From Physics to Experiment: Learning Resolution-Controllable Cryo-EM Density with Structured Diffusion

Cryo-electron microscopy (cryo-EM) density maps are central to macromolecular structure determination, yet generating synthetic maps that faithfully approximate experimental reconstructions from an atomic model remains challenging. Analytical renderers provide explicit resolution control but rely on simplified physical approximations, whereas learned methods can capture experimental characteristics but often provide limited controllability. We introduce CryoNet.Density, a 3D Brownian Bridge Diffusion Model that formulates cryo-EM density-map generation as structured physical-to-experimental translation. Given an atomic model, a differentiable, resolution-matched renderer first produces a physically rendered map; CryoNet.Density then learns the correction toward the paired experimental reference map rather than generating density from noise. Fourier-feature conditioning enables continuous inference from 1.5–10 Å, while a one-transition stochastic sampler requires only two denoiser evaluations. On a held-out 1,923-case test set, CryoNet.Density outperforms analytical renderers and Struc2mapGAN across seven spatial- and Fourier-domain metrics. The generated density maps further improve secondary-structure recognition and residue identification, supporting their structural fidelity. We additionally construct the CryoNet.Density Database, comprising 3,640,616 traceable generated density maps from 260,044 eligible PDB entries at 14 controlled resolutions. Together, these results establish structured physical-to-experimental diffusion as a scalable framework for controllable synthetic cryo-EM density-map generation.