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 (opens in new tab)), together with curated training data, code and model weights for RF3 (https://github.com/RosettaCommons/modelforge (opens in new tab)) under a permissive BSD license.
RosettaFold3 (RF3) is a unified biomolecular modeling system that predicts 3D structures of proteins, nucleic acids, and small molecules within a single framework. Combining multimodal transformers and generative diffusion models, RF3 enables precise modeling of complex molecular assemblies such as protein–ligand, protein–DNA, and protein–RNA interactions.
