3d density estimation using normalizing flows and its application to 3d reconstruction in cryo-EM

Generative models enable the researchers to address multiple problems spanning from noise removal to generating novel samples with properties of the domain. Generative models are commonly studied for images and in this project the idea will be expanded to 3D structures or volumes. Single-particle cryo-electron microscopy (cryo-EM) is a technique to estimate accurate 3D structures of biological molecules which is used by practitioners in fields like precision medicine. This allows them to design drugs that could cure patients with rare diseases and avoid side effects. A trained generative model on previously estimated molecular density models, would enable rapid improvement in resolution of estimated densities of limited resolution. The outcome of this research project will be provided as a ready-made tool that improves the resolution of estimated densities in its input. Through this collaboration, Borealis AI would push forward 3D generative methods research and its application to density estimation.

Faculty Supervisor:

Michael Brown


Abbas Masoumzadeh Tork


Borealis AI


Engineering - computer / electrical


Information and communications technologies




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