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RosettaCommons/RFdiffusion: Code for running RFdiffusion

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RFdiffusion is an open source method for structure generation, with or without conditional information (a motif, target etc). It can perform a whole range of protein design challenges as we have outlined in the RFdiffusion paper. Things Diffusion can do Thanks to Sergey Ovchinnikov, RFdiffusion is available as a Google Colab Notebook if you would like to run it there! We strongly recommend reading this README carefully before getting started with RFdiffusion, and working through some of the examples in the Colab Notebook. If you want to set up RFdiffusion locally, follow the steps below: To get started using RFdiffusion, clone the repo: You'll then need to download the model weights into the RFDiffusion directory. Ensure that you have either Anaconda or Miniconda installed. You also need to install NVIDIA's implementation of SE(3)-Transformers Here is how to install the NVIDIA SE(3)-Transformer code: Anytime you run diffusion you should be sure to activate this conda environment by run

Image: Ian C. Haydon / UW Institute for Protein Design Description RFdiffusion is an open source method for structure generation, with or without conditional information (a motif, target etc). It can perform a whole range of protein design challenges as we have outlined in the RFdiffusion paper. Things Diffusion can do Motif Scaffolding Unconditional protein generation Symmetric unconditional generation (cyclic, dihedral and tetrahedral symmetries currently implemented, more coming!) Symmetric motif scaffolding Binder design Design diversification ("partial diffusion", sampling…

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