task from a novel perspective, inspired by the great success of deep learning applications in image recognition [@krizhevsky2012imagenet], we choose to approach this task from a totally novel perspective. The molecular structure determination problem, in our view, can be considered as three sub-problems: 1) *amino acid detection* in the density volumes, 2) *atomic coordinates assignment* to determine the atomic coordinates of each amino acid and, 3) *main chain threading* to resolve the sequential order of amino acids that form each protein chain.

Leveraging the power of deep Convolutional Neural Network [@srivastava2015training], we reformulated the problem and developed a novel framework for amino acid detection that learns the distribution of conformational densities of individual amino acids. Moreover, we designed a sequence-guided neighbor loss in training step to encode prior knowledge of protein sequences into amino acid detection. We also propose an MCTS algorithm to search and thread over the amino acids to form the full molecular structure. Our approach to molecular structure determination does not require human intervention and, when tested on a newly proposed large-scale dataset, it runs hundreds of times faster, and more accurately than existing methods. Finally, to the best of our knowledge, there remains no publicly available 