RFdiffusion
Design proteins, binders, and more with this protein diffusion model.
Overview
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 outlined in the RFdiffusion paper.
Run RFdiffusion on Neurosnap
The RFdiffusion online webserver allows anybody with a Neurosnap account to run and access RFdiffusion, no downloads required. Information submitted through this webserver is kept confidential and never sold to third parties as detailed by our strong Terms of Use and Privacy Policy.
Features
- 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 around a design)
Statistics
Neurosnap periodically calculates runtime statistics based on job execution data. These estimates provide a general guideline for how long your job may take, but actual runtimes can vary significantly depending on factors like input size or settings used.
| Statistic | Value |
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| Credit Usage Rate | loading... |
| Estimated Total Cost | loading... |
| Runtime Mean | loading... |
| Runtime Median | loading... |
| Runtime Standard Deviation | loading... |
| Runtime 90th Percentile | loading... |
| Runtime Longest | loading... |
API Request
Access RFdiffusion using the Neurosnap API by sending a request using any programming language with HTTP support. To safely generate an API key, visit the API tab of your overview page.
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Citations
Please cite the original work when using RFdiffusion in publications or research outputs.
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Watson, Joseph et al. “Broadly applicable and accurate protein design by integrating structure prediction networks and diffusion generative models” bioRxiv.Org, 10 December 2022, https://www.biorxiv.org/content/10.1101/2022.12.09.519842v1. |
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If you specified Cyclic Chains please cite the following as well: Rettie, S.A., Juergens, D., Adebomi, V. et al. Accurate de novo design of high-affinity protein-binding macrocycles using deep learning. Nat Chem Biol (2025). https://doi.org/10.1038/s41589-025-01929-w |
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Neurosnap Inc. (2022). Neurosnap: An online platform for computational biology and chemistry. Available at: https://neurosnap.ai/ |
Video Tutorial
The following youtube video describes how to use RFdiffusion using Neurosnap's online webserver. If you have any questions or want to suggest improvements for future tutorials please contact us here.