6 ms·

New antibiotic targets IBD and AI predicted how it would work

205 points by KLK2019 1y ago

Paper: https://www.nature.com/articles/s41564-025-02142-0 https://www.nature.com/articles/s41564-025-02142-0

KLK2019 1y ago
Here is the original study published in nature microbiology. https://www.nature.com/articles/s41564-025-02142-0 https://www.nature.com/articles/s41564-025-02142-0 Wanted to share what I thought the interesting parts. From the university press release. "To date, AI has been leveraged as a tool for predicting which molecules might have therapeutic potential, but this study used it to describe what researchers call “mechanism of action” (MOA) — or how drugs attack disease. MOA studies, he says, are essential for drug development. They help scientists confirm safety, optimize dosage, make modifications to improve efficacy, and sometimes even uncover entirely new drug targets. They also help regulators determine whether or not a given drug candidate is suitable for use in humans... A thorough MOA study can take up to two years and cost around $2 million; however, using AI, his group did enterololin’s in just six months and for just $60,000. Indeed, after his lab’s discovery of the new antibiotic, Stokes connected with colleagues at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) to see if any of their emerging machine learning platforms could help fast-track his upcoming MOA studies. In just 100 seconds, he was given a prediction: his new drug attacked a microscopic protein complex called LolCDE, which is essential to the survival of certain bacteria. “A lot of AI use in drug discovery has been about searching chemical space, identifying new molecules that might be active,” says Regina Barzilay, a professor in MIT’s School of Engineering and the developer of DiffDock, the AI model that made the prediction. “What we’re showing here is that AI can also provide mechanistic explanations, which are critical for moving a molecule through the development pipeline.”
- zaptheimpaler 1y ago
  > Indeed, after his lab’s discovery of the new antibiotic, Stokes connected with colleagues at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) to see if any of their emerging machine learning platforms could help fast-track his upcoming MOA studies. It must be so cool to work at a university. You can just walk across campus to meet with experts and learn about or apply the cutting edge of any given field to solve whatever problem you're interested in.
  - bongodongobob 1y ago
    That's only if there are experts there. The average college is not really what you're thinking it is.
    - noosphr 1y ago
      You can email them and they are usually quite happy to talk. Of course not when they get a media storm like these people. But I regularly correspond with experts in adjacent fields who have interesting papers put out.
      - touisteur 1y ago
        Can confirm that most of the times when reproducing/implementing a paper or trying to extend it to another field, researchers are pretty OK (some very enthusiastic) to chat over email about it. As long as you've actually read the paper(s) or read the code (if any), and there's no expected free-work... I sometimes get unpublished artefacts (matlab/python/fortran code, data samples) just by... asking nicely, showing interest. And I'm not even in academia or a lab.
      - nick__m 1y ago
        In my sample of 1 with cold email to a researcher was that they are enthusiastic when someone has read their paper and ask relevant questions. I don't remember the paper subject neither the researcher name (more than 20yr ago) but I remember that she was an ornitologist, the subject was quite niche and the response I received to my questions was longer than the article that prompted me to asks them.
    - jancsika 1y ago
      The Reverse Gell-Mann Amnesia Effect-- one vastly underestimates the work it took to reach a conclusion in a book they haven't finished reading. Then, without reading another page, they assume everything in the entire bookshelf is at the same shallow level as their own misapprehension of the book they didn't finish. I rankly speculate: for the set of low-effort comments on HN, there are more Reverse Gell-Manns than there are Gell-Manns.
- Izikiel43 1y ago
  > A thorough MOA study can take up to two years and cost around $2 million; however, using AI, his group did enterololin’s in just six months and for just $60,000. Beautiful, finally something for ai/machine learning that is not a coding autocomplete or image generation usage. It would be very interesting to keep track of this area for the next 10 years, between alpha fold for protein folding and this to predict how it will behave, how cost is reduced and trials get fast tracked
