人工智能剛剛創造出自然界中不存在的病毒（2/2）

Recently The New York Times reported the following:

This A.I. Just Created Viruses Not Found in Nature (2/2)

Scientists trained artificial intelligence on libraries of DNA and then asked the model to create recipes for viral genomes. Sixteen of them were viable, yielding new viruses.

The NYT - By Carl Zimmer - Carl Zimmer covers news about science for The Times and writes the Origins column.

Aug. 6, 2026

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They made this choice in part because scientists know Phi X-174 intimately, having studied it for close to a century. And because it’s a bacteriophage that infects only E. coli, they knew that viruses similar to it would be safe.

Once Evo got familiar with the genomes of Phi X-174 and its kin, the researchers prompted the model to write new versions of its own. Evo generated 700,000 potential versions; the scientists pursued only the ones that looked as if they had the best odds of succeeding.

They ended up making DNA molecules from 285 of Evo’s suggested sequences. When those genomes were ready to test, Mr. King and his colleagues inserted them into bacteria, which they spread across petri dishes.

In many of the dishes, the microbes grew peacefully — Evo’s genomes had failed. But in one dish, researchers noticed clear dots appearing in the cloudy film of bacteria, the telltale sign of multiplying viruses.

The DNA that the scientists had inserted into the bacteria had produced protein shells that contained viral genes. The viruses burst out of the cells, leaving behind the ruptured husks of their dead hosts.

As Mr. King and his colleagues tested more genomes, they saw more clear dots. All told, they discovered that 16 of Evo’s genomes produced viable new viruses.

They proved to be as resilient as natural ones. In fact, some multiplied faster than Phi X-174. “They’re not just sickly versions of stuff that already exists,” said Oliver Crook, a protein chemist at the University of Oxford who was not involved in the new study.

Dr. Crook cautioned that Evo’s viruses were not radically new creations. They tend to be very similar to natural species, relying on the same underlying biology.

Scientists will have to run more experiments to find out if Evo has the same success rate if it’s trained on other groups of viruses. If so, Dr. Crook expected that scientists might find some A.I.-generated viruses that could become useful tools for medicine and biotechnology.

“A lot of our science rests on viruses as technology,” he said. To treat people with genetic disorders, for example, doctors will load genes into viruses, which deliver them into cells.

But along with this hope, Evo’s initial success has raised concerns that A.I. could be used to create deadly pathogens. “You could say, ‘Hey, genomic language model, make me an influenza genome that is modified to be more transmissible or to be more lethal,’” Dr. Hanke speculated.

The potential dangers were already on the minds of the researchers as they trained Evo. They did not provide the model with data about viruses that infect humans, and they excluded similar viruses that infect other animals, plants and fungi.

As a result, Evo can’t generate genomes of viruses that could threaten humans. “We just wanted to be extra careful,” said Brian Hie, a computational biologist at Stanford University and an author of the new study.

Dr. Hanke credited Dr. Hie and his colleagues for taking that precaution. “I think that’s quite commendable,” he said, “because they don’t get any guidance from anywhere on what they should be doing.”

Last week, Dr. Hanke noted, the National Institutes of Health rolled out a new policy for stopping high-risk life science research. The policy would bar scientists from experiments that would make biological agents more harmful.

But computer-based research — such as generating virus DNA with A.I. — “is not prohibited by this policy unless it involves an entity of concern,” the agency said in a statement.

It’s easy to tell if a natural virus like smallpox is an entity of concern. But Dr. Hanke said there’s no consensus on judging the possible danger of a virus made by an A.I. model.

“What is the risk of what I’ve never seen before?” he asked.

Translation

人工智能剛剛創造出自然界中不存在的病毒（2/2）

科學家利用DNA庫訓練人工智能，然後讓模型產生病毒基因組的「配方」。其中16個配方成功生成了新的病毒

（繼續）

他們之所以選擇這種病毒，部分原因是科學家對Phi X-174噬菌體非常了解，已經研究了近一個世紀。而且由於它是一種只感染大腸桿菌的噬菌體，科學家知道與其類似的病毒是安全的。

一旦 Evo 熟悉了Phi​​ X-174及其近親的基因組，研究人員就讓模型生成自己的新版本。 Evo產生了70萬個潛在版本；科學家只選擇了那些看起來最有可能成功的版本來研究。

