and KITTI LiDAR detection dataset.[]{data-label="fig:dense"}](1_1_aa_kitti5_small.pdf){width="0.95\linewidth"}

Method {#sec:blind}
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The framework of $A^2$-Net consists of two stages. Stage one represents the deep neural network for amino acid detection in 3D space and pose estimation, which determines the 3D coordinates of atoms in each amino acid. Stage two uses a Monte Carlo Tree Search strategy with tree pruning, based on the candidate amino acid proposals obtained in stage one to construct the main chains of amino acids, i.e., proteins.

![image](1_2_arch2_small.pdf){width="\linewidth"}

3D Amino Acid Detection {#sec:net}
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As shown in Fig. \[fig:pipeline\], when given a density volume, our $A^2$-Net first obtains 3D feature volumes and generates 3D box proposals with the region proposal network (RPN) [@ren2015faster]. The 3D RPN consists of three 3D convolutional layers to generate proposals of amino acid locations. We used 3D anchors at each 3D location to cover the region of amino acids with various scales and aspect ratios. Next, one branch of RPN classified whether the anchors are valid amino acid proposals and the other branch estimates their coordinates. With the amino acid proposals, we used a newly designed Aspect-ratio Preserved RoI (*APRoI*) layer to extract the RoI in the input volume into a fixed cubic. Then the 