mlsanityData to 7 Oct 2026

Mimetic Neural Networks: A unified framework for Protein Design and Folding

arXiv 2102.03881 ↗First posted 7 Feb 2021q-bio.BM cs.CV cs.LGcited by 7

Not on the map. Why?

Abstract (arXiv)

Recent advancements in machine learning techniques for protein folding motivate better results in its inverse problem -- protein design. In this work we introduce a new graph mimetic neural network, MimNet, and show that it is possible to build a reversible architecture that solves the structure and design problems in tandem, allowing to improve protein design when the structure is better estimated. We use the ProteinNet data set and show that the state of the art results in protein design can be improved, given recent architectures for protein folding.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 24 of its 91 references that are in the map. First posted on arXiv 7 Feb 2021.

Builds on

24 of 91 references.

References · 24

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  3. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
  4. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  5. 2018Neural Ordinary Differential Equationscited by 3,733
  6. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  7. 2020Simple and Deep Graph Convolutional Networkscited by 857
  8. 2017Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networkscited by 707
  9. 2017Solving high-dimensional partial differential equations using deep learningcited by 549
  10. 2019Graph U-Netscited by 489
  11. 2019PointFlow: 3D Point Cloud Generation with Continuous Normalizing Flowscited by 487
  12. 2019PairNorm: Tackling Oversmoothing in GNNscited by 297
12 more
  1. 2018Generating 3D faces using Convolutional Mesh Autoencoderscited by 267
  2. 2017The Reversible Residual Network: Backpropagation Without Storing Activationscited by 215
  3. 2018Deep Neural Networks Motivated by Partial Differential Equationscited by 187
  4. 2019Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generationcited by 89
  5. 2017Reversible Architectures for Arbitrarily Deep Residual Neural Networkscited by 69
  6. 2018MeshCNN: A Network with an Edgecited by 69
  7. 2019ProteinNet: a standardized data set for machine learning of protein structurecited by 39
  8. 2020Structure preserving deep learningcited by 30
  9. 2020Deep Learning in Protein Structural Modeling and Designcited by 25
  10. 2020DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Poolingcited by 16
  11. 2018Stacked U-Nets: A No-Frills Approach to Natural Image Segmentationcited by 9
  12. 2019Accurate Protein Structure Prediction by Embeddings and Deep Learning Representationscited by 4

Built on by

Cited by year

Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 1.

7 papers.

  1. 2021PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equationscited by 106
  2. 2022Improving Graph Neural Networks with Learnable Propagation Operatorscited by 19
  3. 2023DRIP: Deep Regularizers for Inverse Problemscited by 7
  4. 2021Haar Wavelet Feature Compression for Quantized Graph Convolutional Networkscited by 5
  5. 2024Global-Local Graph Neural Networks for Node-Classificationcited by 4
  6. 2022Estimating a potential without the agony of the partition functioncited by 2
  7. 2022Neural DAEs: Constrained neural networkscited by 1