3 ms·

In order for an AI to evaluate the effect of a small molecule on the brain, it would have to... simulate the operation of a human brain in a simulated environme

In order for an AI to evaluate the effect of a small molecule on the brain, it would have to... simulate the operation of a human brain in a simulated environment. Similarly, to avoid Thalidomide-style disasters, it would have to simulate the conception, development and growth to adulthood of a human. These things are... physically possible, but have WBE and uploads as a hard requirement. Those are going to affect a hell of a lot of things more than the drug industry! Amusingly, machine-phase nanotechology and blood nanobots would be easier to evaluate, since simple cell-level mechanical interventions (reading surface proteins on cancer cells and chopping them up, say) will have fewer interactions than a small molecule that diffuses into every cell in the body.

jocoda 3y ago
WBE?
AnthonyMouse 3y ago
> In order for an AI to evaluate the effect of a small molecule on the brain, it would have to... simulate the operation of a human brain in a simulated environment. Similarly, to avoid Thalidomide-style disasters, it would have to simulate the conception, development and growth to adulthood of a human. This is how the human doctors who have cured things in the past have done it, is it? The way this is going to work, when it happens, is that you'll ask the AI for a cure and it will give you a hundred candidates. A human doctor will look at the list and throw half of them out because they're toxic, several of the remainder will be excluded by animal trials, the few remaining will proceed to human clinical trials and one of them will actually work.
- zer00eyz 3y ago
  >> The way this is going to work... Google is already doing something like this: https://arstechnica.com/ai/2023/11/googles-deepmind-finds-2-2m-crystal-structures-in-materials-science-win/ https://arstechnica.com/ai/2023/11/googles-deepmind-finds-2-... This is great if you want to use well understood pathways or make new drugs that you can then patent and mark up. New pathways are gonna require feeding data into these models in the first place. Your not getting ozempic out of ML without doing the ground work first: https://globalnews.ca/news/9793403/ozempic-canada-scientist-venomous-lizard-weight-loss/ https://globalnews.ca/news/9793403/ozempic-canada-scientist-...
  - AnthonyMouse 3y ago
    > New pathways are gonna require feeding data into these models in the first place. Your not getting ozempic out of ML without doing the ground work first Sure, but a lot of the ground work has already been done, or is susceptible to simulation. They're getting a lot of results out of simulating protein folding and things like that.
- sbierwagen 3y ago
  Then AI will have no effect on the drug industry at all. The rate limiting step isn't "thinking up molecules." The University of Bern enumerated all possible molecules composed only of hydrogen, carbon, nitrogen, oxygen, sulfur and chlorine, up to 17 atoms. That produced 166 billion molecules. https://pubs.acs.org/doi/10.1021/ci300415d https://pubs.acs.org/doi/10.1021/ci300415d There are commercial drugs considerably larger than that. We've got molecular structures out the nose. There is no shortage of molecules. The problem is the clinical trial. Putting drugs in humans and seeing what they do. That's the part that takes years and tens of millions of dollars. Using AI for anything else is like saying Microsoft Powerpoint accelerated drug development. Sure, it made presentations easier, but did it do anything for the problem of putting chemicals in people?
  - AnthonyMouse 3y ago
    > That produced 166 billion molecules. Which is useless, because you can't run 166 billion clinical trials. But you could run half a dozen if there's a strong chance one of them will be a success. Filtering the list down to 100 molecules from 166 billion, some of which can be further eliminated by human evaluation without the expense of clinical trials, is actually useful. You still ultimately have to do the clinical trial, because there is no substitute for empiricism. > That's the part that takes years and tens of millions of dollars. It doesn't matter if it takes tens of millions of dollars if the result is a billion dollar drug.
  - blackbear_ 3y ago
    > The problem is the clinical trial. [...] That's the part that takes years and tens of millions of dollars. Clinical trials only start after about five years of research and development. While they do represent a large part of the budget (even in the hundreds of millions of dollars), there are countless of other necessary steps before, during, and after trials to ensure that drugs are both safe and effective. The problem is that we still don't understand how most of these molecules behave in the body, and how we can produce them reliably and efficiently enough, which brings me to the next point: > [...] but did it do anything for the problem of putting chemicals in people? Yes, there are plenty of problems that AI and computational chemistry already help with in the pharmaceutical industry, including predicting solubility, stability, crystallization, granulation, toxicity, pharmacokinetics, developing the formulation, optimizing and scaling up both the synthesis and production process, developing appropriate techniques for quality control, and so on. In all these cases and more, AI can help reduce the amount of experiments that need to be done in the lab, which require highly specialized equipment, personnel, and a lot of time. Oh and design of experiments is also a very important topic, again aiming at reducing the amount of lab time needed. Admittedly, most of these things aim at ensuring that we do not put the wrong chemical in people, but they do represent most of the R&D effort spent in pharma, and reducing everything to clinical trials is not correct. There is a very wide gap between "AI will design drugs entirely on its own" and "AI is useless".
nodogoto 3y ago
>In order for an AI to evaluate the effect of a small molecule on the brain, it would have to... simulate the operation of a human brain in a simulated environment. Humans aren't capable of doing this, but still make useful drug discoveries. AI can be empowered to conduct research in the real world, it doesn't need to simulate everything.
- ben_w 3y ago
  We start by doing them on mice (well, in vitro first, mice as the first in vivo), who have no say in the matter; and as mice are only rough analogues of humans, the human trials are still cautious once the animal trials are over.
  - ChatGTP 3y ago
    We also develop a lot of drugs with a which have side-effects, which will is probably better than no drugs in most cases, the side effects are because it's a lot of educated guesswork.

jocoda 3y ago

WBE?

