A systematic evaluation framework for universal antibody-antigen binding affinity prediction and candidate recommendation
Year: 2026
Type: article
Abstract: 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.
Source: iScience
Institutions: Brandeis University, Amazon (United States)
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,240
OpenAlex ID: W7170420194