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A First Step Towards Interpretable Protein Structure Prediction
With SAEFold, we enable mechanistic interpretability on ESMFold, a protein structure prediction model, for the first time.
Watch @NithinParsan demo a case study here w/ links for paper & open-source code 👇
Just published our state-of-the-art polygenic scoring results.
We're emphasizing our benchmarking methods, especially how we split.
medrxiv.org/content/10.64898…
Longer 🧵 breaking down our paper 👇
xitter.gq/johnyang100/status/190…
Reticular (YC F24) retweeted
Replying to @johnyang100 @NithinParsan
shoutout @liambai21 and @etowah0 for beating us to the punch on translating mech interp to protein language models. excited to see where these techniques will lead given the dense amt of coevolutionary + structure info learned by these models.
Incredibly excited to launch publicly as part of @ycombinator's Fall 2024 Batch. Send us a DM if you're in the interpretability space or working in biotech. @johnyang100 and @NithinParsan
YC F24's @ReticularAI makes protein AI models controllable and interpretable to help steer protein design with limited biological data, reducing costly validation cycles.
ycombinator.com/launches/ME3…
Congrats on the launch, @NithinParsan and @johnyang100!



