  Proteins are complex biomolecules that perform a variety of crucial functions
within living organisms. Designing and generating novel proteins can pave the
way for many future synthetic biology applications, including drug discovery.
However, it remains a challenging computational task due to the large modeling
space of protein structures. In this study, we propose a latent diffusion model
that can reduce the complexity of protein modeling while flexibly capturing the
distribution of natural protein structures in a condensed latent space.
Specifically, we propose an equivariant protein autoencoder that embeds
proteins into a latent space and then uses an equivariant diffusion model to
learn the distribution of the latent protein representations. Experimental
results demonstrate that our method can effectively generate novel protein
backbone structures with high designability and efficiency. The code will be
made publicly available at
https://github.com/divelab/AIRS/tree/main/OpenProt/LatentDiff
