@inproceedings{wang-etal-2025-enhancing-safe,
title = "Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization",
author = "Wang, Yuhao and
Ding, Keyan and
Feng, Kehua and
Wang, Zeyuan and
Qin, Ming and
Li, Xiaotong and
Zhang, Qiang and
Chen, Huajun",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-long.616/",
doi = "10.18653/v1/2025.acl-long.616",
pages = "12553--12569",
ISBN = "979-8-89176-251-0",
abstract = "Protein language models have emerged as powerful tools for sequence generation, offering substantial advantages in functional optimization and *denovo* design. However, these models also present significant risks of generating harmful protein sequences, such as those that enhance viral transmissibility or evade immune responses. These concerns underscore critical biosafety and ethical challenges. To address these issues, we propose a Knowledge-guided Preference Optimization (KPO) framework that integrates prior knowledge via a Protein Safety Knowledge Graph. This framework utilizes an efficient graph pruning strategy to identify preferred sequences and employs reinforcement learning to minimize the risk of generating harmful proteins. Experimental results demonstrate that KPO effectively reduces the likelihood of producing hazardous sequences while maintaining high functionality, offering a robust safety assurance framework for applying generative models in biotechnology."
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<abstract>Protein language models have emerged as powerful tools for sequence generation, offering substantial advantages in functional optimization and *denovo* design. However, these models also present significant risks of generating harmful protein sequences, such as those that enhance viral transmissibility or evade immune responses. These concerns underscore critical biosafety and ethical challenges. To address these issues, we propose a Knowledge-guided Preference Optimization (KPO) framework that integrates prior knowledge via a Protein Safety Knowledge Graph. This framework utilizes an efficient graph pruning strategy to identify preferred sequences and employs reinforcement learning to minimize the risk of generating harmful proteins. Experimental results demonstrate that KPO effectively reduces the likelihood of producing hazardous sequences while maintaining high functionality, offering a robust safety assurance framework for applying generative models in biotechnology.</abstract>
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%0 Conference Proceedings
%T Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization
%A Wang, Yuhao
%A Ding, Keyan
%A Feng, Kehua
%A Wang, Zeyuan
%A Qin, Ming
%A Li, Xiaotong
%A Zhang, Qiang
%A Chen, Huajun
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-251-0
%F wang-etal-2025-enhancing-safe
%X Protein language models have emerged as powerful tools for sequence generation, offering substantial advantages in functional optimization and *denovo* design. However, these models also present significant risks of generating harmful protein sequences, such as those that enhance viral transmissibility or evade immune responses. These concerns underscore critical biosafety and ethical challenges. To address these issues, we propose a Knowledge-guided Preference Optimization (KPO) framework that integrates prior knowledge via a Protein Safety Knowledge Graph. This framework utilizes an efficient graph pruning strategy to identify preferred sequences and employs reinforcement learning to minimize the risk of generating harmful proteins. Experimental results demonstrate that KPO effectively reduces the likelihood of producing hazardous sequences while maintaining high functionality, offering a robust safety assurance framework for applying generative models in biotechnology.
%R 10.18653/v1/2025.acl-long.616
%U https://aclanthology.org/2025.acl-long.616/
%U https://doi.org/10.18653/v1/2025.acl-long.616
%P 12553-12569
Markdown (Informal)
[Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization](https://aclanthology.org/2025.acl-long.616/) (Wang et al., ACL 2025)
ACL
- Yuhao Wang, Keyan Ding, Kehua Feng, Zeyuan Wang, Ming Qin, Xiaotong Li, Qiang Zhang, and Huajun Chen. 2025. Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 12553–12569, Vienna, Austria. Association for Computational Linguistics.