[shpongled]

That a UPR inhibitor would inhibit viability of AML cell lines is not exactly a novel scientific hypothesis. They took a previously published inhibitor known to be active in other cell lines and tried it in a new one. It's a cool, undergrad-level experiment. I would be impressed if a sophomore in high school proposed it, but not a sophomore in college.

[CaptainOfCoit]

> I would be impressed if a sophomore in high school proposed it

That sounds good enough for a start, considering you can massively parallelize the AI co-scientist workflow, compared to the timescale and physical scale it would take to do the same thing with human high school sophomores.

And every now and then, you get something exciting and really beneficial coming from even inexperienced people, so if you can increase the frequency of that, that sounds good too.

[shpongled]

We don't need an army of high school sophomores, unless they are in the lab pipetting. The expensive part of drug discovery is not the ideation phase, it is the time and labor spent running experiments and synthesizing analogues.

[falcor84]

As discussed elsewhere, Deepmind are also working on extending Alphafold to simulate biochemical pathways and then looking to tackle whole-cell simulation. It's not quite pipetting, but this sort of AI scientist would likely be paired with the simulation environment (essentially as function calling), to allow for very rapid iteration of in-silico research.

[Teever]

It sounds like you're suggesting that we need machines that mass produce things like automated pipetting machines and the robots that glue those sorts of machines together.

[11101010001100]

This exists, but does not require AI, so there is no hype.

[dekhn]

Replacing a skilled technician is remarkably challenging. Often times, when you automate this, you just end up wasting a ton of resources rather than accelerating discovery. Often, simply integrating devices from several vendors (or even one vendor) takes months.

[shpongled]

They already exist, and we use them. They are not cheap though!

[Teever]

Any idea why they're they so expensive?

[dekhn]

I've built microscopes intended to be installed inside workcells similar to what companies like Transcriptic built (https://www.transcriptic.com/). So my scope could be automated by the workcell automation components (robot arms, motors, conveyors, etc).

When I demo'd my scope (which is similar to a 3d printer, using low-cost steppers and other hobbyist-grade components) the CEO gave me feedback which was very educational. They couldn't build a system that used my style of components because a failure due to a component would bring the whole system down and require an expensive service call (along with expensive downtime for the user). Instead, their mech engineer would select extremely high quality components that had a very low probability of failure to minimize service calls and other expensive outages.

Unfortunately, the cost curve for reliability not pretty, to reduce mechanical failures to close to zero costs close to infinity dollars.

One of the reasons Google's book scanning was so scalable was their choice to build fairly simple, cheap, easy to maintain machines, and then build a lot of them, and train the scanning individuals to work with those machines quirks. Just like their clusters, they tolerate a much higher failure rate and build all sorts of engineering solutions where other groups would just buy 1 expensive device with a service contract.

[shpongled]

There is a big range in both automation capabilities and prices.

We have a couple automation systems that are semi-custom - the robot can handle operation of highly specific, non-standard instruments that 99.9% of labs aren't running. Systems have to handle very accurate pipetting of small volumes (microliters), moving plates to different stations, heating, shaking, tracking barcodes, dispensing and racking fresh pipette tips, etc. Different protocols/experiments and workflows can require vastly different setups.

See something like:

[1] https://www.hamiltoncompany.com/automated-liquid-handling/pl...

[2] https://www.revvity.com/product/fontus-lh-standard-8-96-ruo-...

[sizzle]

Also clinical trials

[petra]

So pharmaceutical research is largely an engineering problem, of running experiments and synthesizing molecules as fast, cheap and accurate as possible ?

[shpongled]

I wouldn't say it's an engineering problem. Biology and pharmacology are very complex with lots of curveballs, and each experiment is often different and not done enough to warrant full engineering-scale optimization (although this is sometimes the case!).

[kridsdale1]

It also seems to be a financial problem of getting VC funds to run trials to appease regulators. Even if you’ve already seen results in a lab or other country.

[ClumsyPilot]

We could have an alternative system where VC don’t need to appease regulators but must place X billion in escrow for compensation of any harm the medicine does to customers.

Regulator is not only there to protect the public, it also protects VC from responsibility

[forgetfreeman]

> VC don’t need to appease regulators

Regulations around clinical trials represent the floor of what's ethically permissible, not the ceiling. As in, these guidelines represent the absolute bare minimum required when performing drug trials to prevent gross ethical violations. Not sure what corners you think are ripe for cutting there.

[melagonster]

This is why FDA requires experiments before company sells any drugs.

[devmor]

This is the general problem with nearly all of this era of generative AI and why the public dislike it so much.

It is trained on human prose; human prose is primarily a representation of ideas; it synthesizes ideas.

There are very few uses for a machine to create ideas. We have a wealth of ideas and people enjoy coming up with ideas. It’s a solution built for a problem that does not exist.

[klipt]

Only two years since chatGPT was released and AI at the level of "impressive high school sophomore" is already blasé.

