PERSPECTIVE article
Front. Microbiol.
Sec. Systems Microbiology
This article is part of the Research TopicArtificial Intelligence and the Future of BiosecurityView all articles
Without Safeguard, AI-Bio Integration Risks Accelerating Future Pandemics
Provisionally accepted
- 1Harvard University, Cambridge, United States
- 2Princeton University, Princeton, United States
- 3Massachusetts Institute of Technology, Cambridge, United States
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Artificial intelligence now shapes the design of biological matter. Protein language models (pLMs), trained on millions of natural sequences, can predict, generate, and optimize functional proteins with minimal human input. When embedded in experimental pipelines, these systems enable closed-loop biological design at unprecedented speed. The same convergence that accelerates vaccine and therapeutic discovery, however, also creates new dual-use risks. We first map recent progress in using pLMs for fitness optimization across proteins, then critically assess how these approaches have been applied to viral evolution and how they intersect with laboratory workflows, including active learning and automation. Building on this analysis, we outline a capability-oriented framework for integrated AI–biology systems, identify evaluation challenges specific to biological outputs, and propose research directions for training-and inference-time safeguards.
Keywords: Protein Language Models, Intelligent Automated Biology, Dual use research of concern (DURC), biosecurity, Protein design
Received: 28 Oct 2025; Accepted: 26 Nov 2025.
Copyright: © 2025 Wang, Huot, Zhang, Jiang, Shakhnovich and Esvelt. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Dianzhuo Wang
Eugene I. Shakhnovich
Kevin M. Esvelt
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