| ▲ | fabian2k a day ago |
| I don't see where AI would currently get the data needed to reliably develop new drugs. Or specifically, to develop drugs with a higher success rate than what we're doing now. The AI would need to be able to predict all effects a specific molecule has on a human. I mean we can do parts of that today, for the specific interactions we already understand. But that would not be fundamentally different and very unlikely to be more effective than current drug development. |
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| ▲ | teeray a day ago | parent | next [-] |
| More than that, as I understand it, coming up with drugs is not the bottleneck. It’s all the testing and clinical trials that take forever. Drug companies optimize this by having multiple irons in the fire at any time, but it’s not even like AI would help there. The work is in shepherding drugs through the approval process. |
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| ▲ | estearum a day ago | parent | next [-] | | To clarify, it's not the approval process that's challenging. It's making a safe and effective drug, then proving that it's safe and effective. If you achieve those, approval is pretty much trivial. | | |
| ▲ | AnthonyMouse 20 hours ago | parent [-] | | That's assuming that the Venn diagram of "proving that it's safe and effective" and "following the approval process" is one circle and there are no unnecessary requirements. Which is demonstrably false, since e.g. the US and Europe have different requirements and neither is a subset of the other. If we accept that they both require adequate proof of "safe and effective" that means they also both impose unnecessary requirements, since otherwise the requirements would fully overlap. | | |
| ▲ | estearum 20 hours ago | parent [-] | | 1) No, not even theoretically. Since "prove" means different things to different organizations. 2) I didn't say there are no additional requirements. For example, you have to fill out some paperwork. I said they are trivial compared to actually making a drug that works and proving that it works. Drugs do not die because of the approval processes, they die because it's unfathomably hard to make drugs that work and then to prove that they work. | | |
| ▲ | AnthonyMouse 18 hours ago | parent [-] | | > No, not even theoretically. Since "prove" means different things to different organizations. This is circular logic. It's just defining "prove" to be coterminous with what a given set of regulations require in order to claim that it's the "proving" which is the hard part and not the complying with the regulations. For it not to be that, "prove" would need an independent objective meaning, and then we're back to non-overlapping circles. > I said they are trivial compared to actually making a drug that works and proving that it works. If this was the case then there wouldn't be drugs that are prohibited in the US but not other first world countries (or vice versa) specifically because they can't be patented or the patents have already expired, and as a result no one pays for the prohibitively expensive regulatory process to get them approved. They've already been proven in the other countries -- higher numbers of patients than you have in normal clinical trials -- but because the approval rules are excessively rigid and unreasonable, that doesn't count. | | |
| ▲ | estearum 7 hours ago | parent [-] | | > They've already been proven in the other countries -- higher numbers of patients than you have in normal clinical trials -- but because the approval rules are excessively rigid and unreasonable, that doesn't count. You are wrong. You absolutely can get drugs approved in the US based on real-world studies or observational data. The reason this happens so infrequently is actually because it's virtually impossible to prove a drug is safe and effective using real-world studies or observational data. We don't rely on (super expensive) RCTs because FDA says you need RCTs. We rely on them because FDA says you need to prove your drugs are safe and effective, and no one knows a better/cheaper/faster way to do that than RCTs. You can absolutely bundle up a bunch of observational data and file for approval. You likely won't get it, not because observational data isn't allowed, but that observational data almost certainly cannot prove the claims you're making. Drug makers know this, so they don't even try. But you're welcome to! | | |
