如果没有保障措施,人工智能与生物学的整合可能会加速未来的流行病
Dianzhuo Wang1, Marian Huot1,2, Zechen Zhang3
1Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, United States.
Frontiers in microbiology
|February 9, 2026
概括
蛋白质语言模型 (pLMs) 加快了疫苗和治疗药物的生物设计. 然而,人工智能驱动的蛋白质工程也带来了双重使用的风险,需要在人工智能生物学系统中采取保护措施.
科学领域:
- 生物技术是生物技术.
- 人工智能的人工智能
- 计算生物学 计算生物学
背景情况:
- 人工智能 (AI) 正在彻底改变生物物质的设计.
- 在大量序列数据上训练的蛋白质语言模型 (pLM) 可以预测,生成和优化蛋白质.
- 集成到实验管道中的pLM可以实现快速,闭环的生物设计.
研究的目的:
- 在pLM驱动的蛋白质健身优化方面取得的进展.
- 在病毒演化和实验室工作流程中批判性地评估pLM应用.
- 为集成的人工智能-生物学系统提出框架和保障措施.
主要方法:
- 概述了蛋白质工程的pLM应用的最新进展.
- 分析pLM与实验工作流程的整合,包括主动学习和自动化.
- 为人工智能生物系统开发面向能力的框架.
主要成果:
- pLMs显著加速功能蛋白质的设计和优化.
- 人工智能驱动的方法正在应用于病毒进化研究.
- 当前的人工智能生物学系统存在双重使用风险,需要仔细考虑.
结论:
- 人工智能,特别是pLM,在生物设计方面提供了前所未有的速度,影响了疫苗和治疗发现等领域.
- 人工智能在生物学领域的快速发展需要一个框架来评估综合系统,并解决潜在的双重用途问题.
- 需要进行研究,以在训练和推断阶段为人工智能生物学系统制定保护措施.
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