mlsanityData to 7 Oct 2026

Controllable Protein Sequence Generation with LLM Preference Optimization

arXiv 2501.15007 ↗First posted 25 Jan 2025cs.AI cs.CE q-bio.QMcited by 4

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Abstract (arXiv)

Designing proteins with specific attributes offers an important solution to address biomedical challenges. Pre-trained protein large language models (LLMs) have shown promising results on protein sequence generation. However, to control sequence generation for specific attributes, existing work still exhibits poor functionality and structural stability. In this paper, we propose a novel controllable protein design method called CtrlProt. We finetune a protein LLM with a new multi-listwise preference optimization strategy to improve generation quality and support multi-attribute controllable generation. Experiments demonstrate that CtrlProt can meet functionality and structural stability requirements effectively, achieving state-of-the-art performance in both single-attribute and multi-attribute protein sequence generation.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 17 of its 40 references that are in the map. First posted on arXiv 25 Jan 2025.

Builds on

17 of 40 references.

References · 17

  1. 2023Direct Preference Optimization: Your Language Model is Secretly a Reward Modelcited by 7,916
  2. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  3. 2020Learning to summarize from human feedbackcited by 2,562
  4. 2019Fine-Tuning Language Models from Human Preferencescited by 2,124
  5. 2015A Diversity-Promoting Objective Function for Neural Conversation Modelscited by 1,068
  6. 2024SimPO: Simple Preference Optimization with a Reference-Free Rewardcited by 951
  7. 2023Understanding the Effects of RLHF on LLM Generalisation and Diversitycited by 386
  8. 2023Preference Ranking Optimization for Human Alignmentcited by 283
  9. 2022ProGen2: Exploring the Boundaries of Protein Language Modelscited by 156
  10. 2024Direct Preference Optimization with an Offsetcited by 122
  11. 2024A Note on Shumailov et al. (2024): `AI Models Collapse When Trained on Recursively Generated Data'cited by 114
  12. 2024LiPO: Listwise Preference Optimization through Learning-to-Rankcited by 94
5 more
  1. 2023Structure-informed Language Models Are Protein Designerscited by 68
  2. 2024ProLLaMA: A Protein Large Language Model for Multi-Task Protein Language Processingcited by 53
  3. 2023InstructProtein: Aligning Human and Protein Language via Knowledge Instructioncited by 27
  4. 2024ProtLLM: An Interleaved Protein-Language LLM with Protein-as-Word Pre-Trainingcited by 21
  5. 2024Towards Understanding the Influence of Reward Margin on Preference Model Performancecited by 9

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Cited by year (2026 to 7 Oct)

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

4 papers.

  1. 2025ProtSAE: Disentangling and Interpreting Protein Language Models via Semantically-Guided Sparse Autoencoderscited by 3
  2. 2025AnnoDPO: Protein Functional Annotation Learning with Direct Preference Optimizationcited by 0
  3. 2025g-DPO: Scalable Preference Optimization for Protein Language Modelscited by 0
  4. 2026ProteinOPD: Towards Effective and Efficient Preference Alignment for Protein Designcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Preference optimization of protein language models as a multi-objective binder design paradigm
  2. 2024ProteinGPT: Multimodal LLM for Protein Property Prediction and Structure Understanding
  3. 2024Reinforcement Learning for Sequence Design Leveraging Protein Language Models
  4. 2024TaxDiff: Taxonomic-Guided Diffusion Model for Protein Sequence Generation
  5. 2024Diffusion Language Models Are Versatile Protein Learners
  6. 2025Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization
  7. 2025Steering Generative Models with Experimental Data for Protein Fitness Optimization
  8. 2025CFP-Gen: Combinatorial Functional Protein Generation via Diffusion Language Models