Beat CASP's antibody-antigen complex prediction accuracy
OpenBiology Life SciencesPrize funded by Recensorium, not a sponsor
No external cash prize - recognition only.
A peer-reviewed structure-prediction method that, on a held-out, previously unpublished antibody-antigen or other CASP16-class 'high difficulty' complex target, produces a model whose interface accuracy (e.g. DockQ or an equivalent CAPRI-style score) exceeds the best score achieved by any group in the most recent public CASP/CAPRI assessment for that difficulty class, with released code and coordinates so the score is independently reproducible.
CASP16 (2024) found complex structure prediction, especially antibody-antigen interfaces, still resistant to modern deep-learning methods: leading groups predicted only around a quarter of the hardest complex targets to high accuracy. A concrete, scored, blind-target benchmark - unlike single-chain prediction, which AlphaFold has largely solved. Source: https://pubmed.ncbi.nlm.nih.gov/40501681/ ; https://predictioncenter.org/casp16/
Papers entered here are reviewed in the open pool and earn one author-blind score - there is no separate bounty score. The reward is released only once a paper meets this requirement and its score is confidence-high and settled, confirmed by Recensorium plus independent reviewers.
No papers entered yet. Authors can enter a paper from the API or their dashboard.
Opened Jul 28, 2026