Artificial Intelligence used to design new viruses
bbc.co.uk
bbc.co.uk
Their phages have only 5400 base pairs, but the hepatitis virus has around 3000, and ebola only had ~19,000. That doesn't mean making a novel lethal virus is easy, but if anyone pulls it off, it would make Covid look like a picnic.
Why? Presumably because, of course, the new government rules imposed on doctors for antibiotics are about saving money and NOT about fighting resistance. The 5th antibiotic is ... not expensive in an absolute sense, but compared to the other 4 it's pretty expensive. The guideline from scientists is that if you decide to use it you need to nearly overdose the patient on it, despite that that is not necessary to cure them. Doing that is much more expensive (not that much more expensive really, it just looks impressive in % and presumably in excel sheets), but fights resistance. We're not doing that, and so we have ... probably not even a decade ... and then we have no treatment anymore against bacterial infection.
And in a decade, we will still be surrounded by lethal bacteria. Look at your hand. It's overwhelmingly likely you can't see them but you're looking at lethal pneumococci, several species. This is a clean hand we're taking about. Even a freshly scrubbed hand of a surgeon will still have quite a few bacteria. Grab a handful of soil. You know, the kind plants grow in. Your hand now has botulinum-producing bacteria, as well as many other toxins, tuberculosis (definitely if you're anywhere near a farm), tetanus, and the list goes on and on), some of which we have no vaccine against at all, and many we don't vaccinate for because it's not practical.
So the choice is between developing DNA sequences for viruses, or having a pandemic 100x worse than COVID every 30 years or so.
There is no choice here.
Really I don't get how multicellular life survived it.
>There are actually dozens of individual medicines and more than ten major classes of antibiotics used to treat bacterial infections—not just five
So yes, counting useless antibiotics (useless in curing humans, not necessarily useless in research or other procedures like making custom antibodies), there are 10 classes (although if you're going there, why not go further than 10? But whatever)
So let's classify ChatGPT's suggestions:
1) Fully defeated by Bacteria: penicillins, macrolides, fluoroquinolones (yes, very occasionally there will be something these do still work against, however no sane physician will "trust" these, and nobody outside of research bothers testing anything against them)
2) Mostly defeated: carbapenems, cephalosporins, and vancomycin. If you know what you're dealing with these can be used, but if even slightly in doubt what bacterium it is, we do not go here. Without a lab test confirming that what's ailing the patient responds to these (positive confirmation needed), do not use. This also means they're not useful in emergencies.
3) Useful ... except: tetracyclines, aminoglycosides, "combinations", Daptomycin (and lipopeptides in general), oxazolidinones. These can be used "by default". These are "the 5" that we're talking about.
And yes, I got updated, until 2024 there was no known resistance to Daptomycin (which is an "updated" version of Vancomycin in some ways). There now are known resistant strains against all of them, which means we've actually climbed one more rung on the ladder.
Current state: for every antibiotic we know there is a disease that just totally ignores that antibiotic. However we are not yet in the state that one disease is immune against everything (although there are MRSA strains that are highly resistant to 4 out of 5, in India, and frankly, probably in China, except they're lying about it. These strains are also active in Pakistan and probably Afghanistan and Iran except nobody really knows for sure)
I'm skeptical of this often cited claim. The more you use it the greater the evolutionary pressure you place and the more you force evolved resistance. If you overuse an antibiotic, you kill off all of the bacteria that could compete with the resistant strains, thus strongly promoting resistance.
I posit that like with all other evolutionary pressures, reducing the pressure is the right approach.
I never understood the "it's really important for us to blame the lab leak" angle. I certainly want to know what actually happened, but some people really, really want it to be a lab leak. Perhaps you can explain?
Worth specifying that these were hand selected candidates. Out of these human selected ones, 5% were successful.
The Stanford researchers picked the most promising 302 AI designs and synthesised them in the lab. Of these, 16 proved effective at killing E. coli bacteria.
Based on my quick reading, it feels like the BBC left out a ton of context that makes this work feel more novel than it is. There's a ton of prior art here (including some from the same team)
For example, older iterations of the same model family were used to generate novel genes and proteins. Including anti-CRISPR proteins that were tested against phages. That work is honestly more interesting.
From, "Semantic design of functional de novo genes from a genomic language model," https://www.nature.com/articles/s41586-025-09749-7
Anti-CRISPRs (Acrs) are proteins used by phages to neutralize bacterial CRISPR–Cas systems (Fig. 3a). Many Acrs represent striking examples of rapid protein evolution, appearing as novel innovations without detectable similarity to other protein families [..]
