5 ms·

Algorithmically-created medicine to be used on humans for first time

56 points by davidfoster 7y ago

yters 7y ago
I would say this is computer engineer created medicine. They're just doing a big brute force search to find matches. But, I guess if every algorithm is an AI, then this counts. But then so does every other instance of algorithms being used in science, which is everything these days. Perhaps AI is just a buzzword used to gain attention???
- kupopuffs 7y ago
  yeah, tell me something new smirking face emoji
- __s 7y ago
  We were doomed as soon as we made the mistake of trusting an AI to prove the four color theorem
  - yters 7y ago
    Can the AI prove the AI that proved the four color theorem!?!
  - ska 7y ago
    There was nothing AI about the original four color proof. Are you thinking of something more recent?
    - dekhn 7y ago
      In the future AI will be the term people use to talk about all of CS, regardless of it being artificial, or intelligent.
      - ska 7y ago
        I don't think that is likely. More likely we follow the pattern we've had for the last 50-60 years; things we call "AI" stop being called "AI" after we understand how they work well enough.
- anthony_doan 7y ago
  It is. Health sector uses mostly statistic. We use randomized experiment and with survival analysis and longitudinal analysis. Then we're also on observation studies with casuality since the 1980s between statistician and econometrician. We're using propensity modeling now for observational studies and quasi experiment. It's where a new wave of interesting stuff is happening all the while AI is taking up this weird hype in the programming world for healthcare. Data science and ML really likes to make things black boxes while ignoring the data and not accounting for uncertainty. I'm going to flat out state, you're not doing this in healthcare. There is no way in hell this is going to work without statistic. Judea Pearl is gonna have to marry statistic with CS if he wants any real headway with causality and even then I'd take Rubin, Rosenbaum, and Angrist.
natechols 7y ago
Obligatory rebuttal from someone who actually knows about drug development: https://blogs.sciencemag.org/pipeline/archives/2020/01/31/another-ai-generated-drug https://blogs.sciencemag.org/pipeline/archives/2020/01/31/an...
- dang 7y ago
  Ok, based on that text, we've replaced AI with algorithms in the title above. If someone can suggest a more accurate and neutral title, we can change it again.
  - natechols 7y ago
    I think it's fine for HN to use the same title that the BBC used - the problem is it's still a giant hand-waving oversimplification either way. It shouldn't be the moderator's job to clarify sloppy science journalism.
    - dang 7y ago
      The HN guidelines say: Please use the original title, unless it is misleading or linkbait. (https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html) That rule serves HN well. People come here to get relief from the world of linkbait, and people who want such relief tend to be the kind of users HN benefits from. Giant hand-waving oversimplifications are certainly misleading-or-linkbait and usually both. So the rule means we should change it. It's just a question of what to change it to. Generally there's a subtitle or heading or photo caption or first sentence that says what the article is actually about—especially in major media, where the headlines are written by specialists who have nothing to do with the article. But I couldn't find any representative phrase in the article itself this time.
      - natechols 7y ago
        I'm trying to think of a better title and am blanking right now, but it's the "-created" that annoys me - it implies that they fed a bunch of data into a black box and a molecule popped out ready for human testing. What really happened, according to the BBC article, is they enhanced a standard virtual screening pipeline with AI. This is a totally useful and legitimate thing to do and I'm sure it has some cost savings, but they're hand-waving past all of the human labor that is unavoidable in drug discovery.
dekhn 7y ago
this is just another example of ML/bio hype.
allovernow 7y ago
There is an unfortunate lack of technical information in this article, but before the ML naysayers arrive in full force to point out that this isn't real AI or ML or what have you, it's quite possible they were using deep neural nets to heuristically approximate complex simulations 3-6 orders of magnitude more quickly than typical simulation. We're doing the same in other domains now.
- natechols 7y ago
  Simulations are not the major bottleneck in drug development. More generally, the naysayers in this case are likely to be people who actually have some knowledge of drug development, and have seen countless companies that promised to revolutionize drug discovery by using computers, most of which are no longer in existence. If an AI-powered company can suddenly start passing Phase III clinical trials at an unprecedented rate, then I'll be genuinely impressed.
- ashleyn 7y ago
  >We're doing the same in other domains now. One of the first examples I remember reading about this was an antenna designed by an AI at NASA[1]. This was back in the days when "AI" wasn't the buzzword so much as "genetic algorithms" was. It ended up looking like this crazy zigzag thing. 1) https://www.nasa.gov/centers/ames/research/exploringtheuniverse/borg.html https://www.nasa.gov/centers/ames/research/exploringtheunive...
- jcranmer 7y ago
  Drug development roughly consists of the following steps: 1. Figure out what enzyme or receptor you have to mess with to cause the disease. You can skip this step if you decide just to blast away in a phenotypic screen. 2. Design a molecule that will actually mess with the enzyme/receptor to prevent it from malfunctioning in whatever way is causing the disease. 3. Figure out how to actually synthesize said molecule. 4. Now make sure this molecule can actually get to the sites where it needs to go to hit the target... 5. ... that it lasts there long enough and in enough potency to do its job ... 6. ... that it doesn't screw anything else up along the way (or get converted by your body into something that does)... 7. ... and that there's no compensatory mechanism that still causes the disease after your drug successfully gummed up what it was supposed to. Or maybe you failed at step 1 and you're barking up the wrong tree. Roughly speaking, the last four steps will correspond to the different trials you have to run to get your drug approved. About 90% of drug candidates that make it to step 4 fail to make it all the way through step 7. The difficult things that kill drugs are understanding their toxicity profiles and other off-target effects. We suck at this because we have so little data (and the effects are quite complicated), and modern AI techniques are mostly borne on shoveling piles of data and hoping for the best--not a good match.

