
Scientists use ML to find an antibiotic able to kill superbugs in mice - adventured
https://www.statnews.com/2020/02/20/machine-learning-finds-novel-antibiotic-able-to-kill-superbugs/
======
entee
This paper shows what can be done when you carefully run an ML program
alongside a wet lab experimental program tailored to feed back into the ML
program. The results end up far more interesting than some recent "ML aided
drug discovery" papers in that they actually discovered a drug that functions
very differently than known antibiotics (AB).

Even though the structures that came out look AB-like, they work differently
than known ABs, probably by disrupting the pH gradient across the cell
membrane. Other ABs might do the same as part of their activity, but work
better under different conditions than this one. The result is an innovative
structure, and a molecule that can hit resistant strains.

Combining ML and wet lab is the real way we'll get new drugs. You need to
regularly check in with a high content ground truth or you'll come up with
either uninteresting or useless results. I'm a bit biased though, that's what
we do at my company ;)

~~~
xzel
People have been doing this exact thing for two decades at least but obviously
with less computing power. There's literally nothing new about the idea. The
real trick is being incredibly lucky and finding something that actually works
in humans after multiple trials. I'm sure you know this based on your comment
and this isn't really directed at you (truly wish you best of luck, I really
hope the computing power and skill we have saves lives) but I absolutely hate
these types of articles, which I've seen becoming more frequent the past year.
AI + scientific challenge + possibility = puff article. This is the type of
semi-hysterical reporting I expect from a local news station.

~~~
redsymbol
> There's literally nothing new about the idea. The real trick is being
> incredibly lucky and finding something that actually works in humans after
> multiple trials.

I guess I'm not sure where the dismissiveness is coming from here. Are
claiming this could have been trivially done before? If so, why didn't you or
someone else do it already?

Or are you claiming it's an uninteresting result that is not worthy of
publication or attention?

~~~
xzel
I'm saying I'm tired of the puff piece articles and reddit style headlines. I
was saying NN's and other "AI" style models has been used in this way for
decades for these types of things. 0 disrespect to the actual science; the
paper is great and I truly hope it works in humans. We need more drugs against
the inevitable fight against drug resistance.

~~~
redsymbol
Okay, fine... what do you want to be different about the situation, then?

Do you not want any non-technical summary articles like this to be written? So
that only those with the training to understand a Cell journal article would
be able to learn anything about the result?

Or do you prefer that no journal articles be published that rely on 2020-era
NN models, because older articles based on less state-of-the-art NNs have been
published already?

~~~
xzel
I'd much prefer there be more detail in the article about AI research in drug
discovery, how it still needs clinical trials in humans. I agree with the sub-
comment as well. I think your last question doesn't make sense to me though as
I haven't said anything similar to that.

But I think you raise an interesting point about non-technical summary
articles: what do they do and who are they for? Does the non-technical public
need to know about this research? Do they gain anything vs. reading the
study's actual summary? I'm not sure. I do think there isn't much for non-
technical people to get from this article that would be useful. I think the
best reason would be for younger people to pique their interest in the field.
Though, I honestly don't know the answer.

~~~
redsymbol
Yeah, good points. I guess such non-tech articles serve several purposes.

I do think this one gives non-technical people get something useful, though.
More broadly, it'll increase science understanding among the non-
scientist/non-technical public, at least on this topic, and good does tend to
come out of that.

------
thedance
It's interesting here that the breakthrough seems to come from the computer
search not suffering from epistemological block. It doesn't think, therefore
it is also not bound by conventional patterns of thought, such as the bias a
researcher may show when thinking that this known compound is not an
antibiotic, when it is in fact an antibiotic compound.

------
koolba
> That is an especially pressing challenge in the development of new
> antibiotics, because a lack of economic incentives has caused pharmaceutical
> companies to pull back from the search for badly needed treatments. Each
> year in the U.S., drug-resistant bacteria and fungi cause more than 2.8
> million infections and 35,000 deaths, with more than a third of fatalities
> attributable to C. diff, according to the the Centers for Disease Control
> and Prevention.

How big does the market have to be to commercially viable for research and
development? Nearly 3M potential patients at a couple hundred dollars per
course is nearing a $1B/year.

~~~
kirrent
At least one disincentive is that if you do find an amazing new antibiotic
effective against certain strains of antibiotic resistant bacteria, antibiotic
stewardship means the medical community will try and use it only where
necessary to slow any adaptation to the new drug. That makes your potential
patient population much smaller.

~~~
whatshisface
Here's an interesting thought. Let's say that the number of possible
antibiotics that can be discovered is so high that bacteria cannot
simultaneously be resistant to all of them (plant immune systems, which
involve a lot of small moleculea, indicate that this may be possible). Then,
this game theory trap where stewards are guarding what we have so that pharma
doesn't want to make anything new is both pointless _and_ self-perpeuating,
because we will never reach the point where we realize we've beaten
resistance.

