Fast protein backbone generation with SE(3) flow matching

TL;DR

FrameFlow is a novel SE(3) flow matching approach that significantly accelerates protein backbone generation, reducing sampling steps and improving designability, thus enabling more efficient de novo protein design.

Contribution

It adapts flow matching to SE(3) for protein generation and modifies training to learn vector fields effectively, achieving faster and more designable protein samples.

Findings

Five times fewer sampling timesteps than FrameDiff.

Twofold improvement in designability.

High-quality protein samples generated efficiently.

Abstract

We present FrameFlow, a method for fast protein backbone generation using SE(3) flow matching. Specifically, we adapt FrameDiff, a state-of-the-art diffusion model, to the flow-matching generative modeling paradigm. We show how flow matching can be applied on SE(3) and propose modifications during training to effectively learn the vector field. Compared to FrameDiff, FrameFlow requires five times fewer sampling timesteps while achieving two fold better designability. The ability to generate high quality protein samples at a fraction of the cost of previous methods paves the way towards more efficient generative models in de novo protein design.