SAASBench: A Synthetic Antibody–antigen Specificity Benchmark

Abstract

Accurate computational prediction of antibody-antigen binding affinity and specificity is critical for accelerating the design of next-generation therapeutics. In computational antibody design, the central challenge is not merely predicting binding, but determining whether an antibody preferentially binds its intended antigen over realistic off-targets. Existing antibody-antigen benchmarks largely focus on affinity prediction or docking accuracy on known binders, and therefore do not directly evaluate antibody specificity. We introduce SAASBench, an adversarial diagnostic benchmark that isolates antibody specificity as a set-based ranking problem. For 20 therapeutically approved full-length antibodies, SAASBench constructs an antibody-conditioned synthetic candidate set containing the true antigen and hard negative decoys drawn from the human extracellular proteome. The decoys are selected to be similar to the positive on structural plausibility of the synthetic Ab-Ag complex and on the change in solvent accessible surface area. Evaluation uses per-antibody ranking metrics aligned with practical downselection decisions. Across 20 antibody panels, affinity-based predictors display heterogeneous performance, ranging from below-random to moderate success. Overall, these results indicate that strong performance on traditional affinity benchmarks does not automatically translate into reliable antibody specificity estimation in proteome-derived settings. SAASBench provides a framework for evaluating the model's ability to estimate the specificity of a candidate antibody in relevant settings.