Antibody Generation via Redistributed Latent Diffusion

Andrey Shevtsov, Viacheslav Meshchaninov, Pavel Strashnov, Dmitry Vetrov

Keywords: Latent Diffusion, Protein Design, Diffusion Model, Antibody

TL;DR: Learning redistributed latent spaces makes latent diffusion practical for antibody sequence generation

Abstract:

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.

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Submission Number: 80