Scientists Used AI to Create 16 New Viruses
Scientists Used AI to Create 16 New Viruses
科学家利用人工智能创造出 16 种新病毒
For the first time, an artificial intelligence system has created a series of previously unknown viruses capable of infecting and eliminating certain types of bacteria. This breakthrough opens up new possibilities for combating bacterial resistance. However, it also raises concerns about the potential misuse of this technology to design biological weapons. 人工智能系统首次创造出了一系列此前未知的病毒，这些病毒能够感染并消灭特定类型的细菌。这一突破为对抗细菌耐药性开辟了新的可能性。然而，这也引发了人们对该技术可能被滥用于设计生物武器的担忧。
For several years now, scientists have been able to synthesize viruses from scratch; these are typically used to develop and evaluate antiviral drugs and vaccines, as well as to expand our understanding of how these microorganisms behave. However, the production of these viral genomes has primarily relied on replicating previously known pathogens or variants. 多年来，科学家们已经能够从零开始合成病毒；这些病毒通常用于开发和评估抗病毒药物及疫苗，并加深我们对这些微生物行为方式的理解。然而，这些病毒基因组的生产主要依赖于复制已知的病原体或其变体。
In contrast, a new study conducted by scientists at Stanford University and the Arc Institute succeeded in having an AI design simple, functional, and previously unseen viruses based on information contained in the genetic sequences of millions of animals, plants, microbes, bacteria, and viruses found in nature. 相比之下，斯坦福大学和 Arc 研究所的科学家们进行的一项新研究成功地让 AI 设计出了简单、功能齐全且前所未见的病毒，其依据是自然界中数百万种动物、植物、微生物、细菌和病毒的基因序列信息。
The researchers worked with bacteriophages—microorganisms characterized by relatively small genomes, which are relatively easy to synthesize and manipulate under controlled conditions. These viruses infect only bacteria, making them a tool with enormous biotechnological potential and a promising alternative to antibiotics for combating resistant bacterial infections. 研究人员选择了噬菌体作为研究对象——这类微生物的基因组相对较小，在受控条件下易于合成和操作。这些病毒仅感染细菌，使其成为具有巨大生物技术潜力的工具，也是对抗耐药细菌感染的一种有前景的抗生素替代方案。
The creation of these entirely new viruses was based on Evo 1 and Evo 2, foundational AI models developed for computational biology applications. Both algorithms were trained on millions of genomes from all domains of life, with the goal of identifying and learning complex evolutionary patterns, including how genes are typically organized, which sequences are conserved, and the biological constraints that allow an organism to remain functional. 这些全新病毒的创造基于 Evo 1 和 Evo 2，这是为计算生物学应用而开发的基础 AI 模型。这两种算法都在来自生命各个领域的数百万个基因组上进行了训练，旨在识别和学习复杂的进化模式，包括基因的典型组织方式、哪些序列是保守的，以及使生物体保持功能所需的生物学约束。
The experimental design used the bacteriophage Phi X-174—which is capable of infecting the bacterium Escherichia coli (E. coli)—as a reference. The goal was not to reproduce this virus, but rather to use it solely as a guide for the algorithms to generate thousands of completely new genomes with a genetic architecture compatible with infecting E. coli. 实验设计以能够感染大肠杆菌（E. coli）的 Phi X-174 噬菌体作为参考。其目的并非复制这种病毒，而是将其仅作为算法的指南，以生成数千个全新的基因组，这些基因组具有能够感染大肠杆菌的遗传架构。
In other words, the viruses derived from the genomes created by the AI retained the functional organization essential for recognizing the bacterium, inserting their DNA, replicating it, producing new viral particles, and assembling them correctly. However, the specific DNA sequences differed considerably from those observed in naturally occurring bacteriophages. 换句话说，由 AI 创建的基因组衍生出的病毒保留了识别细菌、插入 DNA、复制 DNA、产生新病毒颗粒并正确组装它们所需的功能组织。然而，其具体的 DNA 序列与自然界中观察到的噬菌体有很大不同。
16 New Viruses Created Using AI
利用 AI 创造的 16 种新病毒
The scientists then evaluated the AI-generated genomes to select those most likely to be functional, taking into account factors such as gene organization, the presence of regulatory elements, and other criteria inspired by the biology of the Phi X-174 bacteriophage. 随后，科学家们对 AI 生成的基因组进行了评估，以筛选出最可能具有功能的基因组，同时考虑了基因组织、调控元件的存在以及受 Phi X-174 噬菌体生物学启发的其他标准。
