Protein Backbone Generation

Protein backbone generation aims to computationally design novel protein structures, crucial for drug discovery and synthetic biology. Recent research heavily utilizes flow-matching models, particularly those leveraging SE(3) equivariance to accurately represent 3D rotations and translations of protein backbones, alongside diffusion models and latent diffusion models that operate in lower-dimensional latent spaces. These methods are improving the diversity, designability, and novelty of generated structures, often incorporating techniques like motif-scaffolding and reinforced finetuning to guide the generation process. This progress facilitates the design of proteins with specific functionalities and accelerates the development of new therapeutics and biotechnologies.