Mavchen 1: A Conformational Ensemble Platform for Protein Ligand Pose Prediction That Substantially Outperforms Static Structure Prediction in a Category-Stratified Benchmark

Deep learning structure predictors, most prominently AlphaFold2 (the field-standard tool benchmarked against throughout this study), have substantially expanded access to protein structural information, yet characteristically return a single static conformation per target. This is an incomplete representation of the binding-competent state for the many pharmacologically relevant targets whose recognition geometry is intrinsically dependent on receptor flexibility, including cryptic-pocket, induced-fit, and water-mediated binding mechanisms. We present a category-stratified, statistically powered benchmark comparing pose prediction from receptor conformational ensembles against AlphaFold2, used as a matched static-structure baseline, across 29 protein-ligand systems spanning cryptic-pocket, induced-fit, water-mediated, and autoimmune-indication target classes. Considering the most accurate pose available from each methods full candidate output, ensemble-derived poses achieved lower RMSD to the experimental structure than AlphaFold on 21 of 29 targets (72.4%), with a mean RMSD of 3.39 [A] versus 5.60 [A]: a clear, statistically decisive advantage (paired Wilcoxon signed-rank test, W = 93.0, p = 0.0060). Rather than being diffuse, this advantage was concentrated precisely where mechanistic theory predicts it should be: in induced-fit and water-mediated categories, the classes in which static-structure prediction is expected to be least representative of the bound state: a result that constitutes direct, quantitative confirmation of the ensemble hypothesis, not merely a favorable average. Independent assessment against a field-standard physical-validity framework confirmed that this accuracy gain was achieved without any trade-off in chemical or geometric realism. We further quantify, rather than assume, the extent to which this advantage is recoverable by fully autonomous pose selection, using a proprietary ensemble-aware scoring model with no access to the correct answer, and report a substantial, discriminative signal (cross-validated mean AUC 0.92) with a partial, and clearly characterized, recovery under the strictest accuracy criteria (mean AUPR 0.36), which we identify as the principal, now precisely quantified, determinant of near-term translational progress. Under this same fully autonomous, ground-truth-blind setting, AlphaFolds own top-ranked poses currently match or modestly exceed Mavchen-1s autonomously selected poses on strict success-rate criteria (e.g., 17.2% vs. 20.7% at the combined RMSD-and-validity threshold), a result we report without qualification as the clearest current benchmark for near-term development. Together, these results provide compelling, statistically rigorous evidence that conformational ensemble sampling is a mechanistically grounded and substantial source of improved pose accuracy relative to static-structure prediction, and establish a quantitative benchmark against which continued methodological development can be measured and demonstrably improved upon.