------
tjchear
I'm not an expert in ML/Biology, but I wonder if doing so won't completely
eliminate all of humankind's diseases, but shift the battle from one between
humans and bacteria/viruses, to one where ML takes the place of humans by
proxy (say we let this ML vs superbugs play out over centuries). I wonder to
what direction evolutionary pressure in the face of ML would take bacteria.

Perhaps a super smart bug.

~~~
Rochus
Molecular biology is not that easy. Selecting a promising molecule is only a
small fraction of the work. If you know the molecule you still have to be able
to replicate and bring it to the place where it is required. So even if AI can
support the process still a lot of plain old biochemistry is needed. AI is
just one of many tools. And as you can read in the article a lot of trials are
required by the regulatory authorities before the drug can be applied to
humans.

~~~
antirez
True but it's interesting that once you have an oracle that given a molecule
provides you with an antibiotic score, you can show it directly molecules that
are easy to produce and or likely safe for humans. This should help
significantly.

~~~
Rochus
It will not go without replication, trials and production, regardless what
oracle you have. And keep in mind that the oracle has a quite significant
error rate. You won't get an immediate solution, just proposals. And whether
or how a drug works is also dependent on the environment and there are dynamic
aspects and complex processes within a cell extending over space and time.
It's much more complex than speech recognition or self-driving cars.

~~~
tjchear
We speak in hypotheticals of course, but barring any physics defying barriers
here, one can imagine a future where those issues you raised have been
addressed (e.g improved simulation/ML techniques, less red tape, faster
trials, greater accessibility to treatment). Even now, I think mankind's
immune defense is no longer confined to itself, but must be thought of as a
bigger system that extends to the pharmaceutical industrial complex. Any
sufficiently ambitious bacteria is basically taking on not just us, but also
the collective work of all researchers diligently working on the next super
bug killer.

~~~
Rochus
You are invited to try it. You will not be able to avoid the laws of nature,
and there will also be many legal hurdles. But if you make a big step forward,
you will undoubtedly be a Nobel Prize candidate.

------
loopasam
A medicinal chemist take on it:
[https://blogs.sciencemag.org/pipeline/archives/2020/02/20/ma...](https://blogs.sciencemag.org/pipeline/archives/2020/02/20/machine-
learning-for-antibiotics)

------
logifail
Decades ago when I worked in a lab, "high-throughput screening" was definitely
on the list of buzzwords.

Given what I saw back then, I'm struggling to understand how there could
possibly be "a library [..] of 6,111 molecules at various stages of
investigation for human diseases" (a.k.a. "Drug Repurposing Hub") which hasn't
already been partially or fully screened for interesting antibiotic activity.

Could it be there's more fame (and funding) in a project where you can publish
a paper and get headlines about "ML" and "superbugs", than in actually testing
a library of existing compounds to see if any of them kill E. coli?

~~~
anonsivalley652
For DD, it seems initial screening of as many phages, microbes and compounds
as possible using highly-automated brute force might be plenty efficient to
test their effectivenesses against every horrible, resistant and opportunistic
pathogen for candidate identification. Maybe flying drones out to collect
samples in as many random places (public places, restrooms, dirt and even more
random places) as possible, generating more samples than a team of humans ever
could.

There doesn't seem to be any "One True Way," but a holistic synthesis of
collection, identification and selection methods.

~~~
logifail
> highly-automated brute force might be plenty efficient

Hasn't this been going on in one form or another for many decades?

When I was in this field (20+ years ago) I got to visit labs at Glaxo
Wellcome, SmithKline Beecham, Zeneca and so on.

Even back then they were proudly showing off lab robots which allowed them to
run large-scale screening experiments.

Not sure any of this stuff is quite as revolutionary as it looks.

~~~
kokey
I did a stint at GSK about 13 years ago. The large scale mechanised
experiments also produced big databases of compounds and their properties and
mechanisms to make these accessible to researchers and build bigger clusters
of systems to run models on. There was a lot of talk of ML but implementation
was nowhere near the scale we see nowadays. I think what has changed
significantly is the scale, second to that the methods including algorithms
and methods to feed back real data into it. It's an evolutionary improvement
but I sense it's one of those things where one lucky step in the evolution
could make a big difference in productivity.

~~~
logifail
> one lucky step in the evolution could make a big difference in productivity

There's also the issue of ROI. Is Big Pharma really expecting to find an
antibiotic blockbuster drug?

20+ years ago there was a distinct lack of excitement around antibiotics in
general, at least from the commercial types.

Q: Is there more expectation/excitement/R&D budget now?

------
throwGuardian
What's becoming apparent is the fundamental nature of applied mathematics and
ML/AI/DL to our future.

Our education system needs to adapt, include this as a mandatory part of a
college education (BRIC countries are including this at the high school
level). A degree in pure AI/ML without mastery over impactful problems and
it's underlying science, is not the ideal future. Every chemist, biologist,
... should be proficient in ML/DL

~~~
mantap
The field of ML is moving too fast to be taught in schools. It's better to
teach its foundations (linear algebra, etc) which are well developed and
static.

