You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
A bundle of deep-learning packages for biomolecular structure prediction and design contributed to the Rosetta Commons.
The packages listed below are provided by contributors to the Rosetta Commons for free for non-commercial use under the Rosetta-DL Non-Commercial Licensing Agreement or a more permissive license.
For commercial licensing of this bundle, please contact the University of Washington CoMotion (license@uw.edu), which manages these packages on behalf of the Rosetta Commons member institutions. Licensing revenue supports the Rosetta Commons research community by funding conferences, workshops, summer interns and post-baccalaureate scholars, mini-grants, user support, documentation and code infrastructure and testing.
Rosetta-DL packages
RoseTTAFold - Protein folding - Code uses MIT license, data and weights use Rosetta-DL license
DeepAb - Antibody structure prediction - Code, data, and weights use Rosetta-DL license
MaSIF - Molecular surface interaction fingerprints - MIT license
trRosetta2 - Protein folding by Baker lab in CASP14 - MIT license
Protein-Seq-Des - Protein sequence design from a learned potential - BSD3 license
FvHallucinator - Hallucination of antibody structures - Rosetta-DL license
About
A bundle of deep-learning packages for biomolecular structure prediction and design contributed to the Rosetta Commons