他們最終利用Evo提供的285個序列建構了DNA分子。當這些基因組準備好後，King先生和他的同事將它們植入細菌，並鋪開在培養皿中。

在許多培養皿中，微生物生長良 - 但Evo的基因組未能成功複製。但在一個培養皿中，研究人員注意到細菌的渾濁薄膜上出現了清晰的斑點，這是有病毒在繁殖中的明顯跡象。

被科學家植入細菌的DNA產生了含有病毒基因的蛋白質外殼。病毒從細胞中破殼而出，留下宿主細胞破裂的殘骸。

隨著King先生和他的同事測試更多基因組，他們觀察到了更多清晰的斑點。最終，他們發現Evo提供的16個基因組產生了可行的新病毒。

這些病毒與天然病毒一樣能迅速適應環境變化。事實上，有些病毒的繁殖速度甚至超過了Phi X-174。並未參與這項新研究的牛津大學蛋白質化學家Oliver Crook說道: 「它們並非只是現有病毒的弱態版本」。

Crook博士提醒說，Evo 生成的病毒並非全新物種。它們往往與天然病毒非常相似，依賴相同的生物學基礎。

科學家需要進行更多實驗，以確定如果 Evo 使用其他病毒群進行訓練，其成功率是否相同。如果成功，Crook博士預計科學家可能會發現一些人工智能生成的病毒，這些病毒有望成為醫學上和生物技術領域的有用工具。

他說:「我們的許多科學研究在技術上都依賴病毒」。例如，為了治療遺傳性疾病患者，醫生會將基因加載到病毒中，然後由它導入細胞。

然而，在帶來希望的同時，Evo 的初步成功也引發了人們的擔憂：人工智能可能會被用於製造致命病原體。 Hanke博士推測地説: 「你可以說，『嘿，基因組語言模型，幫我產生一個經過改造的流感病毒基因組，使其更具傳染性或更致命』」。

研究人員在訓練Evo模型時就已經考慮到了潛在的危險。他們沒有向模型提供感染人類的病毒數據，並且排除了類似能感染其他動物、植物和真菌的病毒。

因此，Evo無法產生可能威脅人類的病毒基因組。史丹佛大學計算生物學家、這項新研究的作者之一Brian Hie說:「我們只是想格外謹慎」。

Hanke博士讚揚了Hie博士及其同事採取的這種預防措施。 他說:“我認為這非常值得稱讚”，“因為他們沒有得到任何關於應該做什麼的指導。”

上週Hanke博士指出，美國國立衛生研究院推出了一項新的政策，旨在停止高風險的生命科學研究。這項政策將禁止科學家進行任何可能使生物物質更具危害性的實驗。

但該機構在聲明中表示，基於電腦的研究 - 例如利用人工智能產生病毒DNA -「並不在此政策的禁止範圍之內，除非它涉及令人擔憂的實體」。

判斷像天花這樣的天然病毒是否令人擔憂是很容易。但Hanke博士表示，對於如何判斷人工智能模型產生的病毒可能存在的危險，目前尚無共識。

他問:「我從怎會知道從未見過的事物會帶來什麼風險？」。

              So, for the first time, scientists have used artificial intelligence to create new kinds of viruses, raising hopes for medical advances while also raising the disturbing possibility that the technology could someday be used to invent dangerous pathogens. DNA has its own rules of grammar and biologists have uncovered some of nature’s grammatical rules, but many remain a mystery. In the present case, Evo the A.I. had scanned about nine trillion nucleotides. Evo eventually recognized patterns common across the tree of life and used them to generate blueprints for new genes encoding proteins that could perform specific jobs. Apparently, we should be very careful in doing these kinds of researches because we do not know what is the risk of what we’ve never seen before.

Note:

1. A bacteriophage (噬菌體), also known informally as a phage, is a virus that infects and replicates within bacteria. Bacteriophages are composed of proteins that encapsulate a DNA or RNA genome, and may have structures that are either simple or elaborate. Their genomes may encode as few as four genes (e.g. MS2) and as many as hundreds of genes. Phages replicate within the bacterium following the injection of their genome into its cytoplasm. Bacteriophages are among the most common and diverse entities in the biosphere. Bacteriophages are ubiquitous viruses, found wherever bacteria exist. (Wikipedia)