  Cryo-electron microscopy (cryo-EM) has become a major experimental technique
to determine the structures of large protein complexes and molecular
assemblies, as evidenced by the 2017 Nobel Prize. Although cryo-EM has been
drastically improved to generate high-resolution three-dimensional (3D) maps
that contain detailed structural information about macromolecules, the
computational methods for using the data to automatically build structure
models are lagging far behind. The traditional cryo-EM model building approach
is template-based homology modeling. Manual de novo modeling is very
time-consuming when no template model is found in the database. In recent
years, de novo cryo-EM modeling using machine learning (ML) and deep learning
(DL) has ranked among the top-performing methods in macromolecular structure
modeling. Deep-learning-based de novo cryo-EM modeling is an important
application of artificial intelligence, with impressive results and great
potential for the next generation of molecular biomedicine. Accordingly, we
systematically review the representative ML/DL-based de novo cryo-EM modeling
methods. And their significances are discussed from both practical and
methodological viewpoints. We also briefly describe the background of cryo-EM
data processing workflow. Overall, this review provides an introductory guide
to modern research on artificial intelligence (AI) for de novo molecular
structure modeling and future directions in this emerging field.
