openeuler/rfdiffusion repository overview

⁠Quick reference

• The official RFdiffusion docker image.

• Maintained by: openEuler CloudNative SIG⁠.

• Where to get help: openEuler CloudNative SIG⁠, openEuler⁠.

⁠RFdiffusion | openEuler

Current RFdiffusion docker images are built on the openEuler⁠. This repository is free to use and exempted from per-user rate limits.

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:

• Motif Scaffolding
• Unconditional protein generation
• Symmetric oligomer generation (cyclic, dihedral, tetrahedral)
• Binder design
• Design diversification ("partial diffusion")

Learn more on RFdiffusion⁠.

⁠Supported tags and respective Dockerfile links

The tag of each rfdiffusion docker image is consist of the version of rfdiffusion and the version of basic image. The details are as follows

⁠Model Weights

Note: This image does not include model weights. You need to download them separately:

cd /opt/RFdiffusion/models
wget http://files.ipd.uw.edu/pub/RFdiffusion/6f5902ac237024bdd0c176cb93063dc4/Base_ckpt.pt
wget http://files.ipd.uw.edu/pub/RFdiffusion/e29311f6f1bf1af907f9ef9f44b8328b/Complex_base_ckpt.pt

For more model weights, see the RFdiffusion README⁠.

⁠Usage

Here, users can select the corresponding {Tag} by their requirements.

• Pull the openeuler/rfdiffusion image from docker

  docker pull openeuler/rfdiffusion:{Tag}
  

  Copy

• Run rfdiffusion container

  docker run -it --rm openeuler/rfdiffusion:{Tag}
  

  Copy

• Basic unconditional protein generation example

  cd /opt/RFdiffusion
  ./scripts/run_inference.py 'contigmap.contigs=[150-150]' inference.output_prefix=test_outputs/test inference.num_designs=10
  

  Copy

⁠Question and answering

If you have any questions or want to use some special features, please submit an issue or a pull request on openeuler-docker-images⁠.