CoPocket: Pocket Identification Without Receptor Structures Using Co-Folding Representations

Locating ligandable pockets without a known ligand is an early step in drug discovery. However, most pocket predictors need a receptor structure and are benchmarked on ligand-bound conformations that do not exist before a binder is found. Sequence-based methods avoid this requirement, although UniSite's sequence-only variant underperforms its structure-informed counterpart. Benchmarks also conflate new structures of known sites with newly discovered pockets, and compute residue-level and pocket-level metrics from different outputs. CoPocket predicts pockets from sequence and multiple sequence alignment, without receptor structures or a ligand. Its set-prediction decoder maps frozen co-folding residue and pair representations to scored masks, evaluated at both pocket and residue levels. We also build the ChronoPocket dataset, which consolidates over 230,000 PDB ligand contacts into 37,269 pockets and splits them by the date each pocket was first observed. Its ChronoPocket-Discovery subset holds out proteins with previously known sites and additional pockets first observed after the training cutoff. On the temporal test set, CoPocket reaches a pocket average precision of 0.430 at an intersection-over-union threshold of 0.5, compared with 0.230 for an ESM C-based sequence-only baseline. On UniSite test proteins absent from our training data, it outperforms the structure-based UniSite-3D (0.347 vs. 0.303). On ChronoPocket-Discovery, it recovers 20.5% of later-observed pockets in its top five predictions, a challenging reference for pocket discovery.