Deep-learning methods for antibody structure prediction, antibody-antigen interaction modelling and design are advancing rapidly. However, comparisons across studies remain difficult because training and test sets are often constructed independently, and a temporal cutoff alone does not prevent train-test leakage. We present SABLE (Structural Antibody Benchmark for deep-Learning Evaluation), a versioned structural antibody resource that couples a fixed training collection with a leakage-controlled held-out test set for reproducible machine-learning development and evaluation. SABLE combines 16,511 experimental training entries with 327 manually reviewed test entries and 3,274 high-confidence, patent-derived AlphaFold3 models spanning 476 antigens. Candidate test structures were selected after the AlphaFold3 temporal cutoff and filtered against the training set using antibody and antigen sequence similarity filters. Each test entry records its nearest training set neighbour, allowing users to quantify remaining relatedness and stratify performance by similarity. Versioned releases provide metadata, processed structures, CDR annotations, redundancy labels and model-confidence fields. A Python/PyTorch API and standardised benchmark metrics provide reproducible database access and evaluation code without requiring additional antibody-structure processing.
Cohen, T., Bhattacharya, H., Ozery-Flato, M., Schneidman-Duhovny, D.
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