mlsanityData to 7 Oct 2026

Protein sequence design with deep generative models

arXiv 2104.04457 ↗First posted 9 Apr 2021q-bio.QM cs.LG q-bio.BMcited by 35

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Abstract (arXiv)

Protein engineering seeks to identify protein sequences with optimized properties. When guided by machine learning, protein sequence generation methods can draw on prior knowledge and experimental efforts to improve this process. In this review, we highlight recent applications of machine learning to generate protein sequences, focusing on the emerging field of deep generative methods.

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). Builds on 24 of its 87 references that are in the map. First posted on arXiv 9 Apr 2021.

Builds on

24 of 87 references.

References · 24

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Cited by year

Citing papers per year: 2021: 3; 2022: 5; 2023: 15; 2024: 6; 2025: 6.

35 papers.

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  1. 2022PiFold: Toward effective and efficient protein inverse foldingcited by 82
  2. 2023A Text-guided Protein Design Frameworkcited by 61
  3. 2022Learning Hierarchical Protein Representations via Complete 3D Graph Networkscited by 54
  4. 2022Conformal Prediction Under Feedback Covariate Shift for Biomolecular Designcited by 53
  5. 2022Regression Transformer: Concurrent sequence regression and generation for molecular language modelingcited by 49
  6. 2023A Latent Diffusion Model for Protein Structure Generationcited by 28
  7. 2023Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using Samplescited by 25
  8. 2021Deep Extrapolation for Attribute-Enhanced Generationcited by 18
  9. 2021Deep Generative Modeling for Protein Designcited by 17
  10. 2022Plug & Play Directed Evolution of Proteins with Gradient-based Discrete MCMCcited by 17
  11. 2023De novo protein design using geometric vector field networkscited by 14
  12. 2021Adaptive machine learning for protein engineeringcited by 13
23 more
  1. 2023Knowledge-Design: Pushing the Limit of Protein Design via Knowledge Refinementcited by 11
  2. 2024Diffusion on language model encodings for protein sequence generationcited by 11
  3. 2023DiffSDS: A language diffusion model for protein backbone inpainting under geometric conditions and constraintscited by 10
  4. 2023A Multi-Modal Contrastive Diffusion Model for Therapeutic Peptide Generationcited by 10
  5. 2024Generative AI for Controllable Protein Sequence Design: A Surveycited by 8
  6. 2023Thompson Sampling with Diffusion Generative Priorcited by 7
  7. 2025Steering Generative Models with Experimental Data for Protein Fitness Optimizationcited by 7
  8. 2023Diffusion map particle systems for generative modelingcited by 6
  9. 2024De novo antibody design with SE(3) diffusioncited by 5
  10. 2024Mask prior-guided denoising diffusion improves inverse protein foldingcited by 5
  11. 2025A theoretical framework for overfitting in energy-based modelingcited by 5
  12. 2024α-TCVAE: On the relationship between Disentanglement and Diversitycited by 4
  13. 2024Function-Guided Conditional Generation Using Protein Language Models with Adapterscited by 3
  14. 2023De Novo Drug Design with Joint Transformerscited by 2
  15. 2025Discrete Compositional Generation via General Soft Operators and Robust Reinforcement Learningcited by 2
  16. 2023Target specific peptide design using latent space approximate trajectory collectorcited by 1
  17. 2023ProtFIM: Fill-in-Middle Protein Sequence Design via Protein Language Modelscited by 1
  18. 2023Graph Representation Learning for Interactive Biomolecule Systemscited by 1
  19. 2023Target-aware Variational Auto-encoders for Ligand Generation with Multimodal Protein Representation Learningcited by 0
  20. 2023Best practices for machine learning in antibody discovery and developmentcited by 0
  21. 2025Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only datacited by 0
  22. 2025RNAGenScape: Property-Guided, Optimized Generation of mRNA Sequences with Manifold Langevin Dynamicscited by 0
  23. 2025Reflection Pretraining Enables Token-Level Self-Correction in Biological Sequence Modelscited by 0