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abstract: 'Constructing of molecular structural models from Cryo-Electron Microscopy (Cryo-EM) density volumes is the critical last step of structure determination by Cryo-EM technologies. Methods have evolved from manual construction by structural biologists to perform 6D translation-rotation searching, which is extremely compute-intensive. In this paper, we propose a learning-based method and formulate this problem as a vision-inspired 3D detection and pose estimation task. We develop a deep learning framework for amino acid determination in a 3D Cryo-EM density volume. We also design a sequence-guided Monte Carlo Tree Search (MCTS) to thread over the candidate amino acids to form the molecular structure. This framework achieves 91% coverage on our newly proposed dataset and takes only a few minutes for a typical structure with a thousand amino acids. Our method is hundreds of times faster and several times more accurate than existing automated solutions without any human intervention.'
author:
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    Kui Xu$^1$, Zhe Wang$^2$, Jianping Shi$^2$, Hongsheng Li$^3$, Qiangfeng Cliff Zhang$^1$\
    $^1$Tsinghua University $^2$SenseTime Research $^3$The Chinese University of Hong Kong\
    xuk16@mails.tsinghua.edu.cn, {wangzhe, shijianping}@sensetime.com, hsli@ee.cuhk.edu.hk, qczhang@tsinghua.edu.cn
bibliography:
- 'aaai19-xk.bib'
title: '$A^2$-Net: Molecular Structure Estimation from Cryo-EM Density Volumes'
---

Introduction
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Resolving the 3D atomic structures of macro molecules is of 