AbICL is proposed, an ICL framework for antigen-specific antibody affinity ranking that combines a pretrained structural encoder with a context ranking head and is trained with an episodic meta-training strategy that enables the model to leverage support demonstrations for test-time adaptation without gradient updates.
Abstract
Accurate ranking of antibody candidates according to their binding affinity is essential for therapeutic antibody discovery. However, existing methods treat affinity comparisons independently and ignore the contextual information encoded in other labeled comparisons, limiting their ability to capture antigen-specific binding landscapes. For many target antigens, a small number of experimentally characterized affinity comparisons are often available. An important question is whether the model can exploit these existing comparisons to infer antigen-specific ranking patterns that facilitate subsequent affinity ranking. This form of learning from labeled demonstrations closely resembles the paradigm of In-Context Learning, motivating us to revisit antibody affinity ranking from an ICL perspective. To this end, we propose AbICL, an ICL framework for antigen-specific antibody affinity ranking. AbICL combines a pretrained structural encoder with a context ranking head and is trained with an episodic meta-training strategy that enables the model to leverage support demonstrations for test-time adaptation without gradient updates. Experiments on the AbRank benchmark demonstrate that AbICL consistently outperforms existing ranking baselines across almost all data splits and evaluation benchmarks. Further analysis shows that the value of contextual demonstrations depends on how well they match the target inference task, and becomes increasingly pronounced under distribution shift and fine-grained affinity discrimination. These findings highlight the potential of ICL as an effective paradigm for antigen-specific antibody affinity ranking, particularly in challenging settings where a single global ranking function is insufficient.
AAMFM, an Antigen-specific Antibody Multimodal Foundation Model that learns unified representations of antibody sequences and structures conditioned on antigen context, achieves state-of-the-art performance in functional antibody design, revealing its potential for antigen-specific antibody engineering.
Xiaoliang Shi, Zichen Wang, Runze Ma et al.· 0 citations
This work designs a biological prior-guided feature fusion framework that integrates pseudo-structural epitope knowledge and CDR-specific attention mechanisms via a mixture-of-experts architecture to effectively capture complex binding landscapes in antibody screening and drug residence time analysis.
G. Luo, Junkai Wang, Sizhe Zhang et al.· Bioinformatics· 0 citations
This work proposes MochiBind, a sequence-only pairwise binding affinity predictor, and benchmark it against structure-derived baselines such as Boltz-2, GeoDock, and Graphinity, suggesting that sequence-based approaches can match or surpass structure-based models in generalization.
Yunrui Li, Yue Zhao, K. Sonmez et al.· iScience· 0 citations
SAASBench provides a framework for evaluating the model's ability to estimate the specificity of a candidate antibody in relevant settings, indicating that strong performance on traditional affinity benchmarks does not automatically translate into reliable antibody specificity estimation in proteome-derived settings.
Dmitriy Umerenkov, Ivan Poddiakov· Proceedings of the 32nd ACM...· 0 citations
Antigen-specific antibody retrieval aims to rank candidate antibodies for a target antigen, providing an early virtual-screening step before structural modeling or experimental validation. Existing sequence-based antibody-antigen interaction studies often formulate the problem as pairwise binding prediction, and random or non-clustered evaluations can over-estimate generalization when related antigens appear across training and test data. We study a strict antigen-cluster out-of-distribution (OOD) retrieval setting in which test antigens come from sequence clusters unseen during training. This setting is difficult because binding is driven by local epitope-CDR complementarity, while available databases mainly contain observed positive complexes and lack reliable negative labels for unlabeled candidates. We propose Ab-CASLR, an antibody CDR-aware slot late-interaction retriever that encodes antigens with ESM-2, encodes antibodies with IgBert, constrains antibody-side latent slots to complementarity-determining regions (CDRs), and scores local slot compatibility instead of single-vector global similarity. On a strict OOD benchmark with 849 antigen queries and 869 candidate antibodies, the model achieves 7.42% Hits@10, outperforming k-mer homology transfer at 5.53% Hits@10 and yielding 6.28-fold enrichment over exact random screening at K = 10. Ablations and diagnostics show that CDR-constrained antibody slots remain diverse, whereas antigen-side latent slots collapse into similar summaries. These results support CDR-aware local antibody representation as a useful inductive bias for early binder recovery under strict OOD evaluation, while antigen-side epitope grounding remains unresolved.
Peishuo Liu, Mianzhi Pan, Chenyang Yan et al.· bioRxiv· 0 citations
Abstract Motivation To enable real-world protein-ligand affinity prediction, not only out-of-distribution generalization but also robustness to variable structural availability and quality should be considered in model design. Results We present AlignNet, a hierarchical representation alignment framework that mitigates intra- and inter-molecular heterogeneity to learn robust protein-ligand embeddings for generalizable affinity prediction, even from sequence-level inputs. Its intra-molecular module projects unimodal and multimodal features into a unified space, aligning augmented multimodal views for feature fusion and unimodal with multimodal embeddings to distill multimodal priors for structure-agnostic inference. Its inter-molecular module aligns protein and ligand embeddings for cross-molecular integration. Extensive experiments show that AlignNet (i) achieves highly competitive performance, with up to a 20.4% gain in SCC on the challenging LBA 30% split under sequence-only settings, suggesting improved out-of-distribution generalization; and (ii) learns well-separated affinity-related clusters, supporting reliable structure-independent prediction. Availability and implementation AlignNet is available at https://github.com/altriavin/AlignNet.
Xiaowen Hu, Hongyi Huang, Hao Sun et al.· Bioinformatics· 0 citations