@article{Xu_Wang_Shi_Li_Zhang_2019, title={A2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes}, volume={33}, url={https://ojs.aaai.org/index.php/AAAI/article/view/3918}, DOI={10.1609/aaai.v33i01.33011230}, abstractNote={<p>Constructing of molecular structural models from CryoElectron 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.</p>}, number={01}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Xu, Kui and Wang, Zhe and Shi, Jianping and Li, Hongsheng and Zhang, Qiangfeng Cliff}, year={2019}, month={Jul.}, pages={1230-1237} }