mlsanityData to 7 Oct 2026

A Survey on Protein Representation Learning: Retrospect and Prospect

arXiv 2301.00813 ↗First posted 31 Dec 2022cs.LG cs.AIcited by 10

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Abstract (arXiv)

Proteins are fundamental biological entities that play a key role in life activities. The amino acid sequences of proteins can be folded into stable 3D structures in the real physicochemical world, forming a special kind of sequence-structure data. With the development of Artificial Intelligence (AI) techniques, Protein Representation Learning (PRL) has recently emerged as a promising research topic for extracting informative knowledge from massive protein sequences or structures. To pave the way for AI researchers with little bioinformatics background, we present a timely and comprehensive review of PRL formulations and existing PRL methods from the perspective of model architectures, pretext tasks, and downstream applications. We first briefly introduce the motivations for protein representation learning and formulate it in a general and unified framework. Next, we divide existing PRL methods into three main categories: sequence-based, structure-based, and sequence-structure co-modeling. Finally, we discuss some technical challenges and potential directions for improving protein representation learning. The latest advances in PRL methods are summarized in a GitHub repository https://github.com/LirongWu/awesome-protein-representation-learning.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 33 of its 124 references that are in the map. First posted on arXiv 31 Dec 2022.

Builds on

33 of 124 references.

References · 33

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21 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 4; 2025: 3; 2026: 1.

10 papers.

  1. 2024A Survey of Geometric Graph Neural Networks: Data Structures, Models and Applicationscited by 28
  2. 2024PPFlow: Target-aware Peptide Design with Torsional Flow Matchingcited by 26
  3. 2024MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embeddingcited by 22
  4. 2023Data-Efficient Protein 3D Geometric Pretraining via Refinement of Diffused Protein Structure Decoycited by 10
  5. 2025Computational Protein Science in the Era of Large Language Models (LLMs)cited by 6
  6. 2023Interpretable machine learning of amino acid patterns in proteins: a statistical ensemble approachcited by 3
  7. 2025Advances in Protein Representation Learning: Methods, Applications, and Future Directionscited by 3
  8. 2024Enhancing Protein Predictive Models via Proteins Data Augmentation: A Benchmark and New Directionscited by 1
  9. 2025BioBlobs: Unsupervised Discovery of Functional Substructures for Protein Function Predictioncited by 0
  10. 2026RIGA-Fold: A General Framework for Protein Inverse Folding via Recurrent Interaction and Geometric Awarenesscited by 0