[HN Gopher] New superbug-killing antibiotic discovered using AI
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New superbug-killing antibiotic discovered using AI
Author : tsenapathy
Score : 137 points
Date : 2023-05-25 15:07 UTC (7 hours ago)
(HTM) web link (www.bbc.com)
(TXT) w3m dump (www.bbc.com)
| jbullock35 wrote:
| In the title of this post, it would help to have a hyphen between
| "Superbug" and "killing," as in the BBC article. When I first
| read the title of this HN post--without the hyphen--I thought the
| article was going to be about a dangerous new superbug that is
| defeating antibiotics.
| 71a54xd wrote:
| Hopefully they can finally apply something to combat C. Difficile
| Fomite wrote:
| There's a lot of research into ways to treat C. difficile.
|
| Some of it is genuinely antimicrobial stewardship - keeping
| people off proton pump inhibitors and certain classes of
| antibiotics.
|
| Fecal transplant is also getting both more viable and better
| understood (this is what I worked on for my dissertation).
|
| There's also some investigation of alternative therapeutics for
| C. diff that aren't just vancomycin.
| hedora wrote:
| I read this as "AI-equipped superbugs have figured out how to
| kill antibiotics," which almost makes enough sense to be bad
| headline.
| stuff4ben wrote:
| This is where we need to see more investment in AI. Not always
| replacing humans, but rather augmenting and helping them. Sure
| there are efficiency gains that are needed in a capitalist
| society that AI can help with, but I'd rather see gains in other
| areas.
| ChatGTP wrote:
| I feel the same, it would be a much nicer and less agitating
| goal and much more inline with all the "promises" which have
| been made for the last 10-20 years about why we should be
| excited and invest in AI development.
|
| The talking computers goals are "weird", the getting help with
| solving problems part seems more sane.
| carlmr wrote:
| Honestly it's much more likely anyway, the AI hype will die
| down a little again soon, leading to a more realistic view of
| AI being a better personal assistant now.
| manicennui wrote:
| I really wish we would use more precise terminology when
| discussing and reporting on these things. It sounds like they
| used machine learning, and I wish we'd stop calling all machine
| learning AI, but that ship has probably sailed.
| drabbiticus wrote:
| The ship has definitely sailed. See
| https://ai.engineering.columbia.edu/ai-vs-machine-learning/
| which states:
|
| > Artificial intelligence (AI) and machine learning are often
| used interchangeably, but machine learning is a subset of the
| broader category of AI.
|
| and
|
| > Machine learning is a pathway to artificial intelligence.
| This subcategory of AI uses algorithms to automatically learn
| insights and recognize patterns from data, applying that
| learning to make increasingly better decisions.
| [deleted]
| kylehotchkiss wrote:
| Great, keep the recipe under wraps, never allow it to be
| manufactured abroad, and only allow hospitals to distribute it.
| Otherwise this is just going to become the frontline antibiotic
| given whenever somebody gets so little as a cough in most of the
| world :/. The problem with our existing antibiotics was how
| generously they were given out to anybody with any symptom.
| droopyEyelids wrote:
| This point is moot because the antibiotics will be manufactured
| overseas where all the waste & runoff is discarded by pumping
| it into local rivers, causing ecosystem wide adaptation to the
| material.
|
| This is currently the situation in India, where the bulk of our
| antibiotics are manufactured.
| drabbiticus wrote:
| Your comment is grounded in some truths and yet in this
| specific instance is very off base.
|
| From the article:
|
| > Curiously, this experimental antibiotic had no effect on
| other species of bacteria, and works only on A. baumannii.
|
| This is an oversimplified claim made by the BBC article, but
| broadly aligns with the research paper claim that the
| identified drug is specifically not broad-spectrum and not
| active against e.g. Pseudomonal and Staphylococcal species. The
| identified drug candidate is unlikely to ever see broad use
| because of the low activity against many clinically relevant
| bacteria.
|
| The most common use case for this candidate, should it see
| approval through the FDA process for this indication, will be
| for treatment of hospitalized patients who, while waiting for
| culture results, have failed broad-spectrum regimes and whose
| culture results demonstrate Acinetobacter infections.
