AF3-AQA: Assessing and Selecting Antibody–Antigen Structures from AlphaFold3 Ensembles

Accurate antibody–antigen complex structures are important for structure-based antibody discovery and design. Although AlphaFold3 (AF3) can generate diverse candidate structures through stochastic sampling, these candidates often differ substantially in binding pose and structural accuracy, making reliable candidate selection a critical challenge. We study this problem by systematically analyzing scoring signals and developing a learned model to assess candidate quality. To support this study, we introduce AF3-AQA (AlphaFold3 Antibody–Antigen Quality Assessment), comprising 430,100 predicted structures for 391 experimentally resolved targets, with independently sampled pools of 100 and 1,000 candidates per target. Under a unified evaluation protocol, we compare AF3 confidence scores and external rankers, and investigate how different factors contribute to candidate selection. Our analyses show that regional restriction has feature-dependent benefits, aggregation can substantially change selection performance, and strong scalar scores can still fail to distinguish different candidate poses. We further introduce AQA-Score, which transfers single-chain interface-site and residue-pair compatibility representations into candidate-specific assessment with additional structure and energy features. On AF3-AQA-100, AQA-Score improves DockQ@1/5 from 0.345/0.380 for iPTM to 0.363/0.402. Together, these results characterize the strengths and limitations of current selection signals and establish a learned approach for improving antibody–antigen structure selection.