De Novo Design of Antigen-Specific Antibodies Using Structural Constraint-Based Generative Language Model

Yuran Jia, Bing He, Tianxu Lv, YangXiao, Tianyi Zhao, Jianhua Yao

Track: Biology: datasets and/or experimental results

Nature Biotechnology: Yes

Keywords: Antibody engineering, De novo design, Iterative optimization, Large language model

Abstract: Despite significant advances in computational antibody design, the limited availability of high-quality binding data continues to constrain the exploration of diverse antibody syntax and uncharted evolutionary landscapes. To overcome these challenges, we developed PALM-PA (Pre-trained Antibody Generative Large Language Model–Preference Alignment), which integrates antibody linguistic patterns with structural constraints to explore novel sequence spaces. Experimental validation on influenza A hemagglutinin and programmed death-ligand 1 (PD-L1) demonstrated nanomolar binding affinities (30.2 nM and 1.29 nM, respectively), underscoring the feasibility of using structure-guided language models for the de novo design of antibodies.

Anonymization: This submission has been anonymized for double-blind review via the removal of identifying information such as names, affiliations, and identifying URLs.

Presenter: ~Yuran_Jia1

Format: Maybe: the presenting author will attend in person, contingent on other factors that still need to be determined (e.g., visa, funding).

Funding: No, the presenting author of this submission does *not* fall under ICLR’s funding aims, or has sufficient alternate funding.

Submission Number: 37