# Protein model quality assessment using rotation-equivariant, hierarchical neural networks

arXiv: https://arxiv.org/abs/2011.13557 · HTML: https://mlsanity.com/paper/2011.13557/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2020.

## Built on by (most cited first, 8 of 8)

- [Equivariant Graph Neural Networks for 3D Macromolecular Structure](https://mlsanity.com/paper/2106.03843/) (2021)
- [Molformer: Motif-based Transformer on 3D Heterogeneous Molecular Graphs](https://mlsanity.com/paper/2110.01191/) (2021)
- [Evaluating representation learning on the protein structure universe](https://mlsanity.com/paper/2406.13864/) (2024)
- [Protein sequence-to-structure learning: Is this the end(-to-end revolution)?](https://mlsanity.com/paper/2105.07407/) (2021)
- [EGR: Equivariant Graph Refinement and Assessment of 3D Protein Complex Structures](https://mlsanity.com/paper/2205.10390/) (2022)
- [6DCNN with roto-translational convolution filters for volumetric data processing](https://mlsanity.com/paper/2107.12078/) (2021)
- [FlexVDW: A machine learning approach to account for protein flexibility in ligand docking](https://mlsanity.com/paper/2303.11494/) (2023)
- [Graph Representation Learning for Interactive Biomolecule Systems](https://mlsanity.com/paper/2304.02656/) (2023)

## Abstract (arXiv)

Proteins are miniature machines whose function depends on their three-dimensional (3D) structure. Determining this structure computationally remains an unsolved grand challenge. A major bottleneck involves selecting the most accurate structural model among a large pool of candidates, a task addressed in model quality assessment. Here, we present a novel deep learning approach to assess the quality of a protein model. Our network builds on a point-based representation of the atomic structure and rotation-equivariant convolutions at different levels of structural resolution. These combined aspects allow the network to learn end-to-end from entire protein structures. Our method achieves state-of-the-art results in scoring protein models submitted to recent rounds of CASP, a blind prediction community experiment. Particularly striking is that our method does not use physics-inspired energy terms and does not rely on the availability of additional information (beyond the atomic structure of the individual protein model), such as sequence alignments of multiple proteins.