| ▲ | AnthonyMouse 4 hours ago | parent [-] | | > The reason this happens so infrequently is actually because it's virtually impossible to prove a drug is safe and effective using real-world studies or observational data. This is back to the thing where you define the requirement in terms of the regulations. If you have 10,000 people taking a drug for years and it's "unsafe" then it would be conspicuous. A third of them would be dead or their children would have a significantly higher proportion of birth defects than the overall population and the people with the same condition who took a different drug, etc. Whereas if it has some minor side effects, it might not be able to pick that up, but that's because they're minor, so why should that be a reason to prohibit it by law? The thing that really makes the point is how different the process is for food. Their hands are tied there, because food is full of chemistry and biology. You're cooking a plant. Heat triggers chemical reactions. If you use a different temperature for a different amount of time, you get different chemical reactions. The same thing if you store it for a different period of time under different conditions. If you grow the same plant in a different place or use a different variety. If you change the proportions of different ingredients. It would be totally infeasible to require RCTs every time a restaurant wanted to add a new item to their menu or change their supplier of tomatoes. We don't even do that with drugs -- different brands of the same drug have "inactive ingredients" and the requirement is essentially that they themselves be on the safe list but not that anybody has done a comprehensive study to prove that they're inert and unable to affect e.g. the metabolism of the active ingredient. "Sugar is a drug" but the patient doesn't even get a choice about whether it's that or something else they have to ingest along with their prescription. So every time you bite into a sandwich you're putting a million unknown chemical compounds into your body. We're not even talking about herbicides or preservatives here. The chicken feed was fish meal from the Pacific rather than the Atlantic and that affects what's in the chicken, which results in a different chemical reaction when you cook it in the sauce. And for the most part that doesn't matter. The food is more likely to be bad for you because it's full of sodium and high fructose corn syrup on purpose than because it contains <enigmatic organic compound> unintentionally. We have more problems when something contains e.g. heavy metals (which we already know are bad for you) than when it contains something novel. So we're running the scenario where you don't require RCTs for everything in parallel because we have to, and it seems to be fine. What does that say about the claim that not de facto requiring RCTs is "unsafe"? |
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| ▲ | fabian2k a day ago | parent | prev | next [-] | | 90% of drugs fail in the phase 1-3 trials. If AI could create drug candidates that fail less often, that would be a huge win. I doubt it can right now, and it would take years to decades and billions of dollars to figure out if the AI is actually better at that than humans. | | |
| ▲ | refurb 21 hours ago | parent [-] | | That is true - that if AI reduced failure rates it would be a huge success. However drugs fail because we don’t understand the biology well enough. Drug researchers develop drugs based on all their knowledge of biology, and when they fail it’s because the biology didn’t work the way they thought it did. So if the current understanding is the cause of failure how can AI help? It can chug through vast amounts of data, but the data doesn’t explain the biology well enough. Now what? | | |
| ▲ | XenophileJKO 12 hours ago | parent [-] | | Just like all the mathematicians were applying all that they know to solve the math problems.. and AI certainly can't do that... Yes, biology is different and experimental evidence is very different than mathmatics, but the ability to pull from a lot of known information and formulate possible problems that biologists didn't anticipate isn't really a stretch from where we are on the capability curve right now. | | |
| ▲ | refurb 10 hours ago | parent [-] | | Math is an entirely different problem to solve. Look at the showcase AI proof of the Naiver-Stokes existence. It basically took an equation and proved it was valid across all possible variables. That’s a problem in a closed system. There is no question how the function works. The tools needed to solve it are known. Biology is like solving a math equation we only have a crude approximation for. Parts of the equation are missing and there are variables we aren’t aware of. You also have a limited set of tools to test the equation. |
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| ▲ | XenophileJKO a day ago | parent | prev [-] | | I'm so sick of this argument. All AI has to do is catch problems earlier in the pipeline to outcome success rates. You just have to think about the system wholisticly, the ways that AI could make trials more consistently positive are many. Moving the needle on any process upstream of the bottleneck benefits greatly. Currently something like 80% of trials fail, so without changing trial rate you have 5x potential sitting on the table. | | |
| ▲ | estearum a day ago | parent [-] | | > All AI has to do is catch problems earlier in the pipeline to outcome success rates. Which it has shown no ability to do, so far. | | |
| ▲ | vanuatu a day ago | parent | next [-] | | i think it's a promising ai-shaped problem you have inputs and outputs mapped for the trials and know historically how trials tend to fail not a leap to have ai assist in proposing or reviewing trials to reduce failure rates. even a 10% reduction is massive | | |
