AbLWR: a context-aware listwise ranking framework for antibody-antigen binding affinity prediction via positive-unlabeled learning
Abstract
Motivation: Accurate prediction of antibody-antigen binding affinity is fundamental to therapeutic design and the rapid response to evolving pathogens. However, current computational approaches predominantly formulate this as a point-wise regression task. This paradigm struggles to discriminate subtle affinity differences among highly homologous variants and lacks the global context necessary to effectively prioritize top-tier candidates. Furthermore, the field is severely constrained by label sparsity. Although sequence data is abundant, experimentally verified affinities are scarce, leaving a massive pool of unverified antibody-antigen structural interactions unexploited by standard supervised paradigms.
Results: We propose AbLWR (Antibody-antigen binding affinity Listwise Ranking), a novel framework that reformulates the conventional affinity regression task as a listwise ranking problem to directly optimize candidate prioritization. To mitigate label sparsity, AbLWR incorporates a Positive-Unlabeled (PU) learning mechanism, leveraging a dual-level contrastive objective and meta-optimized label refinement to distill robust structural representations from vast unlabeled data. To accommodate antigenic variation, we employ a homologous antigen sampling strategy coupled with a Multi-Head Self-Attention (MHSA) module, which explicitly models inter-sample dependencies within training lists to capture subtle affinity nuances. Extensive evaluations demonstrate that AbLWR significantly outperforms state-of-the-art sequence-, structure-, and complex-based baselines. It improves the Full Rank Accuracy (FRA) by approximately 8% compared to the strongest baseline in randomized cross-validation, and exhibits strong robustness in out-of-distribution scenarios involving distinct antigen and antibody clusters. Case studies on Influenza Viruses and Human Interleukin-33 further validate its practical utility. Specifically, AbLWR successfully tracks affinity drops caused by antigenic drift and immediately captures optimal binders in simulated wet-lab screening.
Availability: Source code and datasets are freely available at https://github.com/Josie-xufan/AbLWR.
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BibTeX
@article{xu2026ablwr,
title = {{AbLWR: a context-aware listwise ranking framework for antibody-antigen binding affinity prediction via positive-unlabeled learning}},
author = {Fan Xu and Zhi-An Huang and Haohuai He and Yidong Song and Wei Liu and Dongxu Zhang and Yao Hu and Jibin Wu and Kay Chen Tan},
journal = {Bioinformatics},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag793},
url = {https://doi.org/10.1093/bioinformatics/btag793},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - AbLWR: a context-aware listwise ranking framework for antibody-antigen binding affinity prediction via positive-unlabeled learning
AU - Fan Xu
AU - Zhi-An Huang
AU - Haohuai He
AU - Yidong Song
AU - Wei Liu
AU - Dongxu Zhang
AU - Yao Hu
AU - Jibin Wu
AU - Kay Chen Tan
PY - 2026
DA - 2026/10/23
JO - Bioinformatics
DO - 10.1093/bioinformatics/btag793
UR - https://doi.org/10.1093/bioinformatics/btag793
AB - Motivation: Accurate prediction of antibody-antigen binding affinity is fundamental to therapeutic design and the rapid response to evolving pathogens. However, current computational approaches predominantly formulate this as a point-wise regression task. This paradigm struggles to discriminate subtle affinity differences among highly homologous variants and lacks the global context necessary to effectively prioritize top-tier candidates. Furthermore, the field is severely constrained by label sparsity. Although sequence data is abundant, experimentally verified affinities are scarce, leaving a massive pool of unverified antibody-antigen structural interactions unexploited by standard supervised paradigms. Results: We propose AbLWR (Antibody-antigen binding affinity Listwise Ranking), a novel framework that reformulates the conventional affinity regression task as a listwise ranking problem to directly optimize candidate prioritization. To mitigate label sparsity, AbLWR incorporates a Positive-Unlabeled (PU) learning mechanism, leveraging a dual-level contrastive objective and meta-optimized label refinement to distill robust structural representations from vast unlabeled data. To accommodate antigenic variation, we employ a homologous antigen sampling strategy coupled with a Multi-Head Self-Attention (MHSA) module, which explicitly models inter-sample dependencies within training lists to capture subtle affinity nuances. Extensive evaluations demonstrate that AbLWR significantly outperforms state-of-the-art sequence-, structure-, and complex-based baselines. It improves the Full Rank Accuracy (FRA) by approximately 8% compared to the strongest baseline in randomized cross-validation, and exhibits strong robustness in out-of-distribution scenarios involving distinct antigen and antibody clusters. Case studies on Influenza Viruses and Human Interleukin-33 further validate its practical utility. Specifically, AbLWR successfully tracks affinity drops caused by antigenic drift and immediately captures optimal binders in simulated wet-lab screening. Availability: Source code and datasets are freely available at https://github.com/Josie-xufan/AbLWR.
SN - 1367-4803
SN - 1367-4811
ER -