mlsanityData to 14 Oct 2026

Advances in Protein Representation Learning: Methods, Applications, and Future Directions

arXiv 2503.16659 ↗First posted 20 Mar 2025cs.LG q-bio.BMcited by 3

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Abstract (arXiv)

Proteins are complex biomolecules that play a central role in various biological processes, making them critical targets for breakthroughs in molecular biology, medical research, and drug discovery. Deciphering their intricate, hierarchical structures, and diverse functions is essential for advancing our understanding of life at the molecular level. Protein Representation Learning (PRL) has emerged as a transformative approach, enabling the extraction of meaningful computational representations from protein data to address these challenges. In this paper, we provide a comprehensive review of PRL research, categorizing methodologies into five key areas: feature-based, sequence-based, structure-based, multimodal, and complex-based approaches. To support researchers in this rapidly evolving field, we introduce widely used databases for protein sequences, structures, and functions, which serve as essential resources for model development and evaluation. We also explore the diverse applications of these approaches in multiple domains, demonstrating their broad impact. Finally, we discuss pressing technical challenges and outline future directions to advance PRL, offering insights to inspire continued innovation in this foundational field.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 51 of its 221 references that are in the map. First posted on arXiv 20 Mar 2025.

Builds on

51 of 221 references.

References · 51

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38 more
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50 most cited of 51 All 51 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026Exploring the limits of pre-trained embeddings in machine-guided protein design: a case study on predicting AAV vector viabilitycited by 1
  2. 2025Quantifying the Role of OpenFold Components in Protein Structure Predictioncited by 0
  3. 2026Learning Topological Representations of Protein Structure and Dynamicscited by 0