mlsanityData to 14 Oct 2026

Generative AI for Controllable Protein Sequence Design: A Survey

arXiv 2402.10516 ↗First posted 16 Feb 2024q-bio.BM cs.AI cs.LGcited by 8

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Abstract (arXiv)

The design of novel protein sequences with targeted functionalities underpins a central theme in protein engineering, impacting diverse fields such as drug discovery and enzymatic engineering. However, navigating this vast combinatorial search space remains a severe challenge due to time and financial constraints. This scenario is rapidly evolving as the transformative advancements in AI, particularly in the realm of generative models and optimization algorithms, have been propelling the protein design field towards an unprecedented revolution. In this survey, we systematically review recent advances in generative AI for controllable protein sequence design. To set the stage, we first outline the foundational tasks in protein sequence design in terms of the constraints involved and present key generative models and optimization algorithms. We then offer in-depth reviews of each design task and discuss the pertinent applications. Finally, we identify the unresolved challenges and highlight research opportunities that merit deeper exploration.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 50 of its 219 references that are in the map. First posted on arXiv 16 Feb 2024.

Builds on

50 of 219 references.

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Built on by

Cited by year (2026 to 14 Oct)

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

8 papers.

  1. 2024Bridge-IF: Learning Inverse Protein Folding with Markov Bridgescited by 9
  2. 2024Multi-Modal CLIP-Informed Protein Editingcited by 6
  3. 2025ProtInvTree: Deliberate Protein Inverse Folding with Reward-guided Tree Searchcited by 6
  4. 2025ProtFlow: Fast Protein Sequence Design via Flow Matching on Compressed Protein Language Model Embeddingscited by 4
  5. 2024Recent advances in interpretable machine learning using structure-based protein representationscited by 1
  6. 2025A Generalist Cross-Domain Molecular Learning Framework for Structure-Based Drug Discoverycited by 0
  7. 2025Property-Isometric Variational Autoencoders for Sequence Modeling and Designcited by 0
  8. 2026Advances in Machine Learning for Directed Evolution: A Five-Year Retrospectivecited by 0