| protastus wrote:
| Regulate the veterinary use too. Some antibiotics have seen
| unrestricted large scale use with livestock.
| Fomite wrote:
| This helps, but doesn't solve the problem entirely. We have
| some data showing cattle with antibiotic resistant E. coli to
| antibiotics that are _not_ administered to livestock, because
| they 're carried on the same plasmids as other resistance
| genes that confer a fitness advantage in some other way (i.e.
| another antibiotic, heavy metals, etc.)
|
| That's not to say that regulating its use isn't a good idea,
| just that it's _really complicated_.
| analog31 wrote:
| Not to mention mixing antibiotics with livestock feed to
| promote growth.
| TechRemarker wrote:
| Since the the author presumably copied and pasted the original
| title, assuming the remove of the "-" was intentional to make
| more click baity since then reads as if someone is using AI to
| create superbugs that can kill anti biotics rather than of course
| what the actual article said which was an antibiotic that is
| superbug-killing.
| ARandomerDude wrote:
| I spent a second trying to understand why anyone would do this
| (gain of function research?), until I realized the title should
| have retained the BBC's hyphen.
|
| > Superbug killing antibiotics with the help of AI
|
| means "A superbug is killing antibiotics, with the help of AI."
|
| > Superbug-killing antibiotics with the help of AI
|
| means "Antibiotics are killing a superbug, with the help of AI."
| account-5 wrote:
| Same here. Exact same though until I read the article and
| realised it was the opposite of what I thought.
| 0xcafecafe wrote:
| I had a similar brush with ambiguity sometime back when there
| was an article about a bill being tabled. The verb table has
| two opposite meanings per merriam webster:
|
| https://www.merriam-webster.com/dictionary/table#:~:text=tab...
|
| a: to remove (something, such as a parliamentary motion) from
| consideration indefinitely b British : to place on the agenda
| ljlolel wrote:
| [flagged]
| hombre_fatal wrote:
| [flagged]
| yyyk wrote:
| >They took thousands of drugs where the precise chemical
| structure was known, and manually tested them on Acinetobacter
| baumanni... This information was fed into the AI... The AI was
| then unleashed on a list of 6,680 compounds... took the AI an
| hour and a half to produce a shortlist... The researchers tested
| 240 in the laboratory, and found nine potential antibiotics.
|
| They started with manually testing thousands of drugs, in order
| to narrow another similarly sized list by one order of magnitude,
| which was then tested manually. Did they actually save time
| compared to what it would have taken to test the 6,680 list
| manually? I guess this needs to go up by one order of magnitude
| to be really worthwhile?
| adventured wrote:
| It's worthwhile to begin trying to use AI for these purposes.
| The only way we get from here to there, is by using this tech
| in its early forms today, exploring with it, experimenting with
| it. Let's see what it can do, learn, adjust, push it further.
|
| The 10x better version of it from the future doesn't just
| appear out of nowhere. We get there by step.
| joejerryronnie wrote:
| I'm worried about the 10,000,000x version which may emerge
| organically at some point in time.
| tehjoker wrote:
| Maybe it was easier to culture the original bug?
| Thorrez wrote:
| Where does it say they manually tested thousands? It sounds to
| me like they manually tested 240.
| yyyk wrote:
| The first sentence I quoted. They used thousands to train the
| AI, and then ran the AI on a similar sized list. Perhaps with
| reuse it will be worthwhile. Or the second list was harder to
| synthesize?
| Thorrez wrote:
| Oop, I need to read better...
| sp332 wrote:
| If you scroll down past the second photo, right after the
| heading "Artificial intelligence".
| mcdonji wrote:
| I think the effort in the testing of the thousands of drugs was
| to help create the AI model. Then they used that model and it
| seems to have identified a promising antibiotic. The next time
| they go through the process they would not need to train the
| model again right? So get another big list of chemicals and run
| them through the model.
