Guided Generation for Developable Antibodies

TL;DR

This paper presents a guided diffusion model that generates antibody sequences optimized for developability, integrating biophysical constraints to improve therapeutic antibody design.

Contribution

It introduces a novel guided discrete diffusion model with SVDD for antibody generation, combining natural sequence features with developability optimization.

Findings

Model reproduces natural antibody features

Achieves higher developability scores with guidance

Enables iterative ML-driven antibody design

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.