# A^2-Net: Molecular Structure Estimation from Cryo-EM Density Volumes

arXiv: https://arxiv.org/abs/1901.00785 · HTML: https://mlsanity.com/paper/1901.00785/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jan 2019.

## Built on by (most cited first, 4 of 4)

- [Artificial Intelligence Advances for De Novo Molecular Structure Modeling in Cryo-EM](https://mlsanity.com/paper/2102.06125/) (2021)
- [CryoNet.Refine: A One-step Diffusion Model for Rapid Refinement of Structural Models with Cryo-EM Density Map Restraints](https://mlsanity.com/paper/2602.22263/) (2026)
- [FFF: Fragments-Guided Flexible Fitting for Building Complete Protein Structures](https://mlsanity.com/paper/2308.03654/) (2023)
- [CryoLVM: Self-supervised Learning from Cryo-EM Density Maps with Large Vision Models](https://mlsanity.com/paper/2602.02620/) (2026)

## Abstract (arXiv)

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.