ArticlePeerJ2018
EnzyNet: enzyme classification using 3D convolutional neural networks on spatial representation.
Article in PeerJ, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- Autoregressive enzyme function prediction with multi-scale multi-modality fusion.Briefings in bioinformatics · 2025Article
- TopEC: prediction of Enzyme Commission classes by 3D graph neural networks and localized 3D protein descriptor.Nature communications · 2025Article
- $\mathcal{S}$ able: bridging the gap in protein structure understanding with an empowering and versatile pre-training paradigm.Briefings in bioinformatics · 2025Article
- Structural and functional prediction, evaluation, and validation in the post-sequencing era.Computational and structural biotechnology journal · 2024Review
- Article
- Machine Learning-Guided Protein Engineering.ACS catalysis · 2023Review
- Hierarchical graph learning for protein-protein interaction.Nature communications · 2023Article
- Hybrid Deep Learning Based on a Heterogeneous Network Profile for Functional Annotations ofInternational journal of molecular sciences · 2021Article
- Comprehensive Survey of Recent Drug Discovery Using Deep Learning.International journal of molecular sciences · 2021Review
- Structure-based protein function prediction using graph convolutional networks.Nature communications · 2021Article
- Amalgamation of 3D structure and sequence information for protein-protein interaction prediction.Scientific reports · 2020Article
- 3D Deep Learning on Medical Images: A Review.Sensors (Basel, Switzerland) · 2020Review
- Article
- libmolgrid: Graphics Processing Unit Accelerated Molecular Gridding for Deep Learning Applications.Journal of chemical information and modeling · 2020Article
- Article
- Drugena: A Fully Automated Immunoinformatics Platform for the Design of Antibody-Drug Conjugates Against Neurodegenerative Diseases.Advances in experimental medicine and biology · 2020Article
- Alignment-Free Method to Predict Enzyme Classes and Subclasses.International journal of molecular sciences · 2019Article
- Multiresolution 3D-DenseNet for Chemical Shift Prediction in NMR Crystallography.The journal of physical chemistry letters · 2019Article
- KekuleScope: prediction of cancer cell line sensitivity and compound potency using convolutional neural networks trained on compound images.Journal of cheminformatics · 2019Article
- Prediction of Enzyme Function Based on Three Parallel Deep CNN and Amino Acid Mutation.International journal of molecular sciences · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (PDB) 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 two-layer architecture was investigated on a large dataset of 63,558 enzymes from the PDB 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.