Building on previous AI models from the Baker Lab and DiMaio Lab at the IPD and the Barzilay Lab and Jaakkola Lab at MIT, RFdiffusion2 uses deep learning to create protein structures tuned to catalyze specific chemical reactions. It works from a simple input—a desired chemical transformation—and generates complete backbones with active sites that can carry it out

We’re excited to share that the beta version of the official RFdiffusion documentation is now live!
This resource is designed to support users at all levels – from those new to RFdiffusion to experienced researchers looking to get the most out of the tool.

A typical binder design task, generating an approximately 80–150 residue binder, each backbone design using RFdiffusion took approximately 25–30 s when run on a single NVIDIA RTX2080 or A4000 GPU, using one CPU core and approximately 8 GB of RAM