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In two new papers we have found that the ESM2 language model generalizes beyond natural proteins, and enables programmable generation of complex and modular protein structures.

Alex Rives

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202,826 次观看 • 3 年前 •via X (Twitter)

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Alex Rives 的头像

Alex Rives3 年前

ESM2 learns the design principles of proteins. With @uwproteindesign we experimentally validated 152 ESM2 designs, including de novo generations outside the space of natural proteins (<20% sequence identity to known proteins). 📄Read the paper here:

Alex Rives 的头像

Alex Rives3 年前

We implemented a high level programming language for generative protein design with ESM2. This made it possible to program the generation of large proteins and complexes with intricate and modular structures. 📄Read the paper here:

Alex Rives 的头像

Alex Rives3 年前

Thread by @TomSercu on how language models generalize to de novo proteins ⬇️

Alex Rives 的头像

Alex Rives3 年前

🧵@BrianHie on a high level programming language for generative protein design

The Guy 的头像

The Guy3 年前

Well, that probably won't have any significant long term impacts. Holy Mackerel! Are you kidding me! Top of the line, cutting edge work guys. Wow! Congratulations. I'm gonna be over here freakin' out for a while.

shb 的头像

shb3 年前

in the appendix it looks like you used the 650M param version for this... why not one of the bigger models? didn't they train up to 15B?

decipher 的头像

decipher3 年前

congratulations, I was expecting novel approaches to solve inverse folding-docking problem after esm & aplhafold2

LUIS VIZCAYA 的头像

LUIS VIZCAYA3 年前

Sometimes I have no words for the amazing progress we are having in AI

Ifigeneia Apostolopoulou 的头像

Ifigeneia Apostolopoulou3 年前

it's the class of problems to which generative models should be applied ;)

Peter Morgan 的头像

Peter Morgan3 年前

Phenomenal

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