Kryptix-1: Conformational Ensemble Sampling Substantially Improves Cryptic Pocket Detection Relative to Static-Structure and Prior Computational Approaches

Abstract

Cryptic binding pockets; sites absent or occluded in a protein’s resting-state structure that become druggable only in specific, transiently populated conformations; represent one of the largest untapped opportunities in structure-based drug discovery. Large-scale structural surveys estimate that cryptic pockets occur in on the order of one in six protein families genome-wide, and that accounting for them could expand the druggable fraction of the disease-associated human proteome from roughly 40% to nearly 80%. Detecting cryptic pockets computationally has historically forced a choice between physics-based conformational sampling, which is reliable but too computationally expensive to deploy across more than a handful of targets, and fast machine-learning pocket predictors trained on static structures, which frequently over-predict and lose the precision needed for practical triage. We report Kryptix-1, an in-house pipeline developed at Covenant Biosciences that combines conformational ensemble generation with a consensus pocket-scoring algorithm, benchmarked here against a matched static-structure baseline and against the published literature on a 10-protein sample from CryptoBench, an independently curated cryptic-pocket reference dataset. On the field’s own standard residue-overlap metric (Jaccard index ≥ 0.5), Kryptix-1 succeeds on 80% of benchmark proteins, compared to 20% for the static baseline and approximately 40–45% for the best-performing methods reported in an independent comparative evaluation using the same metric. We report these results alongside an explicit accounting of where Kryptix-1’s advantage is smaller or reverses, and we are explicit throughout that this is a small pilot evaluation, not a fully powered validation study.