[Nevermark]

Especially when you consider the artificial impressive high school sophomore is capable of having impressive high school sophomore ideas across and between an incredibly broad spectrum of domains.

And that their generation of impressive high school sophomore ideas is faster, more reliable, communicated better, and can continue 24/7 (given matching collaboration), relative to their bio high school sophomore counterparts.

I don’t believe any natural high school sophomore as impressive on those terms, has ever existed. Not close.

We humans (I include myself) are awful at judging things or people accurately (in even a loose sense) across more than one or two dimensions.

This is especially true when the mix of ability across several dimensions is novel.

(I also think people under estimate the degree that we, as users and “commanders” of AI, bottleneck their potential. I don’t suggest they are ready to operate without us. But that our relative lack of energy, persistence & focus all limit what we get from them in those dimensions, hiding significant value.

We famously do this with each other, so not surprising. But worth keeping in mind when judging limits: whose limits are we really seeing.)

[howenterprisey]

I don't need high school level ideas, though. If people do, that's good for them, but I haven't met any. And if the quality of the ideas is going to improve in future years, that's good too, but also not demonstrated here.

[the8472]

> And if the quality of the ideas is going to improve in future years, that's good too, but also not demonstrated here.

I don't quite understand the argument here. The future hasn't happened yet. What does it mean to demonstrate the future developments now?

[Nevermark]

I am going to argue that you do. Then I will be interested in your response, if you feel inclined.

We all have our idiosyncratically distributed areas of high intuition, expertise and fluency.

None of us need apprentice level help there, except to delegate something routine.

Lower quality ideas there would just gum things up.

And then we all have vast areas of increasingly lesser familiarity.

I find, that the more we grow our strong areas, the more those areas benefit with as efficient contact as possible with as many more other areas as possible. In both trivial and deeper ways.

The better developer I am, in terms of development skill, tool span, novel problem recognition and solution vision, the more often and valuable I find quick AI tutelage on other topics, trivial or non-trivial.

If you know a bright high school student highly familiar with a domain that you are not, but have reason to think that area might be helpful, don’t you think instant access to talk things over with that high schooler would be valuable?

Instant non-trivial answers, perspective and suggestions? With your context and motivations taken into account?

Multiplied by a million bright high school students over a million domains.

—

We can project the capability vector of these models onto one dimension, like “school level idea quality”. But lower dimension projections are literally shadows of the whole.

It if we use them in the direction of their total ability vector (and given they can iterate, it is actually a compounding eigenvector!) and their value goes way beyond “a human high schooler with ideas”.

It does take time to get the most out of a differently calibrated tool.

[rtkwe]

Suggesting "maybe try this known inhibitor in other cell lines" isn't exactly novel information though. It'd be more impressive and useful if it hadn't had any published information about working as a cancer inhibitor before. People are blasé about it because it's not really beating the allegations that it's just a very fancy parrot when the highlight of it's achievements is to say try this known inhibitor with these other cell lines, decent odds that the future work sections of papers on the drug already suggested trying on other lines too...

[baq]

A couple years ago even suggesting that a computer could propose anything at all was sci-fi. Today a computer read the whole internet, suggested a place to look at and experiments to perform and… ‘not impressive enough’. Oof.

[ClumsyPilot]

> Today a computer read the whole internet, suggested a place to look at

Or imagine this one - computer maps the whole world, suggests a route how to get to any destination?!

You just described a basic search engine.

LLM is kind of a search engine for language

[OvidNaso]

But so are you and I. That's how you wrote this reply.

[ClumsyPilot]

Preposterous- cavemen had no language but they could reason, think and learn.

A child learns how to eat solid food and how to walk. That a square peg fits into a square hole. This has nothing to do with language.

people who deaf and mute and cannot read can still reason and solve problems.

[Workaccount2]

People are facing existential dread that the knowledge they worked years for is possibly about to become worth a $20 monthly subscription. People will downplay it for years no matter what.

[forgetfreeman]

You just described a library card.

[Der_Einzige]

whoosh

[directevolve]

Most people here know little to nothing of biomedical research. Explaining clearly why this isn’t a scientifically interesting result is helpful.

[thomastjeffery]

Sure, but is it more impressive than books?

[hinkley]

I have a less generous recollection of the wisdom of sophomores.

[kuhewa]

I'm sure the scientists involved had a wish list of dozens of drug candidates to repurpose to test based on various hypotheses. Ideas are cheap, time is not.

In this case they actually tested a drug probably because Google is paying for them to test whatever the AI came up with.

[elicksaur]

I’m not familiar with the subject matter, but given your description, I wouldn’t really be impressed by anyone suggesting it. It just sounds like a very plausible “What if” alternative.

On the level of suggesting suitable alternative ingredients in fruit salad.

We should really stop insulting the intelligence of people to sell AI.

[tomrod]

Incremental progress is incremental progress.

[0] https://matt.might.net/articles/phd-school-in-pictures/

[dekhn]

(to be Shpongled is to be kippered, mashed, smashed, destroyed...completely geschtonkenflopped)