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Accelerating scientific breakthroughs with an AI co-scientist

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Re: Accelerating scientific breakthroughs with an AI co-scientist

#141

Post by webmaven »

colingauvin wrote: post #133
warkdarrior wrote: post #127

Earlier quoted context omitted.

> It's generally very easy to marginally move the needle in drug discovery. It's very hard to move the needle enough to justify the cost. Maybe this kind of AI-based exploration would lower the costs. The more something is automated, the cheaper it should be to test many concepts in parallel.

A med chemist can sit down with a known drug, and generate 50 analogs in LiveDesign in an afternoon. One of those analogs may have less CYP inhibition, or better blood brain barrier penetration, or slightly higher potency or something. Or maybe they use an enumeration method and generate 50k analogs in one afternoon. But no one is going to bring it to market because it costs millions and millions to synthesize, get t…

But doesn't that mean that ranking the ideas to find the ones most worth testing is a useful problem to solve?

Re: Accelerating scientific breakthroughs with an AI co-scientist

#142

Post by warbaker »

dekhn wrote: post #137
warbaker wrote: post #129

Earlier quoted context omitted.

The article you linked is not an example of this happening. Google open-sourced the chip design method, and uses it in production for TPU and other chips. https://github.com/google-research/circuit_training https://deepmind.google/discover/blog/how-alphachip-transfor...

It's an ongoing debacle with multiple people making extremely good arguments that Google overstated the results. Yes, I know it's in TPUs and I said exactly that. You simply can't take Google press at face value.

Google has responded to this "controversy" already:

https://x.com/JeffDean/status/1858540085794451906

https://arxiv.org/abs/2411.10053

Re: Accelerating scientific breakthroughs with an AI co-scientist

#143

Post by melagonster »

ClumsyPilot wrote: post #122
kridsdale1 wrote: post #86

Earlier quoted context omitted.

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.

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

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

Re: Accelerating scientific breakthroughs with an AI co-scientist

#144

Post by dekhn »

warbaker wrote: post #142
dekhn wrote: post #137

Earlier quoted context omitted.

It's an ongoing debacle with multiple people making extremely good arguments that Google overstated the results. Yes, I know it's in TPUs and I said exactly that. You simply can't take Google press at face value.

Google has responded to this "controversy" already: https://x.com/JeffDean/status/1858540085794451906 https://arxiv.org/abs/2411.10053

Yes, I am aware. I didn't find Jeff's argument particularly convincing. Please note: I've worked personally with Jeff before and shared many a coffee with him. He's done great work and messed up a lot of things, too.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#145

Post by tomrod »

shpongled wrote: post #34
crypto420 wrote: post #11

I'm not sure if people here even read the entirety of the article. From the article: > We applied the AI co-scientist to assist with the prediction of drug repurposing opportunities and, with our partners, validated predictions through computational biology, expert clinician feedback, and in vitro experiments. > Notably, the AI co-scientist proposed novel repurposing candidates for acute myeloid leukemia (AML). Subse…

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.

Incremental progress is incremental progress.

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

Re: Accelerating scientific breakthroughs with an AI co-scientist

#146

Post by nazgul17 »

ttpphd wrote: post #31
celltalk wrote: post #20

“Drug repurposing for AML” lol As a person who is literally doing his PhD on AML by implementing molecular subtyping, and ex-vivo drug predictions. I find this super random. I would truly suggest our pipeline instead of random drug repurposing :) https://celvox.co/solutions/seAMLess edit: Btw we’re looking for ways to fund/commercialize our pipeline. You could contact us through the site if you’re interested!

It's almost like scientists are doing something more than a random search over language.

This search is random in the same way that AlphaGo's move selection was random.

In the Monte Carlo Tree Search part, the outcome distribution on leaves is informed by a neural network trained on data instead of a so-called playout. Sure, part of the algorithm does invoke a random() function, but by no means the result is akin to the flip of a coin.

There is indeed randomness in the process, but making it sound like a random walk is doing a disservice to nuance.

I feel many people are too ready to dismiss the results of LLMs as "random", and I'm afraid there is some element of seeing what one wants to see (i.e. believing LLMs are toys, because if they are not, we will lose our jobs).

Re: Accelerating scientific breakthroughs with an AI co-scientist

#147

Post by mupuff1234 »

dekhn wrote: post #137
warbaker wrote: post #129

Earlier quoted context omitted.

The article you linked is not an example of this happening. Google open-sourced the chip design method, and uses it in production for TPU and other chips. https://github.com/google-research/circuit_training https://deepmind.google/discover/blog/how-alphachip-transfor...

It's an ongoing debacle with multiple people making extremely good arguments that Google overstated the results. Yes, I know it's in TPUs and I said exactly that. You simply can't take Google press at face value.

> You simply can't take Google press at face value.

I think that's true for virtually every company and also for most people (in the context of published work)

Re: Accelerating scientific breakthroughs with an AI co-scientist

#148

Post by colingauvin »

webmaven wrote: post #141
colingauvin wrote: post #133

Earlier quoted context omitted.

A med chemist can sit down with a known drug, and generate 50 analogs in LiveDesign in an afternoon. One of those analogs may have less CYP inhibition, or better blood brain barrier penetration, or slightly higher potency or something. Or maybe they use an enumeration method and generate 50k analogs in one afternoon. But no one is going to bring it to market because it costs millions and millions to synthesize, get t…

But doesn't that mean that ranking the ideas to find the ones most worth testing is a useful problem to solve?

If you had a crystal ball that could predict the properties of the molecule, perhaps.

Re: Accelerating scientific breakthroughs with an AI co-scientist

#150

Post by funnyAI »

azinman2 wrote: post #13

It seems in general we’re heading toward’s Minsky’s society of minds concept. I know OpenAI is wanting to collapse all their models into a single omni model that can do it all, but I wonder if under the hood it’d just be about routing. It’d make sense to me for agents to specialize in certain tool calls, ways of thinking, etc that as a conceptual framework/scaffolding provides a useful direction.

"conceptual framework" can actually be another generalist model. Splitting model also comes with some advantages. Like easy separate tuning and replacements. Easy scaling by simply duplicating heavily used model on new hardware.
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