Comment by helsinkiandrew
6 hours ago
> like proposing initial designs for new molecules
James van Geelen Of Citrini research brought up a good point on a podcast this week, that designing the molecules is perhaps 10-20% of the effort in bringing a drug to market. We have more possible drug designs than ever get developed.
Whilst AI drugs might be more likely to pass phase 2 tests than others and AI might be applied to the rest of the process there are still big bottlenecks in the way of AI creating a radical change.
https://podcasts.apple.com/gb/podcast/monetary-matters-with-...
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.
One might argue that we have calibrated drug safety to such a high degree that the aggregate negative social impacts are far higher than the costs of side effects. Of course we don’t want another thalidomide catastrophe but I think there are large potential test groups to mine who are much less sensitive to the risks. For example, the elderly and those with terminal diagnoses. Right now we only permit drug testing in very limited cases for the latter group, and only when the treatment is related to their terminal diagnosis. I’m seeing older people taking all kinds of experimental anti-aging drugs right now, either off label, or novel. We’re not collecting any of this data empirically because what they’re doing is not legal.
Further, [we are getting much better at simulating drug interactions.](https://pubmed.ncbi.nlm.nih.gov/41155903/) I strongly suspect AI will offer new probabilistic models to funnel novel drugs into more targeted trials which are much more likely to succeed. If we had 90% certainty of success and high certainty of lower adverse reactions I think we could allow much less stringent human trials. That means much lower cost and much faster turn-around.
My wife is a doctorate in pharmacology, I made exactly this argument to her but she dismantled it pretty quickly.
a) We do test on terminally ill people, but after testing in other models. b) Thalidomide was all roses compared to the horrors you can inflict. c) Profit-driven industry makes extremely perverse incentives.
Still I do come down more on your side of the argument, and we have historically made massive leaps doing some horrific things.
I wonder if we can get better models based off of human cell-lines / tissues? Like CRISPR did?
Besides moral qualms I think it could be difficult establishing a representative baseline with the elderly and terminally ill
“The elderly” might be much less sensitive to risks but it may still be an issue if the experimental drug you gave Grandma turns her into soup.
> For example, the elderly and those with terminal diagnoses.
The amount of disdain you have for human life when it's past an accepted state of freshness is disgusting.
If so, it will end up just like 3d printed houses - not worth optimizing.
this hot take is more important than we all think. While adding features is more optimised than ever (e.g. auto software development) many systems and processes are fairly optimised already. Many agent implementations I have seen don't optimise the process but replicate/automate it. This has made me think that some things are already optimised, there is always room for improvement but those gains might not weigh up always in the race use AI for everything.
If it can optimize in a segment that is 10-20% of drug development that still sounds very useful…?
20% is no joke, no need to downplay potential benefits.
But if that 20% is not the bottleneck then at most your market is replacing the labor required to get that 20% to the next phase, plus you will probably not be able to capture full value here because of lab/model competition.
It’s only a benefit if it doesn’t bog the next stage down in noise. At that point it could be detrimental.
We already see that with software. It will be exactly the same with drug design.
My daughter is a leading biochem at one of the big pharma companies and that’s their principal worry about this. It’s really easy to create a lot of information these days which compromises the entire process.
It's also not the magical thing that will solve all diseases like some people who are starting to think we're going to enter the singularity soon seem to think.
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