danielmarkbruce

Yup, and it should. There is a place for it in drug discovery, but it's likely an Amdahl's law type thing.

Maybe read the comment i responded to.

The US is founded on the idea of individual freedom, and that one of the few areas of "collective" spending should be defense.

This is 101. If you don't like it that's fine, but not understanding it can be resolved with a simple google search.

Phase 1 isn't the big problem. It's phase 2 and phase 3 where drugs fail hard and the trials cost the most and take the longest.

Honestly once you go through all the details... drug discovery feels like the one of the overhyped and unlikely to work areas for AI. Other areas of medicine (like diagnosis) are already seeing pretty good results, and anecdotally seems to work well and tends to fit within the realm of text analysis where most of the AI gains have happened.

Talk to chatgpt about how a checking account works, and really understand what happens when you put some money in the bank, and when you transfer that money to another person at another bank, how it's accounted for, how the banks actually do that transfer.

That's 80% of the thing right there.

Alphafold solved a waayyy narrower problem that had lots of experimental structural data. Guessing how a drug interacts with an entire human is a whole different scale of thing. Even knowing protein interactions wouldn't tell you how those interactions propagate through an entire human system or whether a drug will be safe and/or effective.

Drug trials in humans.

Who said anything about easy and fun?

It's quite a leap to go from the study you linked to thinking AI will create models that will be useful for new trials. The big leaps in AI have required enormous amounts of data. There is no such analogous data sets in biology and no foreseeable way to get it.

It really is hard to see AI improving the trial process meaningfully. We are more likely to see progress with better trial design and regulatory regimes.

It sounds lazy tbh. No one said privatize. It can be open source can't it?

This is a largely agreed upon problem. What isn't clear is why it seems to get sidestepped in discussion so frequently. No one knows how a molecule will interact with human biology. It can't be simulated computationally to generate data (too slow), there isn't a lot of data in the grand scheme of things, so you can't train a model to actually predict the interaction of a molecule with a human.

And nothing I've said here is an original thought or piece of insight.

why not just do it?

Under examined? No. This is a very common talking point.

A glut of models doesn't mean there is no moat. There are several dimensions on which to compete, and it might turn out that being able to produce models that optimize on different dimensions is a difficult and valuable thing to do. It also might not, but the "there is obviously no moat" only comes from people who don't use these things systematically.

Lol, it's a bad deal huh. Tell that to the guys who bought the IPO of Facebook, Google, Amazon, MSFT, Nvidia.

Just because something might be not great in aggregate, it doesn't mean any single IPO is a bad idea. Some people have insight into the future of a specific industry/business. If you can pick one, good for you.

People said similar things about facebook, and similar things about Nvidia stock recently (not ipo, but similar "why buy at this price"). Global GDP is over $100 trillion every year, there is a lot of business to do in the world, AI might be able to do a lot, it might be very profitable.

The number might be too high, but doesn't take more than a little imagination and some basic facts about the world to see how it could be worth 20 trillion in 10 years.