CryoFATHOM: Flow-matched Amortized Tomography for Held-Out Molecules

Single-particle cryo-electron microscopy (cryo-EM) is a key technique for estimating the 3D density of proteins and other large molecules from randomly oriented 2D projection images. Standard pipelines use many thousands of noisy images to simultaneously estimate a high-resolution molecular density and the projection operator that corresponds to each image. Emboldened by the recent progress in generative AI, we ask if one could learn a strong prior on the distribution of protein shapes to produce useful reconstructions from a tiny number of input images. To answer this question we built CryoFATHOM, a conditional flow matching model that was trained on 850 proteins from the Protein Data Bank. The model samples a molecular density conditioned on its projection images, where for simplicity we assume known orientations. Reconstruction is done by integrating the ODE of the trained conditional velocity field, with no inference-time optimization. By picking the number of Euler steps and incorporating classifier-free guidance we can trade sharpness against fidelity. We release our code and a benchmark for evaluating trainable single-particle reconstruction methods on held-out molecules. In our experiments, CryoFATHOM produced better resolution reconstructions than RELION up to about 100 projection images. Potential applications of our approach include the reconstruction of molecules with continuous heterogeneity, cryo-ET, and medical imaging methods such as CT and MRI.