# EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation

arXiv: https://arxiv.org/abs/1707.06017 · HTML: https://mlsanity.com/paper/1707.06017/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2017.

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

- [Contrastive Representation Learning for 3D Protein Structures](https://mlsanity.com/paper/2205.15675/) (2022)
- [A Survey on Protein Representation Learning: Retrospect and Prospect](https://mlsanity.com/paper/2301.00813/) (2022)
- [Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoy](https://mlsanity.com/paper/2302.10888/) (2023)
- [Clustering for Protein Representation Learning](https://mlsanity.com/paper/2404.00254/) (2024)

## Abstract (arXiv)

During the past decade, with the significant progress of computational power as well as ever-rising data availability, deep learning techniques became increasingly popular due to their excellent performance on computer vision problems. The size of the Protein Data Bank has increased more than 15 fold since 1999, which enabled the expansion of models that aim at predicting enzymatic function via their amino acid composition. Amino acid sequence however is less conserved in nature than protein structure and therefore considered a less reliable predictor of protein function. This paper presents EnzyNet, a novel 3D-convolutional neural networks classifier that predicts the Enzyme Commission number of enzymes based only on their voxel-based spatial structure. The spatial distribution of biochemical properties was also examined as complementary information. The 2-layer architecture was investigated on a large dataset of 63,558 enzymes from the Protein Data Bank and achieved an accuracy of 78.4% by exploiting only the binary representation of the protein shape. Code and datasets are available at https://github.com/shervinea/enzynet.