| YeGoblynQueenne wrote:
| Reading the study's abstract, there will not be a "next time"
| because the dataset they created, and the model they trained,
| was specific to Acinetobacter baumannii:
|
| _Here we screened ~7,500 molecules for those that inhibited
| the growth of A. baumannii in vitro. We trained a neural
| network with this growth inhibition dataset and performed in
| silico predictions for structurally new molecules with
| activity against A. baumannii. Through this approach, we
| discovered abaucin, an antibacterial compound with narrow-
| spectrum activity against A. baumannii._
|
| https://www.nature.com/articles/s41589-023-01349-8
|
| Not only were both the dataset they created, and the model
| they trained on it, specific to one organism, the drug they
| discovered also only works on that one organism ("narrow
| spectrum activity against A. baumannii"). If they wanted to
| discover drugs that work on other organisms, like
| Staphylococcus aureus and Pseudomonas aeruginosa that the BBC
| article mentions, they'd have to start all over again.
|
| So, not an approach that looks very practical at this time.
| Maybe in the future, when the sample efficiency and
| generalisation ability of neural nets has significantly
| improved it will be useful in practice.
|
| Study:
|
| https://www.nature.com/articles/s41589-023-01349-8
| geph2021 wrote:
| I think the effort in the testing of the thousands of drugs
| was to help create the AI model.
|
| This gets to the crux of my skepticism around the big claims
| around the pace of AI advancement. At a fundamental level the
| upper limit of AI advancement, in any area, is "the speed of
| information". For some areas, like pharmaceutical/drug
| development, the information comes from the real world,
| human/biological processes (e.g. clinical drug trials), which
| take time. At the extreme, the outcomes of interest could be
| long-term (i.e. years or decades). AI surely advances
| analytically capabilities, but ultimately models can only be
| developed or refined with new data/information, which unfolds
| at a rate that may be independent of computational speeds. AI
| models that are highly predictive and valuable by definition
| necessitates a feedback loop that is tied back to real-world
| outcomes/timescales.
|
| I'm no expert on AI, but I get this sense that the
| exponential improvements that many believe will lead to the
| singularity may in fact reach an inflection point where the
| curve flattens out becomes linear or asymptotic, as the rate
| of improvement is governed by the rate of new information in
| the real world.
| chrisco255 wrote:
| Even for existing information, there remains an enormous
| amount of contextual / cultural / insider knowledge about
| the world that is not documented in any digestible way by
| an AI.
| pixl97 wrote:
| For the moment. Things like gpt-4 are already multimodal,
| but not widely deployed in that fashion. Your data may
| just be a smart Webcam on wheels away from being
| ingested.
| agentofoblivion wrote:
| You hit the nail on the head, and I train transformers for
| a living. This pervasive axiom that intelligence can just
| scale exponentially at a rapid pace is rarely questioned or
| even stated as an assumption. It's far from clear that this
| is possible, and what you've outlined is a plausible
| alternative.
| pixl97 wrote:
| It depends on the scaling nature of the problem being
| researched. If it's one like the '9 months to make a
| baby' issue, then you can't really reduce the minimum
| time. On the other hand if it's studying bacteria with a
| fast breeding rate, then expanding to hundreds of
| thousands of AI Petrie dishes is apt to rapidly
| accelerate the study of the problem.
| ajuc wrote:
| It's possible that no new information is needed, just
| better analysis.
| drabbiticus wrote:
| Science journalism frequently oversimplifies scientific
| investigation. This comment will similarly make
| simplifications, but hopefully overall will provide more
| clarity.
|
| > The AI was then unleashed on a list of 6,680 compounds whose
| effectiveness was unknown. The results - published in Nature
| Chemical Biology - showed it took the AI an hour and a half to
| produce a shortlist.
|
| "published in Nature Chemical Biology" is a link you have to
| click to see the article in fulltext, which I would encourage
| you to read if you really want to understand the study. I would
| link it directly, but there is some site-referrer magic
| happening that allows the BBC article link to cause Nature
| publishing group to show the fulltext.
|
| To better understand what was done:
|
| Training set (manually tested): off-patent drugs (2,341
| molecules) and synthetic chemicals (5,343 molecules). In
| particular the synthetic chemicals are likely to have
| unacceptable side effect profiles. Result of manual screen: 480
| molecules capable of inhibiting Acinetobacter growth by 20%.
| 480/(2341+5343) = 6-7%
|
| Result of AI processing and additional filtering criteria:
| model applied to "Drug Repurposing Hub" dataset consisting of
| 6,680 molecules which they claim have demonstrably favorable
| cytotoxicity profiles and drug-like properties and yielding 3
| sets of 240 drugs each. The three sets:
|
| 1. 240 drugs identified by the model as having >20% probability
| of at least 20% growth inhibition of Acinetobacter and
| structurally _dissimilar_ to those with antibiotic activity in
| the training set. Manually testing for those capable of more
| stringent criteria (80% inhibition of growth) yielded 9 drugs.
|
| 2. 240 drugs with lowest prediction scores: Manual testing
| yields no active drugs, providing some basic validation that
| classifier is functional.
|
| 3. 240 drugs with highest prediction scores (without additional
| filtering criteria based on structural dissimilarity): Manual
| testing yields 40 drugs capable of 80% inhibition. 40/240 =
| 16-17%. Yield enrichment: 16%/6% = 260% or a 2.6x improvement
| compared to naive screen of training set. To be fair, this
| isn't a direct claim of the paper for good reason: the drugs in
| their training set and "validation set/drug repurposing hub"
| are fundamentally different and may have different baseline
| antibiotic activity across the set.
|
| The process of narrowing down these datasets using the model
| could be accomplished in hours (their claim) instead of days
| (my claim). Days is optimistic prediction, requiring high-
| throughput systems and/or staffing in place to run these
| screening assays mostly in parallel instead of serially. Also
| prevents costs associated with further biochemical
| investigation (cultures, chemical synthesis and assays are not
| free).
|
| Most direct value of this work is in accelerating drug
| screening process, reducing cost and developing AI-tractable
| representations of pharmaceutically-relevant chemical features.
| Additionally, proof of concept for identifying drugs with
| appropriate side effect profiles that happen to have antibiotic
| activity but would not have been identified with
| existing/common structural analysis approaches, since they are
| structurally dissimilar to the testing dataset screen. In this
| case they identified a "CCR2- selective chemokine receptor
| antagonist" that had antibiotic properties; some googling
| suggests that this drug class mostly has roles in
| fibrosis/inflammation regulation and may have roles in
| autoimmune disorders and those with significant fibrosis as
| part of the pathology (e.g. cardiovascular disease, liver
| disease, diabetes). You wouldn't expect most drugs in this
| class to have any antibiotic properties and many companies
| would not focus their first efforts on screening such drugs
| with biochemical assays.
| YeGoblynQueenne wrote:
| Why can't you see the full text? Try this link:
|
| https://www.nature.com/articles/s41589-023-01349-8
|
| I copied it from the Nature article, where I got to from the
| BBC link. The Nature page has the full text (I'm not logged
| in). I'm on firefox, is it your browser?
|
| Edit: Oh, wait, I am logged in to Nature. But only on
| firefox. When I switch to chrome and try navigating to the
| Nature article by clicking the BBC link, I get a paywall.
| What exactly do you see on your side?
| bb123 wrote:
| They probably didn't save much time but you must think of the
| positive things mentioning the AI buzz word will have done for
| their funding.
| 0gravitas wrote:
| And, of course, further research is needed...
| throwuwu wrote:
| Now that they have a trained model they can continue to apply
| it to new chemicals. It could even be used on chemicals that
| have not been synthesized yet to check if they are useful and
| worth the effort and money to synthesize.
| kurthr wrote:
| Hopefully, soon we can start treating all the cattle in the US
| with low doses of this drug, and dumping failed manufacturing
| batches in the Ganges river.
|
| Think of the poor superbugs!
| tohnjitor wrote:
| Hopefully you are personally unable to implement such plans
| because antibiotics are not without their side effects. It
| took twelve years between Levaquin becoming FDA approved in
| 1996 and the addition of a black box warning label in 2008
| due to risk of Achilles tendon rupture.
|
| https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2483892/
| andrewnicolalde wrote:
| I suspect they were being sarcastic.
| nestorD wrote:
| My guess is that they tested thousands of _existing_ drugs
| (cheap to produce and easily available) in order to build a
| short list of _new_ compound (significantly more expensive to
| produce).
