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Comment by djierardi | Hacker News Reader

Yeah. What I think is deceptive is something like solving Navier-Stokes with $10M of compute. The bigger question is how many conjectures and hypotheses they tried, and what fraction of those actually succeeded. If they expended say $1B on various open problems, and only 1% succeeded, is that the kind of investment that one will make? (The $10M alone is more than what would have been devoted to academics investigating these issues. But realistically one has to quantify the cost of the entire enterprise, and the probability of success vs failure.

From OAI we're hearing only about 700+ successes. Relative to what failure rate? The cost of success isn't merely the cost of each success, but rather the overall cost of all experiments, of which -- I'd guess -- a huge fraction failed.

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I think that’s what VCs do. Only a fraction of investments succeed and the ones that do pay off big time.

Now, imagine having a mechanism that will constantly improve, so you can go from 1% success rate to 5% success rate in say a year.

I see your point. What I think could be different is the speed at which things can be done compared to conventional ways of discovery.

Think of a COVID/MRNA like solution for other areas.

I'm all in on that, and I own Moderna stock ... so there's that. But we sort of understand the pathway there: we want to introduce a protein that induces an immune system reaction. Traditionally you re-engineer a virus that produces that protein that produces the RNA that gets transcribed into that protein. But instead, with better tech, you introduce that RNA directly. Simpler and faster. And perhaps you can use that mechanism to introduce other proteins for other ends (as recently with its personalized melanoma treatments).

I'm all for the notion that ML can improve 1% to 5%. But I haven't seen anyone suggesting HOW that happens. So far, it looks like taking 1000s of candidates to 10,000 candidates, which just further clogs the pipeline. Using ML to automate lab tests seems like a win there. But still, pushing those through phase 1, phase 2, phase 3 clinical trials is the bottleneck. 90% of what enters that pipeline dies, and each involves many years and many $100s of millions of investment.

But as I noted: the quicker path would seem to be through peptides rather than small drug molecules. ML does pretty well with proteins/peptide chains and mRNA provides a way to introduce them (or injection, like GLP1's). Still requires all the testing, though, but presumably we know more about how they degrade, etc. over time.

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