56 points by davidfoster 7y ago

I would say this is computer engineer created medicine. They're just doing a big brute force search to find matches. But, I guess if every algorithm is an AI, then this counts. But then so does every other instance of algorithms being used in science, which is everything these days. Perhaps AI is just a buzzword used to gain attention???

kupopuffs 7y ago
yeah, tell me something new smirking face emoji
__s 7y ago
We were doomed as soon as we made the mistake of trusting an AI to prove the four color theorem
- yters 7y ago
  Can the AI prove the AI that proved the four color theorem!?!
- ska 7y ago
  There was nothing AI about the original four color proof. Are you thinking of something more recent?
  - dekhn 7y ago
    In the future AI will be the term people use to talk about all of CS, regardless of it being artificial, or intelligent.
    - ska 7y ago
      I don't think that is likely. More likely we follow the pattern we've had for the last 50-60 years; things we call "AI" stop being called "AI" after we understand how they work well enough.
anthony_doan 7y ago
It is. Health sector uses mostly statistic. We use randomized experiment and with survival analysis and longitudinal analysis. Then we're also on observation studies with casuality since the 1980s between statistician and econometrician. We're using propensity modeling now for observational studies and quasi experiment. It's where a new wave of interesting stuff is happening all the while AI is taking up this weird hype in the programming world for healthcare. Data science and ML really likes to make things black boxes while ignoring the data and not accounting for uncertainty. I'm going to flat out state, you're not doing this in healthcare. There is no way in hell this is going to work without statistic. Judea Pearl is gonna have to marry statistic with CS if he wants any real headway with causality and even then I'd take Rubin, Rosenbaum, and Angrist.

natechols 7y ago

Obligatory rebuttal from someone who actually knows about drug development: https://blogs.sciencemag.org/pipeline/archives/2020/01/31/another-ai-generated-drug https://blogs.sciencemag.org/pipeline/archives/2020/01/31/an...

dang 7y ago
Ok, based on that text, we've replaced AI with algorithms in the title above. If someone can suggest a more accurate and neutral title, we can change it again.
- natechols 7y ago
  I think it's fine for HN to use the same title that the BBC used - the problem is it's still a giant hand-waving oversimplification either way. It shouldn't be the moderator's job to clarify sloppy science journalism.
  - dang 7y ago
    The HN guidelines say: Please use the original title, unless it is misleading or linkbait. (https://news.ycombinator.com/newsguidelines.html https://news.ycombinator.com/newsguidelines.html) That rule serves HN well. People come here to get relief from the world of linkbait, and people who want such relief tend to be the kind of users HN benefits from. Giant hand-waving oversimplifications are certainly misleading-or-linkbait and usually both. So the rule means we should change it. It's just a question of what to change it to. Generally there's a subtitle or heading or photo caption or first sentence that says what the article is actually about—especially in major media, where the headlines are written by specialists who have nothing to do with the article. But I couldn't find any representative phrase in the article itself this time.
    - natechols 7y ago
      I'm trying to think of a better title and am blanking right now, but it's the "-created" that annoys me - it implies that they fed a bunch of data into a black box and a molecule popped out ready for human testing. What really happened, according to the BBC article, is they enhanced a standard virtual screening pipeline with AI. This is a totally useful and legitimate thing to do and I'm sure it has some cost savings, but they're hand-waving past all of the human labor that is unavoidable in drug discovery.

dekhn 7y ago

this is just another example of ML/bio hype.

allovernow 7y ago

There is an unfortunate lack of technical information in this article, but before the ML naysayers arrive in full force to point out that this isn't real AI or ML or what have you, it's quite possible they were using deep neural nets to heuristically approximate complex simulations 3-6 orders of magnitude more quickly than typical simulation. We're doing the same in other domains now.

natechols 7y ago
Simulations are not the major bottleneck in drug development. More generally, the naysayers in this case are likely to be people who actually have some knowledge of drug development, and have seen countless companies that promised to revolutionize drug discovery by using computers, most of which are no longer in existence. If an AI-powered company can suddenly start passing Phase III clinical trials at an unprecedented rate, then I'll be genuinely impressed.
ashleyn 7y ago
>We're doing the same in other domains now. One of the first examples I remember reading about this was an antenna designed by an AI at NASA[1]. This was back in the days when "AI" wasn't the buzzword so much as "genetic algorithms" was. It ended up looking like this crazy zigzag thing. 1) https://www.nasa.gov/centers/ames/research/exploringtheuniverse/borg.html https://www.nasa.gov/centers/ames/research/exploringtheunive...
jcranmer 7y ago
Drug development roughly consists of the following steps: 1. Figure out what enzyme or receptor you have to mess with to cause the disease. You can skip this step if you decide just to blast away in a phenotypic screen. 2. Design a molecule that will actually mess with the enzyme/receptor to prevent it from malfunctioning in whatever way is causing the disease. 3. Figure out how to actually synthesize said molecule. 4. Now make sure this molecule can actually get to the sites where it needs to go to hit the target... 5. ... that it lasts there long enough and in enough potency to do its job ... 6. ... that it doesn't screw anything else up along the way (or get converted by your body into something that does)... 7. ... and that there's no compensatory mechanism that still causes the disease after your drug successfully gummed up what it was supposed to. Or maybe you failed at step 1 and you're barking up the wrong tree. Roughly speaking, the last four steps will correspond to the different trials you have to run to get your drug approved. About 90% of drug candidates that make it to step 4 fail to make it all the way through step 7. The difficult things that kill drugs are understanding their toxicity profiles and other off-target effects. We suck at this because we have so little data (and the effects are quite complicated), and modern AI techniques are mostly borne on shoveling piles of data and hoping for the best--not a good match.