Comments
currentscurrents t1_ja4qide wrote
Full PDF: http://cdn.fraserlab.com/publications/2023_madani.pdf
Code and models: https://github.com/salesforce/progen
st8ic t1_ja60y60 wrote
ah yes, salesforce.com, the premier synthetic biology company
Measurex2 t1_ja6ca1j wrote
They've been going after the Healthcare vertical heavily for the last decade, and Benioff has donated like half a billion to hospitals and research from his own wallet.
https://www.salesforce.com/solutions/industries/healthcare/pharma/health-care-innovation/
I buy every dip in Salesforce because they keep delivering in some many interesting ways. The news of the co-CEO and all their product and department leads leaving dropped their stock in Q4. It's up 25% since then.
PigException t1_ja6o0ms wrote
ARKK is also up since then.
walk-the-rock t1_ja6sp5s wrote
Richard Socher (chief scientist at salesforce) is one of the world leaders in natural language processing within ML/AI
hackinthebochs t1_ja6tpln wrote
At what point do we stop calling it a language model?
Centigonal t1_ja708qj wrote
I think it's fair to call RNA a language.
_der_erlkonig_ t1_ja74633 wrote
Socher's been gone from Salesforce for years
Available_Lion_652 t1_ja7rsg9 wrote
It s obvious. Because they memorize text
Franc000 t1_ja894hj wrote
Who cares if the research is actually good?
walk-the-rock t1_ja8w92x wrote
oh my mistake
the rest of it stands though
uhules t1_jadfp2z wrote
At the point where it stops being a P(w|h) estimator.
MysteryInc152 OP t1_ja3hozj wrote
>Deep-learning language models have shown promise in various biotechnological applications, including protein design and engineering. Here we describe ProGen, a language model that can generate protein sequences with a predictable function across large protein families, akin to generating grammatically and semantically correct natural language sentences on diverse topics. The model was trained on 280 million protein sequences from >19,000 families and is augmented with control tags specifying protein properties. ProGen can be further fine-tuned to curated sequences and tags to improve controllable generation performance of proteins from families with sufficient homologous samples. Artificial proteins fine-tuned to five distinct lysozyme families showed similar catalytic efficiencies as natural lysozymes, with sequence identity to natural proteins as low as 31.4%. ProGen is readily adapted to diverse protein families, as we demonstrate with chorismate mutase and malate dehydrogenase.