RFdiffusion3 is here! We train a general network that explicitly models every atom and use it to design active enzymes and DNA binders.

Tremendous team effort with Jasper, Rohith, Raktim, Rafi, Yanjing, Paul, Jonathan, and many others!

Check it out: lnkd.in/eiUFfJaM.

RosettaFold 3 is here! 🧬🚀

AtomWorks (the foundational data pipeline powering it) is perhaps the really most exciting part of this release!

Congratulations @simonmathis.bsky.social and team!!! ❤️

bioRxiv preprint: www.biorxiv.org/content/10.1...

`atomworks.ml` on the other hand offers advanced dataset featurization and sampling for deep learning workflows, all operating on the canonical AtomArray object from @biotite_python so that all transforms are traceable and generalizable between models. 4/6

AtomWorks has two main components: atomworks.io takes a file (cif, sdf, ...) and does parsing, cleaning and more. You can also look at your structures in a notebook or via PyMol thanks to pymol-remote, so you can directly inspect if your code does what you want! 3/6

AtomWorks is out! Building upon @biotite_python, we built a toolkit for all things biomolecules and trained RF3 with it. All open-source, test it via `pip install atomworks`!

AtomWorks: github.com/RosettaCommo...
RF3: github.com/RosettaCommo...
Paper: tinyurl.com/y2w4z65b

1/6

(7/7)
We also want to give a shout-out to [Biotite](www.biotite-python.org/latest/), which is the bedrock of our approach. Biotite has made our framework vastly more performant and flexible. We're excited for what's next!

(6/7)
Frank DiMaio both directed and carried the team - he deserves the most recognition

(5/7)
It's been the pleasure my life to work alongside @simonmathis.bsky.social, @rkrishna3.bsky.social, @kinasekid.bsky.social , and so many other unbelievably talented individuals on this project. And extra credit to @kdidi.bsky.social for jumping into the frenzy to bring this work across the line

(4/7)
There's no reason every researcher in the BioML space re-invents the wheel ever time they train a structure-based model. The complexity of loading and annotating biomolecular data for machine learning applications should be done once, and done right — that was our goal with AtomWorks

(3/7)
We're also thrilled to release AtomWorks, which we used as the foundation for not only RF3, but also RF2AA, LigandMPNN, ProteinMPNN, and a design model — all by just swapping out a handful of modular components we call Transforms (just like Torchdata, for those familiar)

(2/7)
We're thrilled to share RF3 fully open-source with the community — it handles chirality better than any other model, it supports arbitrary atomic templating (which we included at train-time), and it narrows the open-source gap to AF3

(1/7)
Training biomolecular foundation models shouldn't be so hard. And open-source structure prediction is important. So today we're releasing two software packages: AtomWorks and RosettaFold3 (RF3)

[https://www.biorxiv.org/content/10.1101/2025.08.14.670328v2](www.biorxiv.org/content/10.1...)