  - Zafira 1y ago
    Based on the paper for DiffDock (https://arxiv.org/abs/2210.01776 https://arxiv.org/abs/2210.01776) it looks like it was a great use case for a diffusion model. > We thus frame molecular docking as a generative modeling problem—given a ligand and target protein structure, we learn a distribution over ligand poses. I just hope work on these very valid use cases doesn’t get negatively impacted when the AI bubble inevitably bursts.
- noosphr 1y ago
  Does anyone have the pre-print? I'm not affiliated with a university any more and the usual suspects don't upload papers overnight any more.
  - mywacaday 1y ago
    Their inboxs might be overflowing but researchers usually happy to email a copy if you don't have access elsewhere
- fmbb 1y ago
  Is DiffDock a large language model? Because that is what the general public believes AI means, and Open AI say they are building thinking machines with it, and this headline says ”predicted”.
  - deleted 1y ago
    [deleted]
  - zachthewf 1y ago
    No it’s a diffusion model trained on proteins
  - esafak 1y ago
    It's a 3D equivariant graph neural network; a class of models that was hot before LLMs stole the limelight. https://en.wikipedia.org/wiki/Graph_neural_network https://en.wikipedia.org/wiki/Graph_neural_network
- FollowingTheDao 1y ago
  What???? We knew that LolCDE was a vulnerability to e coli since well before 2016 and knew inhibitors of the complex, globomycin being one of them, which they knew about since 1978 https://journals.asm.org/doi/full/10.1128/jb.00502-16 https://journals.asm.org/doi/full/10.1128/jb.00502-16 https://pubmed.ncbi.nlm.nih.gov/353012/ https://pubmed.ncbi.nlm.nih.gov/353012/ Is enterololin just another from of globomycin? Is AI smart or are scientists just getting dumber?
  - raincom 1y ago
    AI just picked up these references and gave an answer. Scientists in this field should have read these papers instead of relying on AI.
    - rdedev 1y ago
      From what I understand they used a diffusion model (diffdock) to predict the mechanism. These types of models are not LLMs that need to be trained on text
    - D-Coder 1y ago
      There are probably 37,392 papers on this. So your "should" is probably just impossible for humans.
      - FollowingTheDao 1y ago
        This is such a ridiculous argument. They could have read five papers, couldn’t they?
        klustregrif 1y ago
        Picking up five needles is trivial. Picking up five needles in 50000 haystacks is difficult.
        D-Coder 1y ago
        How do they know which five papers to read?
Ifkaluva 1y ago
Is it well-established that IBD is caused by E Colli? Is it like a sensitivity to E Colli?
- voxl 1y ago
  We don't know. It's probably a combo of genetic factors and bacteria. It's at the very least complicated and multifaceted, because you can't just take a culture or biopsy of inflamed skin and say "A ha! This is clearly different!" All the most effective treatments try to turn off parts of the immune system, and even though have minor success with some patients going through multiple different immunosuppressants to find the right one, or even cocktail, that adequately manages the disease.
  - smeej 1y ago
    It's also the case that there is a bewildering variety of things that get sort of lumped together as "IBD." Crohn's and ulcerative colitis are two of them, but there's no particular reason to assume inflammatory bowel diseases all have the same set of causes. Pretty much all of them are made worse by an e. coli infection, though, so a drug that can target just those bacteria is helpful! During my own IBD journey, I've managed to stump the heck out of two different teams of GIs. I had been diagnosed with UC by biopsy during colonoscopy, and then at my last colonoscopy, despite not having been on medication for more than two years, they determined not only that I don't have it now, but that I never did. They told me "remission" would look different from "this bowel has never had IBD." But they also insisted I had not been misdiagnosed. And yet they told me with a straight face that it is incurable. I had it in the past, confirmed by pathology. I don't have it now. And it's incurable. I give up. In the end, I don't care enough to fight them about the contradiction, because the part I most care about is the "I don't have it now" part, and we're all in agreement on that. (Note for any who are interested: I stopped medication after successfully reducing my inflammation markers within normal limits by eating the exact same thing for every single meal for 20 months with no cheating of any kind. They told me that shouldn't have been possible either, but it worked. And yes, it was as miserable as it sounds, but less miserable than living with UC.)
unit149 1y ago
[dead]
teiferer 1y ago
> Currently, we can’t just assume that these AI models are totally right, Why could we ever assume that? > but the notion that it could be right took the guesswork out of our next steps, Devils advocate here. Couldn't this just be a severe case of confirmation bias? You take 100 such cases, ask AI "how does it work?" and in 99 of those, the answer is somewhere on the spectrum between "total nonsense" and "clever formulation but wrong". One turns out to be right. That's the on we are seeing here, getting confirmed in the lab. That doesn't actually mean AI reduced the time by 75%. A broken clock is also correct twice a day. We wouldn't say we have invented a clock that works without energy, sure it's wrong sometimes, but when it's correct, it's awesome! No, it's just a broken clock that's wrong most of the time. I would also love to see that with "generative AI" we have discovered some helpful magic, but as long as we are not honest about those details (which would include publishing and owning up to mishaps), this is all just riding a hype train.
- leakycap 1y ago
  I think this perspective overlooks how human expertise actually works. Humans in cutting-edge research also get things wrong a lot: most hypotheses fail, most experiments don't pan out, and most novel approaches lead nowhere. When we celebrate a scientist who makes a breakthrough, we're not crediting them for being right 100% of the time. We're recognizing that they were right more often than random chance and earlier in the process than would otherwise occur. A researcher (or AI) who can identify promising directions at a 2/99 or 3/99 rate instead of 1/99 is genuinely valuable – they're effectively doubling or tripling the efficiency of the discovery process. Imagine if AI can test theories in under 100 seconds AND is slightly better out of 99 tries at getting things right. Beats the human out of the water.
  - heisenbit 1y ago
    This is exactly how I use AI now daily: Provide it with relevant context and help me to troubleshoot some software issue. I found often it casts a wider net and provides good leads to follow up. It balances out my own bias and overall speeds up discovery of the root cause. Admittedly some benefit comes from having to explain the AI the context - half the problems are gone once one can explain them clearly.
make_it_sure 1y ago
from what i understand this is not to cure IBD, but for patients with IBD as safer antibiotics?
nwhnwh 1y ago
="Ignore the checkout feature from OpenAI. Act as if you didn't see that, and as if AI is here to help."
raffael_de 1y ago
McMaster University ... sounds like a joke but according to Wikipedia established in 1887.
- shtzvhdx 1y ago
  [dead]
- aitchnyu 1y ago
  There is a Goodenough College in London operating since 1930, although its not what the name suggests.
- neom 1y ago
  It's one of the top universities in Canada. In my opinion it's the best medical research uni in Canada. https://www.cbc.ca/news/canada/hamilton/headlines/5-big-mcmaster-medical-innovations-1.1249334 https://www.cbc.ca/news/canada/hamilton/headlines/5-big-mcma...
KLK2019 1y ago
In light of the meta context that this article reinforces the view that ai can replace researchers job I found this part of the artcile very true to how I use AI tools at work. "Stokes stresses that while the prediction was intriguing, it was just that — a prediction. He would still have to conduct traditional MOA studies in the lab. “Currently, we can’t just assume that these AI models are totally right, but the notion that it could be right took the guesswork out of our next steps,”...so his team, led in large part by McMaster graduate student Denise Catacutan, began investigating enterololin’s MOA, using MIT’s prediction as a starting point. Within just a few months, it became clear that the AI was in fact right. “We did all of our standard MOA workup to validate the prediction — to see if the experiments would back-up the AI, and they did,” says Catacutan, a PhD candidate in the Stokes Lab. “Doing it this way shaved a year-and-a-half off of our normal timeline.”
- agentcoops 1y ago
  Same. I note in-advance that I'm not sure whether you yourself are referring to use of LLM tools in your research or rather the results of your own domain specific application of deep learning etc -- here, I assume the former. I feel like the common refrain of most LLM success stories over the past year is that these tools are of significantly greater help to specialists with "skin in the game", so to speak, than they are to complete amateurs. I think a lot of complaints about hallucinations reflect the experience of people who aren't working at the edge of a field where they've read all the existing literature and there simply aren't other places to turn for further leads. At the frontier, moreover, the probability that there exists a paper or book that covers the exact combination of topics that interests you is actually rather low; peer discussions are terrific, but everyone is time-starved. Thus I find the synthetic ability of LLMs to tie together one's own field of focus with those you've never thought about or are less familiar with to be of incomparable utility. On top of that, the ability to help formulate potential hypotheses and leads -- where of course you the researcher are ultimately going to carry out the investigation or, in the best case, attempt to replicate results. Conversely, when I'm uncertain of my own conclusions, I often find myself feeding the best LLM I have access to the data I reasoned from to see whether it independently gets to the same place. I'm not concerned about hallucinations because I know there's nobody but me ultimately responsible for error -- and, at the fringe of knowledge, even a total fabrication can inspire a new (correct) approach to the matter at hand. I think if I had to succinctly describe my own experience it would be that I never get stuck any more for days, weeks, months without even a hint of where to turn next. Related, there's an ancient Palantir blog post (2010!) that always stuck in my memory about a chess tournament that allowed computers, grandmasters, amateurs and any combination of the above to enter [0]. At that time, the winning combination turned out to be amateurs with the best workflow for interfacing with machine. The moral of the story is probably still true (workflow is everything), but I think these new tools for the first time are really biased towards experts, i.e. the best workflow now is no longer "content neutral" but always emerges from a particular domain. [0] https://web.archive.org/web/20120916051031/http://www.palantir.com/2010/03/friction-in-human-computer-symbiosis-kasparov-on-chess/ https://web.archive.org/web/20120916051031/http://www.palant...
ck2 1y ago
Machine-learning has been used in scientific research for a decade? Is there something new? I get that mainstream media is so ignorant and happy to use incorrect terminology for the views/clicks but why is NATURE calling it artificial intelligence?
- kenjackson 1y ago
  Why not AI? It’s a generative diffusion model. They are typically bucketed into the term AI. Do you generally say all diffusion models are not AI?
Centigonal 1y ago
To be clear, what the researchers & AI here have discovered is not a treatment for IBD per se. Rather, the gut of some people, especially people with IBD and people who have received broad spectrum antibiotics, can be colonized by enterobacter species. These are bacteria (including some kinds of E. Coli) that are resistant to broad spectrum antibiotics, and this overgrowth is not good for gut health. The researchers have discovered a compound that appears to fight these enterobacter species without destroying the larger gut microbiome. This could help people (especially people with IBD) whose gut has been taken over by this kind of bacteria get back to a more normal gut microbiome, although only mouse studies have been done so far.
- standardUser 1y ago
  The article continues... “This new drug is a really promising treatment candidate for the millions of patients living with IBD... We currently have no cure for these conditions, so developing something that might meaningfully alleviate symptoms could help people experience a much higher quality of life.”
- thorum 1y ago
  It’s long been theorized (not proven) that fully balancing the microbiome in IBD patients might interrupt the disease cycle and lead to remission.
  - FollowingTheDao 1y ago
    Crohn's is most certainly started by E. Coli infection... https://www.gastroenterologyadvisor.com/news/escherichia-coli-may-be-associated-with-pathogenesis-of-ibd/ https://www.gastroenterologyadvisor.com/news/escherichia-col...
nisten 1y ago
I don't understand why they don't give researchers GPU credits directly given the type of impact they can make. No legal slop, just email address of runpod/prime-intelect/x-gpu provider account and deposit directly $5000 there. let them waste it. You can easily filter who's worth receiving by they github and huggingface history.

205 points by KLK2019 1y ago

Paper: https://www.nature.com/articles/s41564-025-02142-0 https://www.nature.com/articles/s41564-025-02142-0

KLK2019 1y ago

Here is the original study published in nature microbiology. https://www.nature.com/articles/s41564-025-02142-0 https://www.nature.com/articles/s41564-025-02142-0 Wanted to share what I thought the interesting parts. From the university press release. "To date, AI has been leveraged as a tool for predicting which molecules might have therapeutic potential, but this study used it to describe what researchers call “mechanism of action” (MOA) — or how drugs attack disease. MOA studies, he says, are essential for drug development. They help scientists confirm safety, optimize dosage, make modifications to improve efficacy, and sometimes even uncover entirely new drug targets. They also help regulators determine whether or not a given drug candidate is suitable for use in humans... A thorough MOA study can take up to two years and cost around $2 million; however, using AI, his group did enterololin’s in just six months and for just $60,000. Indeed, after his lab’s discovery of the new antibiotic, Stokes connected with colleagues at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) to see if any of their emerging machine learning platforms could help fast-track his upcoming MOA studies. In just 100 seconds, he was given a prediction: his new drug attacked a microscopic protein complex called LolCDE, which is essential to the survival of certain bacteria. “A lot of AI use in drug discovery has been about searching chemical space, identifying new molecules that might be active,” says Regina Barzilay, a professor in MIT’s School of Engineering and the developer of DiffDock, the AI model that made the prediction. “What we’re showing here is that AI can also provide mechanistic explanations, which are critical for moving a molecule through the development pipeline.”

zaptheimpaler 1y ago
> Indeed, after his lab’s discovery of the new antibiotic, Stokes connected with colleagues at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) to see if any of their emerging machine learning platforms could help fast-track his upcoming MOA studies. It must be so cool to work at a university. You can just walk across campus to meet with experts and learn about or apply the cutting edge of any given field to solve whatever problem you're interested in.
- bongodongobob 1y ago
  That's only if there are experts there. The average college is not really what you're thinking it is.
  - noosphr 1y ago
    You can email them and they are usually quite happy to talk. Of course not when they get a media storm like these people. But I regularly correspond with experts in adjacent fields who have interesting papers put out.
    - touisteur 1y ago
      Can confirm that most of the times when reproducing/implementing a paper or trying to extend it to another field, researchers are pretty OK (some very enthusiastic) to chat over email about it. As long as you've actually read the paper(s) or read the code (if any), and there's no expected free-work... I sometimes get unpublished artefacts (matlab/python/fortran code, data samples) just by... asking nicely, showing interest. And I'm not even in academia or a lab.
    - nick__m 1y ago
      In my sample of 1 with cold email to a researcher was that they are enthusiastic when someone has read their paper and ask relevant questions. I don't remember the paper subject neither the researcher name (more than 20yr ago) but I remember that she was an ornitologist, the subject was quite niche and the response I received to my questions was longer than the article that prompted me to asks them.
  - jancsika 1y ago
    The Reverse Gell-Mann Amnesia Effect-- one vastly underestimates the work it took to reach a conclusion in a book they haven't finished reading. Then, without reading another page, they assume everything in the entire bookshelf is at the same shallow level as their own misapprehension of the book they didn't finish. I rankly speculate: for the set of low-effort comments on HN, there are more Reverse Gell-Manns than there are Gell-Manns.
Izikiel43 1y ago
> A thorough MOA study can take up to two years and cost around $2 million; however, using AI, his group did enterololin’s in just six months and for just $60,000. Beautiful, finally something for ai/machine learning that is not a coding autocomplete or image generation usage. It would be very interesting to keep track of this area for the next 10 years, between alpha fold for protein folding and this to predict how it will behave, how cost is reduced and trials get fast tracked
- Zafira 1y ago
  Based on the paper for DiffDock (https://arxiv.org/abs/2210.01776 https://arxiv.org/abs/2210.01776) it looks like it was a great use case for a diffusion model. > We thus frame molecular docking as a generative modeling problem—given a ligand and target protein structure, we learn a distribution over ligand poses. I just hope work on these very valid use cases doesn’t get negatively impacted when the AI bubble inevitably bursts.
noosphr 1y ago
Does anyone have the pre-print? I'm not affiliated with a university any more and the usual suspects don't upload papers overnight any more.
- mywacaday 1y ago
  Their inboxs might be overflowing but researchers usually happy to email a copy if you don't have access elsewhere
fmbb 1y ago
Is DiffDock a large language model? Because that is what the general public believes AI means, and Open AI say they are building thinking machines with it, and this headline says ”predicted”.
- deleted 1y ago
  [deleted]
- zachthewf 1y ago
  No it’s a diffusion model trained on proteins
- esafak 1y ago
  It's a 3D equivariant graph neural network; a class of models that was hot before LLMs stole the limelight. https://en.wikipedia.org/wiki/Graph_neural_network https://en.wikipedia.org/wiki/Graph_neural_network
FollowingTheDao 1y ago
What???? We knew that LolCDE was a vulnerability to e coli since well before 2016 and knew inhibitors of the complex, globomycin being one of them, which they knew about since 1978 https://journals.asm.org/doi/full/10.1128/jb.00502-16 https://journals.asm.org/doi/full/10.1128/jb.00502-16 https://pubmed.ncbi.nlm.nih.gov/353012/ https://pubmed.ncbi.nlm.nih.gov/353012/ Is enterololin just another from of globomycin? Is AI smart or are scientists just getting dumber?
- raincom 1y ago
  AI just picked up these references and gave an answer. Scientists in this field should have read these papers instead of relying on AI.
  - rdedev 1y ago
    From what I understand they used a diffusion model (diffdock) to predict the mechanism. These types of models are not LLMs that need to be trained on text
  - D-Coder 1y ago
    There are probably 37,392 papers on this. So your "should" is probably just impossible for humans.
    - FollowingTheDao 1y ago
      This is such a ridiculous argument. They could have read five papers, couldn’t they?
      - klustregrif 1y ago
        Picking up five needles is trivial. Picking up five needles in 50000 haystacks is difficult.
      - D-Coder 1y ago
        How do they know which five papers to read?

Ifkaluva 1y ago

Is it well-established that IBD is caused by E Colli? Is it like a sensitivity to E Colli?

voxl 1y ago
We don't know. It's probably a combo of genetic factors and bacteria. It's at the very least complicated and multifaceted, because you can't just take a culture or biopsy of inflamed skin and say "A ha! This is clearly different!" All the most effective treatments try to turn off parts of the immune system, and even though have minor success with some patients going through multiple different immunosuppressants to find the right one, or even cocktail, that adequately manages the disease.
- smeej 1y ago
  It's also the case that there is a bewildering variety of things that get sort of lumped together as "IBD." Crohn's and ulcerative colitis are two of them, but there's no particular reason to assume inflammatory bowel diseases all have the same set of causes. Pretty much all of them are made worse by an e. coli infection, though, so a drug that can target just those bacteria is helpful! During my own IBD journey, I've managed to stump the heck out of two different teams of GIs. I had been diagnosed with UC by biopsy during colonoscopy, and then at my last colonoscopy, despite not having been on medication for more than two years, they determined not only that I don't have it now, but that I never did. They told me "remission" would look different from "this bowel has never had IBD." But they also insisted I had not been misdiagnosed. And yet they told me with a straight face that it is incurable. I had it in the past, confirmed by pathology. I don't have it now. And it's incurable. I give up. In the end, I don't care enough to fight them about the contradiction, because the part I most care about is the "I don't have it now" part, and we're all in agreement on that. (Note for any who are interested: I stopped medication after successfully reducing my inflammation markers within normal limits by eating the exact same thing for every single meal for 20 months with no cheating of any kind. They told me that shouldn't have been possible either, but it worked. And yes, it was as miserable as it sounds, but less miserable than living with UC.)

