Controllable Protein Sequence Generation with LLM Preference Optimization
Xiangyu Liu, Yi Liu, Silei Chen, Wei Hu
Abstract
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.
BibTeX
@article{Liu_Liu_Chen_Hu_2025, title={Controllable Protein Sequence Generation with LLM Preference Optimization}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/32030}, DOI={10.1609/aaai.v39i1.32030}, abstractNote={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.}, number={1}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Liu, Xiangyu and Liu, Yi and Chen, Silei and Hu, Wei}, year={2025}, month={Apr.}, pages={505-513} }