[avogt27]

It's really a bummer to see this marketed as 'AI Discovers Something New'. The authors in the actual paper carried out an enormous amount of work, the vast majority of which is relatively standard biochemistry and cell biology - nothing to do with computational techniques. The AlphaFold3 analysis (the AI contribution) literally accounts for a few panels in a supplementary figure - it didn't even help guide their choice of small molecule inhibitors since those were already known. AlphaFold (among other related tools) is absolutely a game changer in structural biology and biophysics, but this is a pretty severe case of AI hype overshadowing the real value of the work.

[bilekas]

Yeah it’s a really strange title for the actual work, it’s like saying bic pens helped find x y z simply because they used them to take notes.

[tim333]

>“It really demanded modern AI to formulate the three-dimensional structure very precisely to make this discovery.”

It's not like bic pens. It's a new technique they couldn't do before that helped crack the mystery.

Also the title is "AI Helps..." not "AI Discovers" so that's kind of a strawman. I don't think anyone is denying the humans did great work. Maybe it's more like Joe Boggs uses the Hubble telescope to find a new galaxy and moaning because the telescope gets a mention.

I'm quite enthusiastic about the AI bit. My grandad died with alzheimer's 50 years ago. My sister is due to die of als in a couple of years. Both areas have been kind of stuck for decades. I'm hoping the AI modeling allows some breakthroughs.

[avogt27]

I think my problem is that this is maybe the most minimal and mundane use of AlphaFold, but it is treated like one of the main points of the paper. The small molecules they tested were already known to inhibit this enzyme, the structural modeling done based on AlphaFold is a minute part of the story compared to the dozens of incredibly difficult experiments they did - it almost seems the sort of thing one of the reviewers suggested during the initial submission and was added after the first round of edits.

I can't tell you how many times I've sat through talks where someone (usually ill-equipped to really engage with the research) suggests that the speaker tries AlphaFold for this or that without a clear understanding of what sort of biological insight they're expecting. It's also a joke at this point how often grad students plug their protein into AlphaFold and spend several minutes giving a half-baked analysis of the result. There are absolutely places where structure prediction is revolutionizing things including drug discovery, but can we acknowledge the hype when we see it?

I'm very sorry for your loss, my aunt is also declining due to this disease. I think statistically everyone either goes through it or becomes a caretaker if they live long enough.

[WhitneyLand]

>statistically everyone either goes through it or becomes a caretaker

May be what happens but it’s not required or what people want. A massive amount of those diagnosed, more than half, would prefer a compassionate end to their life at the right time. Less than 2% are able to end up taking this option.

[bilekas]

> Maybe it's more like Joe Boggs uses the Hubble telescope to find a new galaxy and moaning because the telescope gets a mention.

Maybe I've underestimated the impact the AI tooling has had then, because seems to me that your example wouldn't be an issue as it's literally the entire tool to discover.

> I'm hoping the AI modeling allows some breakthroughs.

I'm actually on board with you on this, I think it can be extrememly useful and really speed things up when dealing with such huge amount of complex data that needs to be worked with, my only gripe here was the title itself. It's seems forced when it could have been "Amazing breakthrough discovered to unravel cause of Alzheimer’s" - From here the main body of the article would match the title, with a nice shout out to a really creative use of AI.

[taneq]

Maybe more like “Excavator helps archaeologists discover new species”?

[bonoboTP]

Sure, when the excavator was new and could help do larger scale archeological excavations that were never possible before, then why not title it like that?

[tim333]

I was thinking maybe like Van Leeuwenhoek uses glass gizmo to discover first microorganism. In that AI molecular simulation is a new tech which will probably get better and help many discoveries.

[skeeter2020]

OK, then "New mega-sized excavator allows archeologist to process more material than before"?

[gosub100]

Computer-assisted genomics leads scientists to discover...

[thesz]

  > It's a new technique they couldn't do before that helped crack the mystery.

What about SAT-based solvers [1] for same problem?

[1] https://ieeexplore.ieee.org/document/5361301

Would that technique do the same? If not, why?

[aantix]

>I don't think anyone is denying the humans did great work

The title cites the AI contribution, not the human

[dumb1224]

The paper has a much clearer title 'Transcriptional regulation by PHGDH drives amyloid pathology in Alzheimer’s disease'. This is from the univ press media page, it's very common to use over-hyped titles to draw views.

[mk89]

With all the money cutting happening, I am not surprised they are joining the bandwagon to get some investors...

I just read some days ago here on HN an interesting link which shows that more than 70% of VC funding goes straight to "AI" related products.

This thing is affecting all of us one way or another...

[trott]

> The AlphaFold3 analysis (the AI contribution) literally accounts for a few panels in a supplementary figure - it didn't even help guide their choice of small molecule inhibitors since those were already known.

(Disclaimer: I'm the author of a competing approach)

Searching for new small-molecule inhibitors requires going through millions of novel compounds. But AlphaFold3 was evaluated on a dataset that tends to be repetitive: https://olegtrott.substack.com/p/are-alphafolds-new-results-...

[YeGoblynQueenne]

What is the upshot of that?

[api]

Historically it's "superstar researcher discovers something new" where the superstar researcher actually relies on the research of hordes of grad students and postdocs.

[sublimefire]

Yes I do agree that much of the work was done using conventional methods and quite little was done with AI. AI model did do the folding though which was IMO critical to understand the structure and see the secondary substructure.

The title is clickbaity, it would be useful to stress that AI solves a very specific problem here that is extremely hard to do otherwise. It is like a lego piece.

[avogt27]

Several crystal structures of the catalytic domain of the protein had already been determined. The DNA binding domain of the protein which AlphaFold predicted is a relatively common fold that probably could have been figured out using homology modeling, which was common 10+ years ago. Even the small molecule docking used pretty old school computational techniques, and all but one drug interacted with the predetermined structures. The analysis was indeed aided by AI in the form of AlphaFold, but my guess is it sped a couple things up rather than making them possible.

[nonameiguess]

It's helpful when reading these kinds of things to realize what you're reading. This isn't research. It's a press release. The author lists himself as a "Public Information Officer" for UC San Diego. Looking back through his article archives, it appears most, if not all, of the press releases make heavy emphasis of technology used by the research rather than anything about the research itself.

Go the current very last page and he's hyping up nanotech in 2015, which as far as I'm aware, didn't end up panning out or really going anywhere. https://today.ucsd.edu/archives/author/Liezel_Labios/P260

[rs186]

Was going to say about the same thing. I have some background in biomedical research a while ago, and I could tell that on the high level the main body of the work here is similar to the methodology used in tons of research that were already done many years ago. People have already been using various machine learning/deep learning methods for a long time, and this is definitely not something significant that the headline tries to make or how people are perceiving it. Not to discount their work, but really, not too much to see for the average reader on the Internet.

In other words, this is something that happens in the field all the time, most of which don't get any attention from people outside the field, were it not because of the "AI" buzzword in the article.

[discodonkey]

I think the authors of this article probably sought to highlight the fact that AI is now being used in medical research, rather than credit it with all the work (see "helps unravel" as opposed to "unravels").

[HWR_14]

The authors of this article probably sought to have their names and phrases like "AI powered research" published together.

[Majora320]

ML/"AI" has been used in medical research for years and years, the buzzword headlines are a recent phenomenon.

[AdventureMouse]

I agree but it says something about the level of interest and confidence people have in the current state of Alzheimer’s research.

How many people would have read the article if it didn’t mention AI?

[skeeter2020]

I have multiple comments here and didn't read the article regardless!

[mbgerring]

Press releases like this are published for the purposes of securing funding. Medical research departments at universities are currently under siege by the federal government. Emphasizing the use of AI is a great way to avoid Elon Musk's search, replace and destroy operation for research funding.

[avogt27]

I agree that this is probably at least partially a motivation, but it seems like a losing strategy to me. AlphaFold is run by a private company, and falsely elevating the importance of its use in the paper could be used to fuel the argument that all this research needs to be privatized. Given the current situation, I hope people realize that the breakthrough in structure prediction is literally impossible without 70+ years data generated by publicly funded research. Most of the foundational work in deep learning guided structure prediction was also publicly funded, with Deep Mind getting in at the tail end of the race once it seemed like the problem could be brute forced by throwing enough resources at it.

[yieldcrv]

At the end of the paper it says

> *These authors contributed equally

so your position is satisfied by listing an AI amongst those authors

[0x70run]

Wish I could upvote this more!

[frizlab]

When I read the title of the article in my RSS feed my first instinct was to go straight to here with a snarky “How was it not actually AI that did this?” in my head…

As usual I was not disappointed.

[SwtCyber]

Honestly, the fact that the core discovery still relied so heavily on classic biochemistry and experimental validation actually makes it even more impressive to me

[jamesrcole]

[EDIT: people downvoting this, how about you explain what you object to in it]

> It's really a bummer to see this marketed as 'AI Discovers Something New'.

The headline doesn't suggest that. It's "AI Helps Unravel", and that seems a fair and accurate claim.

And that's true for the body of the article, too.

[theptip]

It’s “AI helps unravel”, not “AI discovers”. And it’s newsworthy, as AI-assisted discoveries are not yet boringly well-known.

I think it’s cool to see, and a good counterpoint to the “AI can’t do anything except generate slop” negativity that seems surprisingly common round here.

[bGl2YW5j]

Thanks for highlighting this

[amelius]

> The authors in the actual paper carried out an enormous amount of work, the vast majority of which is relatively standard biochemistry and cell biology - nothing to do with computational techniques.

OK but if the AI did all the non-standard work, then that's even more impressive, no?