fundamental importance to biological and medical research. Single particle Cryo-EM has emerged as a revolutionary technique that images biomolecules frozen in their native (or native-like) states. With Cryo-EM, 2D projection images are firstly collected and then reconstructed into a volumetric data, *i.e.*, density volume, by software tools such as Relion [@cryoem-relion] and cryoSparc [@cryoem-cryospac-nmeth; @cryoem-cryospac-tpami; @cryoem-cryospac-cvpr]. Next, a molecular model that represents the atomic coordinates of each amino acid, the building blocks of protein molecules, is constructed and fitted into the 3D density volume (Fig. \[fig:overview\]).

![The overview of resolving 3D atomic structures. []{data-label="fig:overview"}](1_0_overview_small.pdf){width="\linewidth"}

Despite the steady progresses towards automatic Cryo-EM structure determination, molecular model building remains a bottleneck. This step is difficult to automate since it relies substantially on human expertise. Structural biology experts are needed to manually assign specific amino acids to a density volume, with the help of 3D visualization tools, such as Chimera [@cryoem-chimera] and Coot [@cryoem-coot]. These manual operations are also time-consuming and inevitably error-prone. Attempts to automate this process include Rosetta [@rosetta-denovo] (we named as Rosetta-denovo), RosettaES [@cryoem-rosettaes], Phinex [@cryoem-phenix] and EMAN2 [@cryoem-eman2]. However, their accuracy and coverage remain quite limited, often take hundreds of hours and frequently require human intervention.

We approached this 