Report
Controllable Protein Sequence Generation with LLM Preference Optimization
| العنوان: | Controllable Protein Sequence Generation with LLM Preference Optimization |
|---|---|
| المؤلفون: | Liu, Xiangyu, Liu, Yi, Chen, Silei, Hu, Wei |
| سنة النشر: | 2025 |
| المجموعة: | Computer Science Quantitative Biology |
| مصطلحات موضوعية: | Computer Science - Artificial Intelligence, Computer Science - Computational Engineering, Finance, and Science, Quantitative Biology - Quantitative Methods |
| الوصف: | 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. Comment: Accepted in the 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025) |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2501.15007 |
| رقم الانضمام: | edsarx.2501.15007 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |