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points by enraged_camel 2 days ago | 0 comments
Specifically, it's because the verification loops are much longer in applied sciences. In biology you need to wait for wet lab experiments, clinical trials, regulatory steps, etc. before you can call something "solved".

Since sequential steps are slow, I think we're going to see massive increases in parallelization. I'm not sure how the regulatory frameworks will keep up honestly.

[–] slashdave 2 days ago
> we're going to see massive increases in parallelization

Why? This is not an AI problem, it's an industry one. And if it was simple, it would already exist.

[–] xp84 2 days ago
Well, it matters more for AI because there are some limitations of human research that don't apply to AI. Not all of course, just mainly the fact that there's a finite number of great researchers. Other things are limitations that could theoretically be overcome -- for instance, it's "only" money stopping you from having more lab space as long as you have a suitable robot to run a lab.
[–] slashdave 2 days ago
> as long as you have a suitable robot to run a lab

Let me guess. You have never stepped in a lab.

[–] menaerus 2 days ago
> And if it was simple, it would already exist.

Not necessarily. Many things are simply gatekept behind the door.

[–] slashdave 2 days ago
Do you have any idea of the amount of money involved in pharmaceutical R&D?
[–] menaerus 2 days ago
Yes, I do. My comment was a general sentiment saying that sole inexistence of something is not a proof that that something has to be very complex. Pharma is no different when it comes to people relationships and/or fear of somebody else stealing your chunk of expertise and/or fear of some other company taking a significant part of your revenue. Gatekeeping is one form of "preventing" or slowing down the process, and we see it in various domains regardless of their complexity.

We see it happening in SWE as we speak - people from and outside the tech who had no or very little experience with programming are now starting to deliver their ideas into products without devs or very few of them. We also see this within the SWE where strong domain experts are now able to deliver ideas into code easier than ever, and by themselves, and for which they would previously need a handful of programming experts. We also see it in other way too, strong programming experts are now able to pick up other domain easier than ever.

This process is something that I believe it will (and hope so) inevitably be picked up by the pharma too so the myth about the complexity will become broken.

[–] slashdave 10 hours ago
No myth.

Software is utterly trivial compared to the life sciences. The reason is quite simple: reproducibility and explainability.

[–] hibikir 2 days ago
We already have as much parallelization as companies can afford: Hundreds, if not thousands of new corn varieties, coming from one lab. The budget is the limit. Testing is just expensive.

Process improvements come from failing faster, so you spend less money on your test. Finding the actual answer without testing would also be wonderful, but LLMs might be fast, but they aren't magic. Something like Alphafold saves time in some steps, but it's not just "hand me a candidate", but find completely novel verification mechanisms, which would be just as useful for humans. Either way, parallelization is unlikely to increase.

[–] tarsierscripter 2 days ago
Amdahl's Law [1] is not your friend here. Even a thousand women can't produce a baby in one month. Trying a million things in parallel doesn't help much when the possibilities involve cosmic-level numbers and you need to iterate on past results to make progress.

[1] https://en.wikipedia.org/wiki/Amdahl%27s_law

[–] Bluestein 2 days ago
> wet lab experiments, clinical trials, regulatory steps

I wonder what the unlock would be, if any, of having simulations substitute any (or all, in time) of these steps. Akin to the method used to train FSD cars, or robots.-

[–] xp84 2 days ago
In my untrained opinion, I don't think that will be as much of a cheat as it might seem. Or more accurately, creating a simulation accurate enough to where it would work for this purpose would be where all the breakthroughs would have to be made. Building a perfect simulation requires an understanding of the system being simulated, that I don't think we fully have of molecular biology -- if we did, I suspect we'd already have many of the answers we are seeking when we do this kind of research.

But I'm no biologist so if someone else is, feel free to correct me.

[–] vessenes 2 days ago
You may recall Google published AlphaFold in the last few years -- its main goal is precisely this. Combining up that tech with these pipelines is likely to speed things up. It looks like things are already speeding up a lot based on the press release, though. This seems likely to be an area where we'll see some compounding returns over the next couple years.
[–] dekhn 1 day ago
AlphaFold's goals were twofold (heh): demonstrate that protein structure prediction (for a subset of proteins) was solvable, and to accelerate drug discovery. As most people in the pharma field already know, PSP was never the gating step in getting drugs approved.
[–] Bluestein 2 days ago
Indeed, I do recall, now that you mention it.-
[–] jurgenburgen 2 days ago
By the time we can simulate a human body to the degree that clinical trials aren’t necessary I think we have solved disease a long time ago.
[–] neuronexmachina 2 days ago
"All models are wrong, but some are useful."
[–] Bluestein 2 days ago
All valid points on being able to simulate well enough meaning we would have nearly solved everything to begin with.-

I wonder if, at some point, AI's will perform to such degree they are able to construct "black box" simulations as needed, that nobody understands BUT that are sufficient to empirically experiment on ...

... much in the same way LLM's function now: In an aggregate sense, they are bloack boxes.-