A One-shot Framework for Directed Evolution of Antibodies

ICLR 2026 Conference Submission16802 Authors

Keywords: One-shot learning, matching, directed evolution, antibodies, structure-embeddings

TL;DR: AffinityEnhancer boosts antibody affinity from a single sequence, without structure or antigen data, by learning from matched high– vs. low-affinity pairs, capturing generalizable binding features and outperforming SOTA baselines.

Abstract: Improving antibody binding to an antigen without antibody-antigen structure information or antigen-specific data remains a critical challenge in therapeutic protein design. In this work, we propose \textbf{\textsc{AffinityEnhancer}}, a framework to improve the affinity of an antibody in a one-shot setting. In the \emph{one‐shot} setting, we start from a single lead sequence—never fine‐tuning on it or using its structure in complex with the antigen or epitope/paratope information—and seek variants that reliably boost affinity. During training, \textsc{AffinityEnhancer} utilizes pairs of related sequences with higher versus lower measured binding in a pan-antigen dataset comprising diverse “environments” (antigens) and a shared structure-aware module that learns to transform low‐affinity sequences into high‐affinity ones, effectively distilling consistent, causal features that drive binding. By incorporating pretrained sequence-structure embeddings and a sequence decoder, our method enables robust generalization to entirely new antibody seeds. Across multiple unseen internal and public seeds, \textsc{AffinityEnhancer} identifies key affinity enhancing mutations on the paratope, outperforms existing structure‐conditioned and inpainting approaches, achieving substantial (in silico) affinity gains in true, one‐shot experiments without ever seeing antigen data.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 16802