AI is transforming life sciences research at unprecedented speed, accelerating discovery across protein structure prediction, genome modeling, and drug development (Jumper et al., 2021; Mak et al., 2024). Yet this rapid advancement, coupled with the open science movement, introduces significant dual-use research concerns that have received limited empirical scrutiny. Here we present the first systematic analysis of dual-use research of concern (DURC) content on open preprint servers. We screened ~52,000 bioRxiv preprints (2024-2025) using a hybrid pipeline of lexical filtering and large language model (LLM) evaluation, scoring metadata across nine DURC, three PEPP, and five governance categories aligned with U.S. and Australia Group oversight frameworks. Our analysis reveals that dual-use-adjacent knowledge is routinely present in openly accessible titles and abstracts, often exceeding established risk thresholds even in studies with legitimate public health objectives. While this mapping captures surface-level information diffusion, it does not measure operational capability, downstream misuse potential, or the substantial technical and biosafety barriers that constrain harmful application. We argue that institutional review processes, funding requirements, and preprint platform policies must evolve to incorporate proactive, metadata-level monitoring without compromising scientific transparency. Ultimately, harmonizing controlled-access mechanisms for high-risk methodologies with open summaries of scientific contributions offers a pragmatic framework for governing AI-accelerated biology at scale.

翻译：人工智能正以前所未有的速度变革生命科学研究，加速了蛋白质结构预测、基因组建模和药物开发领域的发现（Jumper et al., 2021；Mak et al., 2024）。然而，这一快速进展与开放科学运动相结合，引发了令人担忧的双重用途研究问题，而这一问题迄今鲜有实证研究。本文首次对开放预印本服务器上“关注双重用途研究”（DURC）内容进行了系统性分析。我们采用词汇过滤与大语言模型（LLM）评估相结合的混合流程，筛选了约52,000篇bioRxiv预印本（2024-2025年），并根据美国与澳大利亚集团监管框架，对九个DURC类别、三个PEPP类别以及五个治理类别中的元数据进行了评分。分析显示，双重用途相关知识普遍存在于公开可获取的标题和摘要中，即使在具有合理公共卫生目标的研究中，也常常超出既定的风险阈值。尽管这一映射捕捉了表层信息扩散，但并未衡量操作能力、下游误用可能性，或制约有害应用的大量技术与生物安全壁垒。我们主张，机构审查流程、资金要求及预印本平台政策必须同步演进，在不妨碍科学透明性的前提下，纳入主动的元数据层面监控。最终，将高风险方法学的受控访问机制与科学贡献的开放摘要进行协调统一，可为大规模治理人工智能加速的生物学提供一种务实框架。