Comment by gumby
10 hours ago
Coming up with candidate drugs is the easiest part of the process. It’s possible that that could be improved a little (not sped up, but suggesting compounds in silico less likely to fail) and possibly have suggestions for scale up, but those are all hard, and the latter is less amenable to being “read” during trains phase.
This has been one of the target AI applications since the 1960s.
(Former drug developer who will never touch the life sciences again)
Interesting. In TFA they don't focus on that, they say:
"Developing a drug involves many varied tasks: doing early research to identify a target for a drug and the right type of drug to engage it, designing a molecule with the right properties, optimizing it to be manufactured reliably at scale, drafting the regulatory filings needed to run clinical trials, designing and administering those trials, and much more.
Our strategy to realize AI’s medical benefits is based on having AI increasingly involved in all of these tasks."
That's all true, but they don't mention the relative costs of those different tasks, or how AI helps them.
Optimizing molecular properties of a lead is important, but ultimately not a huge source of costs. In any case, LLMs probably have as much chance of predicting outcomes as anyone else (i.e. very small).
Manufacturing is somewhat expensive, but most of the cost sits in actually creating a production process, which is not a thing LLMs can do.
Clinical trials are where most of the costs lie, but unless you can create a magical classifier that can tell me if my drug will make it past phase 3, you're not significantly bending the cost curve.
Calling these "machines" LLMs seems to sell the process short. Using the same style of attention in a different domain is very valuable. An LLM wasn't what created a video of a panda on the moon. I am not remotely an expert, just a software person with a lot of interest over the years.
My reading of the hypothesis is this, interested in your thoughts:
> unless you can create a magical classifier
A generator could concentrate on pockets while remaining unable to judge most drugs. And LLM RLVR a) works well, and b) is black box optimisation and so is suited since it doesn't need system identification (which you can't do for complex biological systems). So the hypothesis is that, yes making a classifier is very very hard, but you could through some (lots?) initial interactive data, end up with our generator outputting with high probability drugs-that-end-up-passing-trials-with-probability-p. Whether these drugs thus generated are drugs we care about (i.e, whether `p` is good enough) and whether the whole pocket it generates in is large enough, and how jagged this behaviour is (i.e distribution of `p` across taxonomies of drugs), are the few questions we want answered.
1 reply →
Administering phase 3 drug trials is the most expensive part of the process by far. They conveniently put that at the end.
They put it at the end because they list the parts chronologically...
1 reply →
It would be pretty reckless to do it any other way. That is, to evaluate whether it makes sense to distribute as a product without having any reason to believe that it is safe for humans and has any effect at all.
That's my understanding as well. Seems like they're improving a $20 part of a billion dollar process, and a part that was already heavily using state of the art AI/simulation at that. The computers already spit out more potential candidates than what can be reasonably tested in vitro.
> Seems like they're improving a $20 part of a billion dollar process, and a part that was already heavily using state of the art AI/simulation at that. The computers already spit out more potential candidates than what can be reasonably tested in vitro.
This is the fundamental problem with nearly all tech-meets-drugs startups. They almost always focus on the part of the problem where there isn't a bottleneck, because the parts where there is a bottleneck are the parts we don't understand at all.
> This is the fundamental problem with nearly all tech-meets-drugs startups. They almost always focus on the part of the problem where there isn't a bottleneck, because the parts where there is a bottleneck are the parts we don't understand at all.
Are they doing the thing where they develop the technology first, then hunt around for problems for it to solve and hype up potential applications?
1 reply →
I don’t see the issue really though, if the $20 part still produces something more likely to get through the billion dollar parts what’s the problem ?
5 replies →
Indeed. Just think about how much people meme here "in mice" when stuff gets posted. There's a very long process between computational screening and getting an actual drug into actual humans.
What is the hard part then? My read from here is that it is testing drugs on humans, or rather getting the approvals needed to begin human trials. Is this understanding correct?
yes. you have to recruit a diverse and representative population, which is very difficult and time-consuming. then, you have to spend several years conducting the trial. most of the time, the drug either has no effect or is actively harmful. 90% of drugs that go to clinical trials fail!
Is simulating the effects on humans not an option? Or is it that simulation technologies are not good (accurate) enough?
Thank you for entertaining my curiosity! I have been meaning to educate myself on this but don't know anybody in my circle who is knowledgeable in this field.
1 reply →
What turned you off from life sciences? I've been investigating moving into it and would be interested to hear your experience.