Antibody Generation via Redistributed Latent Diffusion

Generating antibody sequences is challenging because they combine conserved framework regions with hypervariable loops. Latent diffusion is attractive for this task since it enables flexible conditioning and bidirectional generation. But standard approaches fail. Global noise schedules treat all positions equally, so models learn the predictable frameworks well while the diverse loops remain poorly captured. We address this by learning a latent space that redistributes information evenly, allowing standard diffusion to succeed where it previously failed. On organism-conditioned generation across six species, our approach achieves 10× lower Fréchet Distance than latent diffusion without redistribution. It supports chain-type control, loop infilling, and paired-chain generation. Validation across five protein encoders confirms the method is encoder-agnostic. These results establish latent diffusion as a practical tool for antibody sequence design.