# Enhancing Cryo-EM Density Map Segmentation in Phenix for Improved Atomic Model Building

arXiv: https://arxiv.org/abs/2605.05259 · HTML: https://mlsanity.com/paper/2605.05259/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 25 references that are in the map.
- First posted on arXiv 6 May 2026.

## Builds on (most cited first, 3 of 3)

- [A Graph Neural Network Approach to Automated Model Building in Cryo-EM Maps](https://mlsanity.com/paper/2210.00006/) (2022)
- [CryoSAMU: Enhancing 3D Cryo-EM Density Maps of Protein Structures at Intermediate Resolution with Structure-Aware Multimodal U-Nets](https://mlsanity.com/paper/2503.20291/) (2025)
- [Struc2mapGAN: improving synthetic cryo-EM density maps with generative adversarial networks](https://mlsanity.com/paper/2407.17674/) (2024)

## Abstract (arXiv)

We introduce PhenixCraft, a fully automated pipeline for building atomic models from cryo-EM density maps. By integrating AlphaFold predictions, we enhance the map-segmentation step in Phenix during model building, addressing challenges posed by noise and artifacts that traditionally hinder this step. Our results demonstrate PhenixCraft's superior performance in TM-scores and sequence accuracy, significantly improving upon the limitations and inefficiencies of traditional model building using Phenix.