Hidden in the Fold: Cryptic Pocket Discovery with Local-Global Geometric Context

Cryptic pockets are transient or weakly exposed binding sites that are often invisible in static protein structures, yet they play important roles in ligand recognition, allosteric regulation, and drug discovery. Unlike canonical surface pockets, cryptic pockets may only emerge through subtle conformational rearrangements, making their discovery a challenging problem that requires reasoning beyond apparent local concavity. In this work, we propose PocketMon, a context-conditioned equivariant residue field for cryptic pocket discovery. It treats cryptic pocket discovery as a local-global geometric inference problem, where visible residue geometry and latent residue-pair interactions jointly indicate hidden binding opportunities. To this end, PocketMon integrates equivariant geometric modeling with general pairwise residue priors, enabling whole-protein residue-level prediction. We further curate and standardize three cryptic-pocket discovery benchmarks to systematically evaluate this problem. Across these benchmarks, PocketMon consistently outperforms competitive baselines, with up to 14.83% absolute PR-AUC improvement on PocketMiner, and we further apply it to human proteome-scale cryptic pocket detection to provide a broad map of potential hidden binding sites.