AnthonyMouse 3y ago

> In order for an AI to evaluate the effect of a small molecule on the brain, it would have to... simulate the operation of a human brain in a simulated environment. Similarly, to avoid Thalidomide-style disasters, it would have to simulate the conception, development and growth to adulthood of a human. This is how the human doctors who have cured things in the past have done it, is it? The way this is going to work, when it happens, is that you'll ask the AI for a cure and it will give you a hundred candidates. A human doctor will look at the list and throw half of them out because they're toxic, several of the remainder will be excluded by animal trials, the few remaining will proceed to human clinical trials and one of them will actually work.

zer00eyz 3y ago
>> The way this is going to work... Google is already doing something like this: https://arstechnica.com/ai/2023/11/googles-deepmind-finds-2-2m-crystal-structures-in-materials-science-win/ https://arstechnica.com/ai/2023/11/googles-deepmind-finds-2-... This is great if you want to use well understood pathways or make new drugs that you can then patent and mark up. New pathways are gonna require feeding data into these models in the first place. Your not getting ozempic out of ML without doing the ground work first: https://globalnews.ca/news/9793403/ozempic-canada-scientist-venomous-lizard-weight-loss/ https://globalnews.ca/news/9793403/ozempic-canada-scientist-...
- AnthonyMouse 3y ago
  > New pathways are gonna require feeding data into these models in the first place. Your not getting ozempic out of ML without doing the ground work first Sure, but a lot of the ground work has already been done, or is susceptible to simulation. They're getting a lot of results out of simulating protein folding and things like that.
sbierwagen 3y ago
Then AI will have no effect on the drug industry at all. The rate limiting step isn't "thinking up molecules." The University of Bern enumerated all possible molecules composed only of hydrogen, carbon, nitrogen, oxygen, sulfur and chlorine, up to 17 atoms. That produced 166 billion molecules. https://pubs.acs.org/doi/10.1021/ci300415d https://pubs.acs.org/doi/10.1021/ci300415d There are commercial drugs considerably larger than that. We've got molecular structures out the nose. There is no shortage of molecules. The problem is the clinical trial. Putting drugs in humans and seeing what they do. That's the part that takes years and tens of millions of dollars. Using AI for anything else is like saying Microsoft Powerpoint accelerated drug development. Sure, it made presentations easier, but did it do anything for the problem of putting chemicals in people?
- AnthonyMouse 3y ago
  > That produced 166 billion molecules. Which is useless, because you can't run 166 billion clinical trials. But you could run half a dozen if there's a strong chance one of them will be a success. Filtering the list down to 100 molecules from 166 billion, some of which can be further eliminated by human evaluation without the expense of clinical trials, is actually useful. You still ultimately have to do the clinical trial, because there is no substitute for empiricism. > That's the part that takes years and tens of millions of dollars. It doesn't matter if it takes tens of millions of dollars if the result is a billion dollar drug.
- blackbear_ 3y ago
  > The problem is the clinical trial. [...] That's the part that takes years and tens of millions of dollars. Clinical trials only start after about five years of research and development. While they do represent a large part of the budget (even in the hundreds of millions of dollars), there are countless of other necessary steps before, during, and after trials to ensure that drugs are both safe and effective. The problem is that we still don't understand how most of these molecules behave in the body, and how we can produce them reliably and efficiently enough, which brings me to the next point: > [...] but did it do anything for the problem of putting chemicals in people? Yes, there are plenty of problems that AI and computational chemistry already help with in the pharmaceutical industry, including predicting solubility, stability, crystallization, granulation, toxicity, pharmacokinetics, developing the formulation, optimizing and scaling up both the synthesis and production process, developing appropriate techniques for quality control, and so on. In all these cases and more, AI can help reduce the amount of experiments that need to be done in the lab, which require highly specialized equipment, personnel, and a lot of time. Oh and design of experiments is also a very important topic, again aiming at reducing the amount of lab time needed. Admittedly, most of these things aim at ensuring that we do not put the wrong chemical in people, but they do represent most of the R&D effort spent in pharma, and reducing everything to clinical trials is not correct. There is a very wide gap between "AI will design drugs entirely on its own" and "AI is useless".

nodogoto 3y ago

>In order for an AI to evaluate the effect of a small molecule on the brain, it would have to... simulate the operation of a human brain in a simulated environment. Humans aren't capable of doing this, but still make useful drug discoveries. AI can be empowered to conduct research in the real world, it doesn't need to simulate everything.

ben_w 3y ago
We start by doing them on mice (well, in vitro first, mice as the first in vivo), who have no say in the matter; and as mice are only rough analogues of humans, the human trials are still cautious once the animal trials are over.
- ChatGTP 3y ago
  We also develop a lot of drugs with a which have side-effects, which will is probably better than no drugs in most cases, the side effects are because it's a lot of educated guesswork.