source academic community. this is not just typing into claude "hey give me a binder to X" and have it spit out a protein sequence. this also means that the limitations of these tools need to be thought about, and in particular we know that all of these tools fall down when faced with targets that are outside of the range of their training data (so we can't really design binders against most targets with them) so... this is cool but you really need to think about it in context

maybe i am lucky but across my work (ai+bio research) and relevant hobbies (music, motorsports) there have been 0 model releases where my reaction has been “oh no, the machine is somehow reducing my enjoyment of my craft.”