Accelerating Biomolecular Modeling with AtomWorks and RF3
Tuesday September 23rd, 4:00-5:00pm EST | Nate Corley (PhD student, University of Washington)
Abstract: Deep learning methods trained on protein structure databases have revolutionized biomolecular structure prediction, but developing and training new models remains a considerable challenge. To facilitate the development of new models, we present AtomWorks: a broadly applicable data framework for developing state-of-the-art biomolecular foundation models spanning diverse tasks, including structure prediction, generative protein design, and fixed backbone sequence design. We use AtomWorks to train RosettaFold-3 (RF3), a structure prediction network capable of predicting arbitrary biomolecular complexes with an improved treatment of chirality that narrows the performance gap between closed-source AlphaFold3 (AF3) and existing open-source implementations. We expect that AtomWorks will accelerate the next generation of open-source biomolecular machine learning models and that RF3 will be broadly useful as a structure prediction tool. To this end, we release the AtomWorks framework (https://github.com/RosettaCommons/atomworks), together with curated training data, code and model weights for RF3 (https://github.com/RosettaCommons/modelforge) under a permissive BSD license.
Preprint: https://www.biorxiv.org/content/10.1101/2025.08.14.670328v1.full.pdf
Nate Corley is a PhD student in David Baker's laboratory at the University of Washington's Institute for Protein Design. Nate's research interests center in method development to support computational enzyme design - spanning structure prediction, de novo design, and filtering.