| bazmattaz wrote:
| This is incredibly exciting stuff and all I keep thinking of is
| why the hell aren't governments pouring money into research like
| this?
|
| ML models like this need training examples and The article talks
| about manual testing thousands of compounds to gather enough
| training data. This is where lots of money could have a huge
| benefit. The more high quality data a model has the better it's
| accuracy
| droopyEyelids wrote:
| The way governments pour money into this is by funding grants
| at research universities.
|
| This came out of McMaster, though, and I don't know if the
| Canadian government does funding the same way as we do in the
| USA.
|
| Still it raises an ugly question, why can discoveries funded by
| the citizens of a country be turned into the patented property
| of private companies.
| Fomite wrote:
| Canadian and U.S. research funding work _very_ differently
| Fomite wrote:
| What makes you think they aren't?
|
| I can name four or five labs _on my floor_ that are currently
| working on various aspects of antimicrobial resistance, all of
| which are government funded.
| greenhearth wrote:
| How long before AI recombines DNA for creature creation?
| sklargh wrote:
| This is a heartening affirmative use. The negative of this image,
| creating biological warfare agents with extraordinary virulence
| and/or infectiousness is terrifying. Not really sure who would or
| how to begin reducing those risks.
| m3kw9 wrote:
| And equally use AI to neutralize these new diseases, if there
| are scientists left
| RobinL wrote:
| If anyone's wondering in what way AI/ML was used, here's the
| relevant part of the article:
|
| >>> We first screened a diverse collection of 7,684 small
| molecules at 50uM for those that inhibited the growth of A.
| baumannii ATCC 17978 in Lysogeny Broth (LB) medium (Fig. 1b and
| Extended Data Fig. 1a). This chemical collection consisted of
| both off-patent drugs (2,341 mol-ecules) and synthetic chemicals
| (5,343 molecules) curated from various high-throughput screening
| sub-libraries at the Broad Institute. Using a conventional hit
| cutoff of one standard deviation below the mean growth of the
| entire dataset resulted in 480 molecules being defined as
| 'active' and 7,204 being defined as 'inactive' (Supplementary
| Data 1).Next, this dataset was used to train a binary classifier
| to predict whether structurally new molecules may display
| activity against A. baumannii. Briefly, we leveraged a directed
| message-passing neural network architecture, which translates the
| graph structure of a mol-ecule into a continuous vector18 (Fig.
| 1a).This type of model operates by iteratively exchanging
| informa-tion of local chemistry between adjacent atoms and bonds
| in a series of 'message-passing' steps. Each iteration of message
| passing propa-gates information about local chemistry across the
| molecule, thereby allowing the model to build a more holistic
| representation of the mol-ecule. After a defined number of
| message-passing steps, the vector representations of various
| local chemical regions of a molecule are summed into a single
| continuous vector that captures the complexity of the entire
| compound. This learned final vector is then supplemented with
| fixed molecular features computed using RDKit19. A final vector
| containing both learned and computed features is then used as an
| input vector for a feed-forward neural network that predicts
| antibacterial properties. The model was further optimized by
| using an ensemble of ten classifiers, increasing its robustness.
| Our final model achieved an area under the precision-recall curve
| of 0.337+-0.088 and an area under the receiver-operating
| characteristic curve of 0.792+-0.042, providing confidence in
| leveraging the model for predictions in new chemical spaces.
| tedunangst wrote:
| > small molecules at 50uM
|
| That's an unusual unit.
| twic wrote:
| My question is how this compares to other methods for drug
| design. They are not the first people to use a computer to pick
| molecules out of a library to test as antibiotics. Is their
| method much better, a bit better, no better, etc? That doesn't
| seem to be mentioned in the discussion.
| arbuge wrote:
| 1. That is of course awesome.
|
| 2. I read about this and see Nvidia up in the news by another 20%
| today and it makes me think. The old economy "value stocks" in
| comparison, which are popularly considered to have no exposure to
| AI, are being dumped by the wayside. But AI advances will benefit
| those same old economy stocks in many cases. They still possess
| the necessary distribution to bring them them to market
| effectively. It's traditional drug companies in this particular
| example that are best positioned to capitalize on this.
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