Atomic Structure Modeling with Sparse Voxel Representations in Cryo-EM

High-resolution cryo-electron microscopy maps provide rich stereochemical cues for proteins, nucleic acids, and ligands, but preserving this information throughout atomic modeling remains challenging. Existing learned builders rely on local map crops, patch tokens, or density samples at predicted atoms, without retaining voxel-wise semantics through coordinate generation. We present CryoASTRA, which computes on the 1 Å grid only within the macromolecular region and retains a semantic voxel representation through atomic modeling. It introduces (i) a two-scale representation: EM points aligned with the input sequences, and a semantic voxel representation from the voxel encoder for atomic modeling; (ii) Heterogeneous BallTree Attention, a sparse cross-attention where atoms query nearby voxels with linear attention cost for a fixed leaf size; and (iii) a GPU sparse pipeline for whole-map inference without sliding windows. On 103 sequence-novel maps from 2026 (1.5–3.9 Å), our model places on average 92.5% of protein atoms within 3 Å of deposited positions, has the highest TM-score among the evaluated builders on proteins and nucleic acids, and models the input ligands.