Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization

• URL: http://arxiv.org/abs/2507.10923v1
• Date: Tue, 15 Jul 2025 02:30:33 GMT
• Title: Enhancing Safe and Controllable Protein Generation via Knowledge Preference Optimization
• Authors: Yuhao Wang, Keyan Ding, Kehua Feng, Zeyuan Wang, Ming Qin, Xiaotong Li, Qiang Zhang, Huajun Chen,
• Abstract summary: We propose a framework that integrates prior knowledge via a Protein Safety Knowledge Graph to minimize the risk of generating harmful proteins.<n> Experimental results demonstrate that KPO effectively reduces the likelihood of producing hazardous sequences while maintaining high functionality.
• Score: 38.16596343856071
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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