Structure-Aware Antibody Design with Affinity-Optimized Inverse Folding

Xinyan Zhao; Yi-Ching Tang; Rivaaj Monsia; Victor J. Cantu; Ashwin Kumar Ramesh; Xiaozhong Liu; Zhiqiang An; Xiaoqian Jiang; Yejin Kim

arXiv · 2512.17815

Structure-Aware Antibody Design with Affinity-Optimized Inverse Folding

Xinyan Zhao·Yi-Ching Tang·Rivaaj Monsia·Victor J. Cantu·Ashwin Kumar Ramesh·Xiaozhong Liu·Zhiqiang An·Xiaoqian Jiang·Yejin Kim

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Abstract

Motivation: The clinical efficacy of antibody therapeutics critically depends on high-affinity target engagement, yet laboratory affinity-maturation campaigns are slow and costly. In computational settings, most protein language models (PLMs) are not trained to favor high-affinity antibodies, and existing preference optimization approaches introduce substantial computational overhead without clear affinity gains. Therefore, this work proposes SimBinder-IF, which converts the inverse folding model ESM-IF into an antibody sequence generator by freezing its structure encoder and training only its decoder to prefer experimentally stronger binders through preference optimization. Results: On the 11-assay AbBiBench benchmark, SimBinder-IF achieves a 55 percent relative improvement in mean Spearman correlation between log-likelihood scores and experimentally measured binding affinity compared to vanilla ESM-IF (from 0.264 to 0.410). In zero-shot generalization across four unseen antigen-antibody complexes, the correlation improves by 156 percent (from 0.115 to 0.294). SimBinder-IF also outperforms baselines in top-10 precision for ten-fold or greater affinity improvements. A case study redesigning antibody F045-092 for A/California/04/2009 (pdmH1N1) shows that SimBinder-IF proposes variants with substantially lower predicted binding free energy changes than ESM-IF (mean Delta Delta G -75.16 vs -46.57). Notably, SimBinder-IF trains only about 18 percent of the parameters of the full ESM-IF model, highlighting its parameter efficiency for high-affinity antibody generation.

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Xinyan Zhao, Yi-Ching Tang, Rivaaj Monsia, Victor J. Cantu, Ashwin Kumar Ramesh, Xiaozhong Liu, Zhiqiang An, Xiaoqian Jiang, Yejin Kim. 2025-12-19. Structure-Aware Antibody Design with Affinity-Optimized Inverse Folding. https://arxiv.org/abs/2512.17815

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