Daniel Aibinder

Standard cryo-EM pipelines first reconstruct a 3D density from 2D projections and only then fit an atomic model, so atomic-coordinate priors never reach the most under-determined stage. EB-FM trains a flow-matching prior inside a Monte Carlo stochastic-approximation expectation-maximization loop, repeatedly inverting the nonlinear imaging forward model during training, so no clean structures are needed for pretraining. On simulated cryo-EM data with known poses it recovers conformational distributions directly in atomic-coordinate space for four proteins, though predicted motion amplitudes stay below the molecular-dynamics ground truth.

Standard cryo-EM pipelines first reconstruct a 3D density from 2D projections and only then fit an atomic model, so atomic-coordinate priors never reach the most under-determined stage. EB-FM trains a flow-matching prior inside a Monte Carlo stochastic-approximation expectation-maximization loop, repeatedly inverting the nonlinear imaging forward model during training, so no clean structures are needed for pretraining. On simulated cryo-EM data with known poses it recovers conformational distributions directly in atomic-coordinate space for four proteins, though predicted motion amplitudes stay below the molecular-dynamics ground truth.