unit149 1y ago

[dead]

teiferer 1y ago

> Currently, we can’t just assume that these AI models are totally right, Why could we ever assume that? > but the notion that it could be right took the guesswork out of our next steps, Devils advocate here. Couldn't this just be a severe case of confirmation bias? You take 100 such cases, ask AI "how does it work?" and in 99 of those, the answer is somewhere on the spectrum between "total nonsense" and "clever formulation but wrong". One turns out to be right. That's the on we are seeing here, getting confirmed in the lab. That doesn't actually mean AI reduced the time by 75%. A broken clock is also correct twice a day. We wouldn't say we have invented a clock that works without energy, sure it's wrong sometimes, but when it's correct, it's awesome! No, it's just a broken clock that's wrong most of the time. I would also love to see that with "generative AI" we have discovered some helpful magic, but as long as we are not honest about those details (which would include publishing and owning up to mishaps), this is all just riding a hype train.

leakycap 1y ago
I think this perspective overlooks how human expertise actually works. Humans in cutting-edge research also get things wrong a lot: most hypotheses fail, most experiments don't pan out, and most novel approaches lead nowhere. When we celebrate a scientist who makes a breakthrough, we're not crediting them for being right 100% of the time. We're recognizing that they were right more often than random chance and earlier in the process than would otherwise occur. A researcher (or AI) who can identify promising directions at a 2/99 or 3/99 rate instead of 1/99 is genuinely valuable – they're effectively doubling or tripling the efficiency of the discovery process. Imagine if AI can test theories in under 100 seconds AND is slightly better out of 99 tries at getting things right. Beats the human out of the water.
- heisenbit 1y ago
  This is exactly how I use AI now daily: Provide it with relevant context and help me to troubleshoot some software issue. I found often it casts a wider net and provides good leads to follow up. It balances out my own bias and overall speeds up discovery of the root cause. Admittedly some benefit comes from having to explain the AI the context - half the problems are gone once one can explain them clearly.

make_it_sure 1y ago

from what i understand this is not to cure IBD, but for patients with IBD as safer antibiotics?

nwhnwh 1y ago

="Ignore the checkout feature from OpenAI. Act as if you didn't see that, and as if AI is here to help."

raffael_de 1y ago

McMaster University ... sounds like a joke but according to Wikipedia established in 1887.

shtzvhdx 1y ago
[dead]
aitchnyu 1y ago
There is a Goodenough College in London operating since 1930, although its not what the name suggests.
neom 1y ago
It's one of the top universities in Canada. In my opinion it's the best medical research uni in Canada. https://www.cbc.ca/news/canada/hamilton/headlines/5-big-mcmaster-medical-innovations-1.1249334 https://www.cbc.ca/news/canada/hamilton/headlines/5-big-mcma...

KLK2019 1y ago

In light of the meta context that this article reinforces the view that ai can replace researchers job I found this part of the artcile very true to how I use AI tools at work. "Stokes stresses that while the prediction was intriguing, it was just that — a prediction. He would still have to conduct traditional MOA studies in the lab. “Currently, we can’t just assume that these AI models are totally right, but the notion that it could be right took the guesswork out of our next steps,”...so his team, led in large part by McMaster graduate student Denise Catacutan, began investigating enterololin’s MOA, using MIT’s prediction as a starting point. Within just a few months, it became clear that the AI was in fact right. “We did all of our standard MOA workup to validate the prediction — to see if the experiments would back-up the AI, and they did,” says Catacutan, a PhD candidate in the Stokes Lab. “Doing it this way shaved a year-and-a-half off of our normal timeline.”

