🚀 ROCKET

Refining Openfold predictions with Crystallographic / Cryo-EM LiKElihood Targets

Fit atomic models to data using AlphaFold as a prior Check out our paper, AlphaFold as a Prior: Experimental Structure Determination Conditioned on a Pretrained Neural Network, in Nature Methods to learn how it works. You can find detailed documentation and walk-through tutorials in our GitBook and our SBGrid webinar. Installation Ready to get going? Head over to our docs for a full walk-through on: 📦 Installing ROCKET → Questions? Found a bug or have a feature request? Please open an issue on GitHub. For science questions — methodology, interpreting results, or general discussion — head over to the rs-station Discourse.

Citing our work

@article{fadini2026,
  title={AlphaFold as a prior: experimental structure determination conditioned on a pretrained neural network},
  author={Fadini, Alisia and Li, Minhuan and McCoy, Airlie J. and Banjara, Suresh and Okumura, Hiroki and Napier, Eve and Fontana, Pietro and Khan, Amir R. and Jovine, Luca and Terwilliger, Thomas C. and Read, Randy J. and Hekstra, Doeke R. and AlQuraishi, Mohammed},
  journal={Nature Methods},
  volume={23},
  pages={785--795},
  year={2026},
  doi={10.1038/s41592-026-03047-4}
}