标题： 人工智能时代的韧性生物安全 显示英文标题

标题： Resilient Biosecurity in the Era of AI-Enabled Bioweapons

摘要： 生成生物学的最新进展使得设计新型蛋白质成为可能，这为药物发现创造了重大机遇，同时也引入了新的风险，包括合成生物武器的潜在发展。现有的生物安全措施主要依赖于推理时的过滤器，如序列比对和蛋白质-蛋白质相互作用（PPI）预测来检测危险输出。在本研究中，我们评估了三种领先的PPI预测工具：AlphaFold 3、AF3Complex和SpatialPPIv2。这些模型在已充分表征的病毒-宿主相互作用上进行了测试，例如与乙型肝炎病毒和SARS-CoV-2相关的相互作用。尽管它们是在许多相同的病毒上训练的，但这些模型未能检测到大量已知的相互作用。值得注意的是，这些工具都无法识别任何经过实验验证的SARS-CoV-2突变体，这些突变体具有确认的结合能力。这些发现表明，当前的预测过滤器对于可靠地标记已知的生物威胁都不足，并且更不可能检测到新的威胁。我们认为应转向以响应为导向的基础设施，包括快速的实验验证、可调整的生物制造以及能够跟上AI驱动发展的监管框架。 显示英文摘要

摘要： Recent advances in generative biology have enabled the design of novel proteins, creating significant opportunities for drug discovery while also introducing new risks, including the potential development of synthetic bioweapons. Existing biosafety measures primarily rely on inference-time filters such as sequence alignment and protein-protein interaction (PPI) prediction to detect dangerous outputs. In this study, we evaluate the performance of three leading PPI prediction tools: AlphaFold 3, AF3Complex, and SpatialPPIv2. These models were tested on well-characterized viral-host interactions, such as those involving Hepatitis B and SARS-CoV-2. Despite being trained on many of the same viruses, the models fail to detect a substantial number of known interactions. Strikingly, none of the tools successfully identify any of the four experimentally validated SARS-CoV-2 mutants with confirmed binding. These findings suggest that current predictive filters are inadequate for reliably flagging even known biological threats and are even more unlikely to detect novel ones. We argue for a shift toward response-oriented infrastructure, including rapid experimental validation, adaptable biomanufacturing, and regulatory frameworks capable of operating at the speed of AI-driven developments.