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This paper introduces an Auditable Algebraic Counting Field (ACF) to predict cryptic ligand-binding pockets from apo protein structures, addressing the challenge of identifying these pockets from unbound geometry and ensuring prediction inspectability. ACF leverages explicit geometric, physicochemical, and topological features compiled into integer-weighted lookup tables, enabling prediction reconstruction without sequence search or language models. Evaluated on external datasets, ACF demonstrated superior ranking performance over P2Rank, achieving a +0.044 mean paired ROC-AUC advantage, while also providing an auditable path from structural data to residue scores.
Cryptic pockets are now predictable and auditable from apo structures using a compact, integer-weighted algebraic counting field that bypasses sequence search and protein language models.
Cryptic ligand-binding pockets are not apparent in experimentally determined apo structures, making them difficult to identify from unbound receptor geometry. A complementary challenge is to make the structural measurements and learned evidence behind each prediction directly inspectable. We introduce a supervised algebraic counting field (ACF) for predicting cryptic-pocket residues from apo structures. ACF compiles explicit geometric, physicochemical, and topological features into compact, integer-weighted lookup tables. Each prediction score can be reconstructed from feature values, training counts, table weights, and spatial aggregation, without sequence search, structural-template transfer, or a protein language model at inference. We evaluate ACF on CryptoBench and two locked external collections, separating ranking performance from the effects of residue-calling budgets. On an external set of 57 post-CryptoBench apo-holo units, ACF exceeded P2Rank by +0.044 in mean paired ROC-AUC (multiplicity-adjusted 95% CI [+0.010, +0.079]). The advantage was dataset-dependent: official-fold ROC-AUC and matched-budget F1 differences against P2Rank remained unresolved, and a second external evaluation did not confirm gains from added structural features. ACF thus provides a compact predictor with externally validated signal and an inspectable path from structural measurements and training counts to residue scores.