large-scale labeled dataset for the research of molecular structure determination from Cryo-EM density volumes. The dataset we collected and used in this study, named as the $A^2$ dataset, includes 250K amino acid objects in 1,713 protein chains from 218 structures. It constitutes a useful resource for evaluating molecular structure determination methods.

To summarize, our contributions are four fold:

-   This is the first attempt to formulate molecular structure determination from Cryo-EM density volumes with a deep learning approach.

-   We adapt a novel 3D network architecture for amino acid detection and internal atom coordinate estimation in density volumes, and proposed an APRoI layer and neighbor loss for better performance.

-   We design a sequence-guided MCTS algorithm for fast and accurate main chain threading.

-   We will release a large scale, richly annotated dataset of protein density volumes, to facilitate research in this area.

Related Work
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### Molecular Structure Determination

Structure determination of Cryo-EM maps is the process of generating a structure model with 3D coordinates for each atom in the macromolecule (e.g., proteins) that fits the map (Figure 1). The main approaches for molecular structure determination are $de$ $novo$ building and homology modeling, which its homologous structures in the 