
Algorithmically-created medicine to be used on humans for first time - davidfoster
https://www.bbc.co.uk/news/technology-51315462
======
natechols
Obligatory rebuttal from someone who actually knows about drug development:
[https://blogs.sciencemag.org/pipeline/archives/2020/01/31/an...](https://blogs.sciencemag.org/pipeline/archives/2020/01/31/another-
ai-generated-drug)

~~~
dang
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
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
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
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.

~~~
derefr
Feels like the whole article is buying the lede, then. The news story is
really something like "AI accelerates drug discovery pipeline—new drugs will
now come faster." The fact that one such drug is in human trials is kind of
irrelevant.

~~~
natechols
Yes, and the more general form of this ("computers accelerate drug discovery
pipeline") has been appearing almost as long as I've been alive.

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yters
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???

~~~
__s
We were doomed as soon as we made the mistake of trusting an AI to prove the
four color theorem

~~~
ska
There was nothing AI about the original four color proof. Are you thinking of
something more recent?

~~~
dekhn
In the future AI will be the term people use to talk about all of CS,
regardless of it being artificial, or intelligent.

~~~
ska
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.

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dekhn
this is just another example of ML/bio hype.

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allovernow
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.

~~~
jcranmer
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.

~~~
natechols
One thing that depresses me about this entire field is that there are probably
enough data points buried in all of the various Big Pharma databases for AI to
come up with some truly novel and useful insights. Even though most of the
data will be for drugs that failed, however, there's still zero incentive for
the companies to share the data. In general I'm extremely skeptical of claims
that central planning will be more efficient than capitalism, but this is one
case where the competition seems actively harmful.

~~~
dekhn
I've worked with big pharma databases. Nearly all of them are inappropriate
for the techniques used at large internet companies (deep networks trained to
predict user behavior to maximize some objective). Pharma is slowly coming to
the understanding that they _could_ use their resources to produce high
quality datasets that are amenable to machine learning, but it's a slow
process and it always has to fight against the status quo.