| ▲ | estearum a day ago | parent [-] | | It's not an AI-shaped problem. The mapping from inputs to outputs is something like "the most complex process humans are aware of." You're trying to map a chemical in a pill (or a shot) from the organism down to organs down to cells, through the metabolome, proteome, genome, and so on, both down then back up, until you get back to "outcome that a human being feels positively about." |
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| ▲ | malfist a day ago | parent | prev [-] | | All AI has to do to make poverty nonexistent is to find a way to make everyone rich. All AI has to do to prove n=NP is to just prove n is equal to NP All ai has to do to make space travel possible is find a way around the laws of thermodynamics |
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| ▲ | Otterly99 12 hours ago | parent | prev | next [-] |
| We do not understand the human body well enough to be able to directly jump from drug design to effect on the human body. Hell, I'm not even sure you could even do that on a single cell level, apart from the obvious "this kills the cell". The whole field of biology is a huge mess, and it is very hard to generalize results from the different scales (protein -> proteins -> single cell -> tissue -> living creature -> human). Not only that, long-term effects take, by definition, a long time to surface and cannot be accelerated. This is what AI companies either have a hard time understanding, or choose to ignore. |
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| ▲ | shevy-java a day ago | parent | prev | next [-] |
| But why not? I mean, you can determine the molecular structure of things. Look at xray and cryo-electron microscopy. Why should that be a topic that can not be solved programmatically? > The AI would need to be able to predict all effects a specific molecule has on a human. So you can have fully automated labs. You have organoids that can be combined as a test system (or as pseudo mini brains). Why would that be impossible by AI? I don't understand that assumption. To me, the problem set is finite, so of course it COULD be solved. Which unique feature do humans have that AI could not simulate? We have robots and automated labs. I don't really see why humans would be necessary to find autogenerated drug xyz. > very unlikely to be more effective than current drug development. I highly doubt that claim. I more smell that some people are worried about losing overpriced drugs here. Imagine if AI were to lead to cheaper drugs that work perfectly fine still. Personally I'd still prefer to see AI vanish, but that is also unlikely to happen. I don't understand the rationale you give here though. Why can AI not design drugs? What is the real explanation here? I mean, penicillin was invested by fungi and discovered mostly by accident. AI can not have anything like that? |
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| ▲ | fabian2k a day ago | parent | next [-] | | Solving protein structures, and specifically protein structures bound to ligands that are your potential drug candidates are very, very early phases in drug development. That is before even the phase 1 trials. It's important work and a significant effort, but it's not where the actual difficulty in drug development lies. 90% of drugs fail after that point, and that is where you need to get better if you want to beat the current approaches. AI also can't run drug trials faster than humans. They take a lot of time, effort and money. | | |
| ▲ | signatoremo a day ago | parent | next [-] | | China can run drug trials much faster than in the West - [0] Why do you think the status quo is optimal that can’t be accelerated and done at lower cost? Of course AI can’t do trials on its own. [0] - https://www.bloomberg.com/news/features/2026-10-29/china-s-s... | | |
| ▲ | stackskipton a day ago | parent [-] | | No, but issue with status quo is the bureaucracy and politics. As noted in the article, it's cheaper and faster in China because regulations appear to be laxer and it's possibly resulted in deaths. Whether or not that's acceptable is political discussion, not an AI one. |
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| ▲ | 21 hours ago | parent | prev [-] | | [deleted] |
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| ▲ | a day ago | parent | prev | next [-] | | [deleted] | |
| ▲ | croes a day ago | parent | prev [-] | | The world isn’t deterministic and there isn’t just one human you have to test against. What works for one, kills another. Just look at what peanuts can do. If your tests don‘t take something like this into account, you get in trouble. And there are lots of those things to test against, known and even unknown. And then there is evolution. What works today, doesn’t work tomorrow. Or things like this happen https://www.wgbh.org/tv-shows/reactions/how-an-hiv-miracle-d... And of course there is the profit thing, that creates problems to give everyone the treatment they need. AI can help prevent defeat but there is no winning |
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| ▲ | Bengalilol a day ago | parent | prev [-] |
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