This selection resulted in a sample of 300 genomes, which were artificially synthesized, molecule by molecule, in the laboratory. They were then introduced into E. coli bacteria to test whether they were capable of producing functional viruses. 筛选结果得到了 300 个基因组样本，并在实验室中逐分子地进行了人工合成。随后，这些基因组被引入大肠杆菌中，以测试它们是否能够产生功能性病毒。
Of the 300 synthesized genomes, only 16 gave rise to fully functional bacteriophages, featuring previously unpublished sequences, different genes, new regulatory elements, and even varying genome sizes. The behavior of these viruses also varied: While some infected the bacteria more quickly, others exhibited different abilities to replicate. 在 300 个合成的基因组中，只有 16 个产生了完全功能性的噬菌体，它们具有此前未发表的序列、不同的基因、新的调控元件，甚至不同的基因组大小。这些病毒的行为也各不相同：有些感染细菌的速度更快，而另一些则表现出不同的复制能力。
The research, published this week in the journal Science, also evaluated the ability of AI-generated bacteriophages to combat resistant bacteria. The experiment involved exposing a mixture of AI-designed phages and a mixture of natural phages similar to Phi X-174 to strains of E. coli that had already developed resistance to that virus. 这项本周发表在《科学》杂志上的研究还评估了 AI 生成的噬菌体对抗耐药细菌的能力。实验将 AI 设计的噬菌体混合物与类似于 Phi X-174 的天然噬菌体混合物，分别暴露于已经对该病毒产生耐药性的大肠杆菌菌株中。
The results showed that the AI-generated viruses were able to rapidly overcome bacterial resistance and establish infection. According to the authors, this finding demonstrates “a path toward artificial intelligence–generated phage therapies against rapidly evolving bacterial pathogens.” 结果显示，AI 生成的病毒能够迅速克服细菌的耐药性并建立感染。作者认为，这一发现展示了“一种针对快速进化的细菌病原体，利用人工智能生成噬菌体疗法的途径。”
The Two Sides of the Milestone
里程碑的双面性
The discovery opens up new possibilities for tackling the growing problem of bacterial resistance. According to the researchers, this approach could facilitate the development of personalized treatments capable of evolving at nearly the same rate as the pathogens themselves. 这一发现为解决日益严重的细菌耐药性问题开辟了新的可能性。研究人员表示，这种方法可以促进个性化治疗的发展，使其能够以几乎与病原体本身相同的速度进化。
Although this milestone represents a significant advance for molecular biomedicine, it also raises concerns about the potential malicious use of this technology to develop, for example, new diseases, highly toxic substances, or pathogens capable of triggering a new pandemic. 尽管这一里程碑代表了分子生物医学的重大进步，但它也引发了人们对该技术可能被恶意利用的担忧，例如开发新疾病、高毒性物质或能够引发新大流行的病原体。
Moritz Hanke, a researcher at the Johns Hopkins Center for Health Security, argues that there are currently no safeguards capable of effectively preventing the creation of a lethal virus with the help of AI, telling The New York Times that there is “a huge disconnect” between the speed at which science and technology are advancing and the development of effective regulatory frameworks. 约翰霍普金斯大学健康安全中心的研究员莫里茨·汉克（Moritz Hanke）认为，目前还没有任何保障措施能够有效防止利用 AI 制造致命病毒。他在接受《纽约时报》采访时表示，科学技术的进步速度与有效监管框架的制定之间存在“巨大的脱节”。
The debate surrounding these risks is not new. Three years ago, a study by the Rand Corporation warned that the most advanced AI systems at the time had the capacity to refine the planning and execution of attacks using biological weapons. Now, with the rapid development of this technology, fears are growing that such capabilities will become even greater and more sophisticated. 围绕这些风险的争论并非新鲜事。三年前，兰德公司的一项研究警告称，当时最先进的 AI 系统已经有能力优化利用生物武器进行攻击的计划和执行。如今，随着该技术的飞速发展，人们越来越担心这种能力会变得更加强大和复杂。
The nonprofit organization also warned that the speed at which AI systems evolve often outpaces governments’ capacity for regulatory oversight. 该非营利组织还警告称，AI 系统的进化速度往往超过了政府的监管能力。