Evidence map›Paper›PMID 40832246›Full record

ArticlebioRxiv : the preprint server for biology2025

Accelerating Biomolecular Modeling with AtomWorks and RF3.

Nathaniel Corley, Simon Mathis, Rohith Krishna, Magnus S Bauer, Tuscan R Thompson, Woody Ahern, Maxwell W Kazman, Rafael I Brent, Kieran Didi, Andrew Kubaney and 20 more

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

30 authors.

Nathaniel CorleyInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Simon MathisInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Rohith KrishnaInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Magnus S BauerInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Tuscan R ThompsonInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Woody AhernInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Maxwell W KazmanInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Rafael I BrentInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Kieran DidiInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Andrew KubaneyInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Lilian McHughInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Arnav NagleInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Andrew FavorInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Meghana KshirsagarMicrosoft, AI for Good lab, Redmond, WA 98052, USA.
Pascal SturmfelsInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Yanjing LiInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Jasper ButcherInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Bo QiangInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Lars L SchaafCavendish Laboratory, Department of Physics, University of Cambridge, Cambridge CB3 0HE, UK.
Raktim MitraInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Katelyn CampbellInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Odin ZhangInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Roni WeissmanInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Ian R HumphreysInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Qian CongEugene McDermott Center for Human Growth and Development, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Jonathan FunkInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Shreyash SonthaliaInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Pietro LiòDepartment of Computer Science, University of Cambridge, Cambridge CB3 0FD, UK.
David BakerInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.
Frank DiMaioInstitute for Protein Design, University of Washington, Seattle, 98105, Washington, USA.ORCID 0000-0002-7524-8938

Funding

Project 5: mRNA and mRNA-launched nanoparticle vaccinesU19AI181881 · NIAID · UNIVERSITY OF WASHINGTON · PI KING, NEIL · 2024 to 2024
$41.1M
Protein structure determination from low-resolution experimental dataR01GM123089 · NIGMS · UNIVERSITY OF WASHINGTON · PI DIMAIO, FRANK P · 2017 to 2025
$2.6M
Gates Foundation INV-010680NIAID NIH HHS U19 AI181881NIGMS NIH HHS R01 GM123089
6 · The paper itself

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.

Identifiers

PMID40832246
PMCPMC12363939

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.