> 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-...
If it can optimize in a segment that is 10-20% of drug development that still sounds very useful…?
If so, it will end up just like 3d printed houses - not worth optimizing.
20% is no joke, no need to downplay potential benefits.
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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.