  In the past decade, deep conditional generative models have revolutionized
the generation of realistic images, extending their application from
entertainment to scientific domains. Single-particle cryo-electron microscopy
(cryo-EM) is crucial in resolving near-atomic resolution 3D structures of
proteins, such as the SARS-COV-2 spike protein. To achieve high-resolution
reconstruction, AI models for particle picking and pose estimation have been
adopted. However, their performance is still limited as they lack high-quality
annotated datasets. To address this, we introduce physics-informed generative
cryo-electron microscopy (GenEM), which for the first time integrates
physical-based cryo-EM simulation with a generative unpaired noise translation
to generate physically correct synthetic cryo-EM datasets with realistic
noises. Initially, GenEM simulates the cryo-EM imaging process based on a
virtual specimen. To generate realistic noises, we leverage an unpaired noise
translation via contrastive learning with a novel mask-guided sampling scheme.
Extensive experiments show that GenEM is capable of generating realistic
cryo-EM images. The generated dataset can further enhance particle picking and
pose estimation models, eventually improving the reconstruction resolution. We
will release our code and annotated synthetic datasets.
