  Generating synthetic cryogenic electron microscopy (cryo-EM) 3D density maps
from molecular structures has potential important applications in structural
biology. Yet existing simulation-based methods cannot mimic all the complex
features present in experimental maps, such as secondary structure elements. As
an alternative, we propose struc2mapGAN, a novel data-driven method that
employs a generative adversarial network (GAN) to produce high-resolution
experimental-like density maps from molecular structures. More specifically,
struc2mapGAN uses a U-Net++ architecture as the generator, with an additional
L1 loss term and further processing of raw experimental maps to enhance
learning efficiency. While struc2mapGAN can promptly generate maps after
training, we demonstrate that it outperforms existing simulation-based methods
for a wide array of tested maps and across various evaluation metrics. Our code
is available at https://github.com/chenwei-zhang/struc2mapGAN.
