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 👇
For anyone going to ICLR, we'll be presenting our poster at the GEM, LMRL, SciFM, XAI4Science, and MLMP workshops. Stop by if you're curious about bio interp!
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 👇
If you’ve ever
- thought AI protein folding is magical ✨
- wanted more than a pLDDT score 🔎
- or just think mech interp in bio is cool 🤓
then read the 🧵 👇 on our first paper towards interpretable protein structure prediction just accepted to workshops at ICLR
@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.
https://t.co/vIesWS3rkh
Congrats on the launch, @NithinParsan and @johnyang100!