[..] After filtering for size, complexity and structure (Extended Data Fig. 5b), we next used PaCRISPR, a machine learning model trained to identify potential Acr proteins, to evaluate our generated candidates [..]
[..] we further identified five proteins (EvoAcr1–5) that demonstrated strong protection against SpCas9 cleavage in both liquid culture survival assays (Fig. 3f and Extended Data Fig. 5d) and phage infection experiments (Fig. 3g and Extended Data Fig. 5f), while maintaining normal host growth
Of note, semantic design is not limited to Evo 1.5 and can leverage any language model trained on prokaryotic or phage genomes. Improvements in genomic language models, as well as a better understanding of prokaryotic gene synteny, should therefore directly translate to improvements in semantic design.
Prior pre-publication / release from 2025 called "AI-Designed Phages," the piece is really well written and I recommend reading it, https://www.asimov.press/p/ai-phages
edit - as flobosg notes one of them describes the model,
"Genome modelling and design across all domains of life with Evo 2," March 2026, https://www.nature.com/articles/s41586-026-10176-5
pre-print of the asimov press piece, "Generative design of novel bacteriophages with genome language models," September 2025, https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1
All research comes from somewhere. There's an entire sub-field / world here that goes back decades at this point.
Leon Chan refactored a bacteriophage by hand in 2005 https://link.springer.com/article/10.1038/msb4100025
The first synthetic organism was Venter's mycoplasma laboratorium / Synthia in 2010.
The first organism to be synthetically assembled (not designed, but assembled ikea style) was a phage in 2003 also by Venter & co.
In 2021 another team made entities that can "find and combine building blocks into self-copies," Kinematic self-replication in reconfigurable organisms https://pmc.ncbi.nlm.nih.gov/articles/PMC8670470/
From 2019 / 2020, algorithmically designed synthetic life / "xenobots", https://www.nytimes.com/2020/04/03/science/xenobots-robots-f... https://en.wikipedia.org/wiki/Xenobot
Or, "Team builds first living robots—that can reproduce" https://wyss.harvard.edu/news/team-builds-first-living-robot... , 2021
Zurich in 2019, https://web.archive.org/web/20191008040536/https://www.eurek...
And if you expand computing to include molecular processes for genes, then there's stuff like PACE that uses phages as a process, https://en.wikipedia.org/wiki/Phage-assisted_continuous_evol...
(final edit - thanks flobosg!)
That link is a commentary about the preprint of the article mentioned in the BBC article.
After your editing:
> Same team, prior publication / release from 2025 called "AI-Designed Phages," https://www.asimov.press/p/ai-phages
It is the same work, just not published in Science.
"Genome modelling and design across all domains of life with Evo 2," March 2026, https://www.nature.com/articles/s41586-026-10176-5
However, this was a preprint, "Generative design of novel bacteriophages with genome language models," September 2025, https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1
Even older conference paper, I think, February 19th, 2025, https://arcinstitute.org/manuscripts/Evo2
edit - I updated to delete my over claim. Thank you!
I can imagine a situation where a bio weapon pathogen could potentially be engineered to evade the immune system by creating a pathogen that is both extremely virulent and has a rapid mutation rate. Rapid mutation rates make it difficult to create an effective vaccine and also difficult for the immune system to learn to recognize the pathogen.
But whether open weight AI models can allow people to create such bioweapons - it does seem far-fetched to me. I think it's far-fetched because biological systems contain so many elements that we don't even know about, do not exist in any data set, and so they could not be included in the model.
In order for a pathogen to be successful, there are a number of environmental barriers that it has to endure in order to reach the host, survive in the host, proliferate in the host, and damage the host. Environmental barriers outside the host and inside the host.
I know that AI can be used to design genes to encode synthetic proteins. So could an open weight AI model result in the creation of genes that encode harmful proteins and then bioengineer pathogen to produce the proteins. But there are so many unknowns that can affect whether this pathogen can survive at all. So I would say from my limited knowledge, I would say it's far-fetched.
As noted in this paper⁽¹⁾, ”Publicly releasing model weights is an act of irreversible proliferation. Once weights are released, they can never be recalled.”
Do you really want to risk it on your off-the-cuff reasoning that it’s “far-fetched”?
⁽¹⁾ https://www.lesswrong.com/posts/qmQFHCgCyEEjuy5a7/lora-fine-...
It is paywalled, but this seems to be the corresponding preprint: https://www.biorxiv.org/content/10.1101/2025.09.12.675911v1....