GlycoGen: Crystallizing Flows for De Novo Glycan Structure Prediction

Glycosylation is among the most diverse and important post-translational modifications in biology, governing immunogenicity, self-recognition, and the clinical viability of biologic drugs. Glycans cannot be sequenced from a template, and de novo structure prediction from tandem mass spectrometry (MS/MS) remains a longstanding bottleneck. The current state of the art, GlycoBART, is a $207$M autoregressive transformer whose $O(\mathrm{beam} \cdot \mathrm{output\_length})$ decoder passes preclude real-time annotation. We introduce $\textbf{GlycoGen}$, a $50$M Discrete Flow Matching (DFM) model that predicts glycan structure from MS/MS spectra by direct, non-autoregressive generation in a few parallel forward passes. We pair it at inference time with $\textbf{Crystallizing Flows}$, a deterministic sampler that propagates the per-token distribution on the probability simplex and freezes each token to a one-hot (*crystallizes* it) the moment its argmax leaves the mask, exiting early once all tokens are unmasked. The sampler feeds the denoiser mixture embeddings, and runs out-of-the-box with no retraining. GlycoGen + Crystallizing Flows reach $\mathbf{0.9266}$ top-1 structural accuracy on the CandyCrunch test set, three orders of magnitude faster than GlycoBART, Pareto-dominating the prior state of the art on *both* accuracy *and* speed. We extend our experiments to a matched-architecture MDLM denoiser (Sahoo et al. 2024) and observe a comparable lift: the sampler generalizes from continuous-time flow matching to discrete-time masked diffusion. To our knowledge, this is a new state of the art for de novo open-vocabulary glycan structure prediction, and the first system fast enough for accurate real-time inline annotation on modern LC-MS/MS instruments.