Cryo EM Reconstruction

Cryo-EM reconstruction aims to determine the 3D structure of biomolecules from noisy 2D electron microscopy images, a computationally challenging task due to low signal-to-noise ratios and missing data. Current research heavily utilizes deep learning, employing autoencoder architectures and amortized inference methods to improve pose estimation (particle orientation) and handle heterogeneous datasets (multiple conformations). These advancements, including techniques like equivariant neural networks and Bayesian inference incorporating molecular priors, are significantly enhancing reconstruction speed, accuracy, and resolution, leading to more detailed structural insights in various biological fields.