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.
Abstract
Summary Predicting antigen-antibody binding is essential to drug discovery and protein engineering. For de novo antibody design, generalizable binding prediction models are crucial for efficient in silico screening. However, existing affinity predictors lack generalization, with performance deteriorating for antibodies targeting antigens absent from training data or datasets lacking non-binders. To address this, we establish a benchmarking framework for evaluating universal antibody-antigen binding affinity prediction. Our framework compares sequence- and structure-based methods across diverse antigens, introducing standardized evaluation protocols based on pairwise accuracy and retrieval metrics. We propose MochiBind, a sequence-only pairwise binding affinity predictor, and benchmark it against structure-derived baselines such as Boltz-2, GeoDock, and Graphinity. The results show that MochiBind achieves comparable or superior performance in pairwise accuracy and retrieval, suggesting that sequence-based approaches can match or surpass structure-based models in generalization. The proposed benchmark provides a foundation for fair comparison and future development, enabling scalable, sequence-driven solutions to binding affinity prediction.
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
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
Protein therapeutic design and property prediction are frequently hampered by data scarcity. Here we propose a model, DyAb, that addresses these issues by leveraging a pair-wise representation to predict differences in binding affinity, rather than absolute values. DyAb is built on top of a pre-trained protein language model and achieves a Spearman rank correlation of up to 0.85 on binding affinity prediction across monoclonal antibodies targeting three different antigens (EGFR, IL-6, and an internal target), given as few as 100 training data. We employ DyAb in two design contexts: as a ranking model to score combinations of known mutations, and combined with a genetic algorithm to generate new sequences. Our method consistently generates antibody variants with high binding rates, including designs that improve on the binding affinity of the lead molecule by more than ten-fold. DyAb represents a powerful tool for optimizing antibody binding affinity in low data regimes common in early-stage drug development.
J. Lin, Jennifer L. Hofmann, Andrew Leaver-Fay et al.· mAbs· 0 citations
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.
Zhiyuan Chen, Jing Hu, Junzhe Wang et al.· 0 citations
Experimentally validated prospective, blinded benchmarks are needed to separate durable advances from hype in computational antibody design. Here AIntibody, a challenge inspired by the Critical Assessment of Structure Prediction, tests 511 artificial intelligence (AI)-designed or predicted antibodies from 29 organizations on three tasks: in silico affinity maturation from phase 1 sequencing outputs, affinity ranking within heavy-chain complementarity-determining region 3 (HCDR3) clusters of a selection output and CDR design of proteins not included in a selection output. Validated with diverse experimental assays, several groups produced developable antibodies with affinities <100 pM. However, these successes were exceptions that did not transfer across tasks. Affinity-matured antibodies were modeled effectively. Except for one model, predicting high-affinity clones from clustered HCDR3 datasets was worse than random clone picking. Out-of-library design was highly variable for most method submissions, with many failing to outperform standard selections. The AIntibody challenge shows that AI can optimize antibodies in defined, biologically grounded regimes, in addition to highlighting critical gaps including affinity prediction and library-inspired antibody design and cross-task generalization.
M. Erasmus, Daniel Bedinger, Elizabeth Hopkins et al.· Nature Biotechnology· 0 citations
This work evaluated ImmuneBuilder, IgFold, AlphaFold3, GRAMM, and dyMEAN on 50 non-redundant humanized antibody–antigen complexes using multiple retained predictions and paired statistical testing, finding all three antibody structure predictors were accurate.
Zeyuan Yu, Jilei Wu, Ziyao Ning et al.· Bioinformatics Advances· 0 citations