The problem with generative AI is that it often gets simple things wrong. It can generate a picture of a dog with five legs. The geometry of a drug molecule is very critical! What if the generative model generated a target molecule with the wrong number of geometric features like the number of atoms in a molecule ? Not good 😊
Sure. But what makes Ruxandra's critique here potent is that it holds if the models do exactly what we want. Critiquing where the models are today usually gets dismissed because they get much better every 6 months. The value of this argument is that it just skips over that problem and gets to the bigger one, which is if models are *perfect* speed-to-discovery might 1) stay bad or 2) get worse if we don't fix regulation.
But not everything is in conflict. More new hypotheses almost certainly *is* a good thing, and is more likely to lead to more “better” hypotheses, not fewer.
Largely agree with the thesis here, worth noting that transcription factors (intrinsically disordered, historically considered “undruggable”) may fall under the same category of GPCRs where we really are limited by drug candidates and AI could help. Also many isolated cases, consider suzetrigine, breakthrough pain medication where a large part of the difficulty was the excruciatingly difficult med-chem optimization. Actually now that I think about it, what percent of the proteome is currently considered druggable? There’s a plausible world where we have many hypotheses and possible drugs, but they’re systematically concentrated around kinase inhibitors or whatever, so there is tons of low hanging fruit in the biology sense that better AI drug design could help access
I still think that getting the right dosage clinical effect etc will involve in-human experimentation. Look at GLP-1 agonists, which took time to perfect for obesity and there were iterations between clinical trials and peptide chemistry improvements.
Yeah this is why I largely agree! Just worth noting that one driver of clinical trial costs is weak effect sizes; a me-too statin with a number needed to treat of 1000 will cost a ton to test, whereas a clinical trial for penicillin mostly consists of observing “hey my patients are all better instead of immediately dead”. So it’s possible that opening up whole new classes of drugs will cut down on costs that way.
I generally agree with your take here. People are way overindexing on what AI can do in biology. AI is a great tool and for certain problems it's the best approach we have, but it's nowhere near the silver bullet that can just magically generate answers to all our biological questions.
When you begin to actually think what the body needs to be healthy… it’s not poisonous and useless drugs but nutrition. You will shift to a real healthy life. AI it’s programmed by the same companies that want to keep you sick for profits.
The problem with generative AI is that it often gets simple things wrong. It can generate a picture of a dog with five legs. The geometry of a drug molecule is very critical! What if the generative model generated a target molecule with the wrong number of geometric features like the number of atoms in a molecule ? Not good 😊
Well new molecules could be checked by humans I guess!
Sure. But what makes Ruxandra's critique here potent is that it holds if the models do exactly what we want. Critiquing where the models are today usually gets dismissed because they get much better every 6 months. The value of this argument is that it just skips over that problem and gets to the bigger one, which is if models are *perfect* speed-to-discovery might 1) stay bad or 2) get worse if we don't fix regulation.
“We do not need more hypotheses, we need better ones”
Sorry, but this is just a false assertion.
We need both.
Just because the former does not *guarantee* the latter, you have no evidence - and precious little logic - that the former will reduce the latter.
P.S. But if you wanna argue, as Scannell does, that the bigger issue is all the regulation, you will get no disagreement from me.
There's no point in generating new hypotheses if we can't test them.
And obvsly I say we need to generate better ones and that's good.
But not everything is in conflict. More new hypotheses almost certainly *is* a good thing, and is more likely to lead to more “better” hypotheses, not fewer.
I mean, no. We've had more scientists and more papers and they haven't led to better hypotheses. We've literally ran the experiment.
Largely agree with the thesis here, worth noting that transcription factors (intrinsically disordered, historically considered “undruggable”) may fall under the same category of GPCRs where we really are limited by drug candidates and AI could help. Also many isolated cases, consider suzetrigine, breakthrough pain medication where a large part of the difficulty was the excruciatingly difficult med-chem optimization. Actually now that I think about it, what percent of the proteome is currently considered druggable? There’s a plausible world where we have many hypotheses and possible drugs, but they’re systematically concentrated around kinase inhibitors or whatever, so there is tons of low hanging fruit in the biology sense that better AI drug design could help access
I still think that getting the right dosage clinical effect etc will involve in-human experimentation. Look at GLP-1 agonists, which took time to perfect for obesity and there were iterations between clinical trials and peptide chemistry improvements.
Yeah this is why I largely agree! Just worth noting that one driver of clinical trial costs is weak effect sizes; a me-too statin with a number needed to treat of 1000 will cost a ton to test, whereas a clinical trial for penicillin mostly consists of observing “hey my patients are all better instead of immediately dead”. So it’s possible that opening up whole new classes of drugs will cut down on costs that way.
Do you always need the whole human for drug trials?
ahah what do you have in mind here?
Testing on individual organs or organoids.
I generally agree with your take here. People are way overindexing on what AI can do in biology. AI is a great tool and for certain problems it's the best approach we have, but it's nowhere near the silver bullet that can just magically generate answers to all our biological questions.
See also: https://blog.genesmindsmachines.com/p/we-still-cant-predict-much-of-anything
When you begin to actually think what the body needs to be healthy… it’s not poisonous and useless drugs but nutrition. You will shift to a real healthy life. AI it’s programmed by the same companies that want to keep you sick for profits.
Really appreciate this kind of post!
amen to that!