Guided Generation for Developable Antibodies
Journal:
arXiv
Published Date:
Jul 3, 2025
Abstract
Therapeutic antibodies require not only high-affinity target engagement, but
also favorable manufacturability, stability, and safety profiles for clinical
effectiveness. These properties are collectively called `developability'. To
enable a computational framework for optimizing antibody sequences for
favorable developability, we introduce a guided discrete diffusion model
trained on natural paired heavy- and light-chain sequences from the Observed
Antibody Space (OAS) and quantitative developability measurements for 246
clinical-stage antibodies. To steer generation toward biophysically viable
candidates, we integrate a Soft Value-based Decoding in Diffusion (SVDD) Module
that biases sampling without compromising naturalness. In unconstrained
sampling, our model reproduces global features of both the natural repertoire
and approved therapeutics, and under SVDD guidance we achieve significant
enrichment in predicted developability scores over unguided baselines. When
combined with high-throughput developability assays, this framework enables an
iterative, ML-driven pipeline for designing antibodies that satisfy binding and
biophysical criteria in tandem.
