1/ We're excited to announce that @rs-station.bsky.social is joining the Open Molecular Software Foundation @omsf.io!

Huge resource for the community. Turning compute into data to train models to turn more compute into data...the cycle must go on bsky.app/profile/moal...

Mohammed AlQuraishi@moalquraishi.bsky.social · 5mo ago

We're also releasing a massive self-distillation set, a key ingredient in training AF3, comprising millions of diverse MSAs and structures. We estimate its cost to near $20M, representing perhaps the largest compute investment by an academic effort for a biological dataset. 6/9

New OpenFold3 preview out! (OF3p2) It closes the gap to AlphaFold3 for most modalities. Most critically, we're releasing everything, including training sets & configs, making OF3p2 the only current AF3-based model that is functionally trainable & reproducible from scratch🧵1/9

Excited to share PoET-2, our next breakthrough in protein language modeling. It represents a fundamental shift in how AI learns from evolutionary sequences. 🧵 1/13

It's long seemed that molecular biology is a natural home for ML interpretability research, given the maturity of human-constructed models of biological mechanisms—permitting direct comparison with their ML-derived counterparts—unlike vision and NLP. Our first foray below👇.

Etowah Adams@etowah0.bsky.social · last yr.

Can we learn protein biology from a language model? In new work led by @liambai.bsky.social and me, we explore how sparse autoencoders can help us understand biology—going from mechanistic interpretability to mechanistic biology.

This might be the best paper on applying sparse autoencoders to protein language models. The authors identify how neural networks trained on amino acid sequences "discover" different features, some specific to individual protein families, other for substructures www.biorxiv.org/content/10.1...

Can we learn protein biology from a language model? In new work led by @liambai.bsky.social and me, we explore how sparse autoencoders can help us understand biology—going from mechanistic interpretability to mechanistic biology.