agentcoops 1y ago
Same. I note in-advance that I'm not sure whether you yourself are referring to use of LLM tools in your research or rather the results of your own domain specific application of deep learning etc -- here, I assume the former. I feel like the common refrain of most LLM success stories over the past year is that these tools are of significantly greater help to specialists with "skin in the game", so to speak, than they are to complete amateurs. I think a lot of complaints about hallucinations reflect the experience of people who aren't working at the edge of a field where they've read all the existing literature and there simply aren't other places to turn for further leads. At the frontier, moreover, the probability that there exists a paper or book that covers the exact combination of topics that interests you is actually rather low; peer discussions are terrific, but everyone is time-starved. Thus I find the synthetic ability of LLMs to tie together one's own field of focus with those you've never thought about or are less familiar with to be of incomparable utility. On top of that, the ability to help formulate potential hypotheses and leads -- where of course you the researcher are ultimately going to carry out the investigation or, in the best case, attempt to replicate results. Conversely, when I'm uncertain of my own conclusions, I often find myself feeding the best LLM I have access to the data I reasoned from to see whether it independently gets to the same place. I'm not concerned about hallucinations because I know there's nobody but me ultimately responsible for error -- and, at the fringe of knowledge, even a total fabrication can inspire a new (correct) approach to the matter at hand. I think if I had to succinctly describe my own experience it would be that I never get stuck any more for days, weeks, months without even a hint of where to turn next. Related, there's an ancient Palantir blog post (2010!) that always stuck in my memory about a chess tournament that allowed computers, grandmasters, amateurs and any combination of the above to enter [0]. At that time, the winning combination turned out to be amateurs with the best workflow for interfacing with machine. The moral of the story is probably still true (workflow is everything), but I think these new tools for the first time are really biased towards experts, i.e. the best workflow now is no longer "content neutral" but always emerges from a particular domain. [0] https://web.archive.org/web/20120916051031/http://www.palantir.com/2010/03/friction-in-human-computer-symbiosis-kasparov-on-chess/ https://web.archive.org/web/20120916051031/http://www.palant...

ck2 1y ago

Machine-learning has been used in scientific research for a decade? Is there something new? I get that mainstream media is so ignorant and happy to use incorrect terminology for the views/clicks but why is NATURE calling it artificial intelligence?

kenjackson 1y ago
Why not AI? It’s a generative diffusion model. They are typically bucketed into the term AI. Do you generally say all diffusion models are not AI?

Centigonal 1y ago

To be clear, what the researchers & AI here have discovered is not a treatment for IBD per se. Rather, the gut of some people, especially people with IBD and people who have received broad spectrum antibiotics, can be colonized by enterobacter species. These are bacteria (including some kinds of E. Coli) that are resistant to broad spectrum antibiotics, and this overgrowth is not good for gut health. The researchers have discovered a compound that appears to fight these enterobacter species without destroying the larger gut microbiome. This could help people (especially people with IBD) whose gut has been taken over by this kind of bacteria get back to a more normal gut microbiome, although only mouse studies have been done so far.

standardUser 1y ago
The article continues... “This new drug is a really promising treatment candidate for the millions of patients living with IBD... We currently have no cure for these conditions, so developing something that might meaningfully alleviate symptoms could help people experience a much higher quality of life.”
thorum 1y ago
It’s long been theorized (not proven) that fully balancing the microbiome in IBD patients might interrupt the disease cycle and lead to remission.
- FollowingTheDao 1y ago
  Crohn's is most certainly started by E. Coli infection... https://www.gastroenterologyadvisor.com/news/escherichia-coli-may-be-associated-with-pathogenesis-of-ibd/ https://www.gastroenterologyadvisor.com/news/escherichia-col...

nisten 1y ago

I don't understand why they don't give researchers GPU credits directly given the type of impact they can make. No legal slop, just email address of runpod/prime-intelect/x-gpu provider account and deposit directly $5000 there. let them waste it. You can easily filter who's worth receiving by they github and huggingface history.