AI预测工具无法识别已知病毒突变，亟需更强生物安全响应体系。
Resilient Biosecurity in the Era of AI-Enabled Bioweapons
- 测试AlphaFold3等三款PPI预测模型对病毒-宿主互作的检测能力
- 三种模型均未能识别四种已验证的新冠病毒突变体结合关系
- 提示现有过滤机制难防新型生物武器，需构建快速响应体系
生成生物学的进步使新型蛋白质设计成为可能，既推动药物研发，也带来合成生物武器风险。当前生物安全措施主要依赖推理阶段的序列比对和蛋白-蛋白相互作用（PPI）预测工具来检测危险输出。本研究评估了三种主流PPI预测工具：AlphaFold 3、AF3Complex与SpatialPPIv2，在乙肝病毒及SARS-CoV-2等已知病毒-宿主互作数据上的表现。尽管这些模型训练时覆盖大量病毒数据，却未能识别大量已知互作关系。尤为严重的是，所有工具均未成功检测出四个经实验证实具有结合能力的SARS-CoV-2突变体。结果表明，现有预测性过滤机制对已知威胁都难以可靠识别，更无法应对新型威胁。我们主张转向以响应为核心的生物安全基础设施，包括快速实验验证、可适应的生物制造能力和能跟上AI发展速度的监管框架。
原文摘要 · Abstract (English)
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.
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