------
antipaul
Thank goodness it was "aided by machine learning". Otherwise, would anybody be
reading this article ;)?

~~~
wyattpeak
We're techies, not doctors. It's the computational solutions that interest us,
only secondarily the underlying problems being solved.

Thankfully, it doesn't matter a whit whether or not a bunch of programmers
read about medical advancements on their lunch break.

~~~
kharak
You should realised that not everyone here identifies as techi. I for one care
about interesting problems and ideas and ignore most of the pure tech posts.
From other posts I've seen, there are people from quit different occupations
here.

------
tasubotadas
Here is the code repository for the model
[https://github.com/chemprop/chemprop](https://github.com/chemprop/chemprop) .
I am surprised by the (high) quality of the code published there. It is quite
a rare case in the academic world :).

~~~
phobar
They are funded by several big pharma companies (from which they have learned
a lot in terms of QSAR). One main point of the collaboration is the creation
of production ready toolkits.

------
dom96
I saw news about this on The Guardian and it was the first time in a while
where I was truly amazed by an ML-related discovery. This kind of thing is
incredibly exciting and I hope we continue to see similar discoveries in ever
more wider fields.

------
DeonPenny
Knew this happen eventually. ML drugs will be huge

------
allovernow
A lot of the more cynical commenters here are misunderstanding the
breakthroughs that have lead to the novel discovery, and underpin the emerging
ML revolution that we are just beginning to witness. Yes, partly the results
of this study are due to increased compute, but I'd say that's only about 50%
of the secret sauce. The other 50% is attributable to many very recent
developments in the field ML which are gradually coming together in solutions
to a range of problems - deep learning, convolutional networks, new
architectures (GAN, transformer, RNN, LSTM, autoencoders, etc), regularization
techniques, gradient control, hyperparameter optimization...the list is quite
long, and every SOTA neural network inevitably incorporates a large proportion
of these small steps towards the massive leaps that are being taken in the
applied ML space. The concepts of perceptrons and gradient descent may be 50+
years old, but an entire body of theory has been explosively developed in the
last decade, such that comparing modern ML to what was being done just a
decade ago is akin to the difference between, say, programming theory now and
100 years ago. And for the same reason - just as compute advances enabled
development of computer science, so to have recent hardware advances unlocked
a new level of machine learning.

But I'd like to point out that, even moreso than software development, very
little of the grand breakthroughs we will soon see will be possible without
multidisciplinary domain knowledge. It is very difficult to effectively apply
ML without a solid technical understanding of the properties of the applied
data space, which for real world applications are constrained by physical laws
and represented and communicated best by mathematical descriptions. ML
engineering is a generalist's game - and what we are going to find is that the
most successful ML engineers come from broadly applicable, math heavy
backgrounds - physics in particular, electrical engineering, to a lesser
degree mathematics, etc - because ultimately training a neural network comes
down to adequately sampling a problem space and curating data with an
intuition which is most ideally developed by the study of mathematics. It is a
very general view of the world which is difficult to communicate to someone
who is not experienced with higher math.

The current wave of applied ML startups will see a high rate of failure -
because ML is still being treated as an extension of programming, in the sense
that you expect to be able to hire a bunch of pure developers to translate a
specialist's knowledge into code. But this emerging field is different, the
few startups that succeed in the applied ML space will be those that are able
to find the rare domain experts who have picked up ML along with their math
and science experience. There will effectively emerge two classes of ML
engineers with substantially different levels of compensation - the coveted
generalists who have cross-pollinated with math heavy disciplines, and the
rest.

~~~
superpermutat0r
I think multidisciplinary knowledge is unnecessary, especially now with deep
learning where there is no feature vector design.

Even the DeepMind discoveries in various fields always feature the same people
that I doubt know deeply about protein folding or similar stuff.

Even when you look at computer vision research and how all the sophisticated
methods became unnecessary when NNs came to dominate shows the same thing.

I remember having to learn about dependency parsing, part-of-speech tagging,
named entity recognition, entity relationship inference, document
summarization and a bunch of sophisticated modelling. Combining all of that to
get to high level tasks like machine translation or question answering or even
summarization (some methods pruned the dependency tree to get a summarized
sentence) was difficult.

Look at transformers disrupting the NLP. There is no concept of dependency
tree, no need to do POS tagging, it's not even necessary to think about that
when making a machine translation system. People were figuring out how to
build better and faster dependency parsers, POS taggers etc. Domain knowledge
was massive and it became redundant with the advent of transformers.

------
DoreenMichele
_...in mice._

It would be nice if we spent as much time, money and attention on figuring out
prevention. Inadequate hygiene infrastructure (like toilets) in developing
areas is part of the problem here.

But addressing that isn't as exciting to people as finding a cure for a super
bug. If we really want to fix this, that needs to change.

~~~
throwGuardian
Super bugs are a mostly first world problem, rampant in top tier hospitals
providing cutting edge treatments, despite following safety precautions meant
to deter secondary bacterial infections during or post hospitalization

~~~
refurb
_Super bugs are a mostly first world problem_

Not true at all. There are several known resistant strains that have come out
of developing countries.

Why? Antibiotic use can be rampant - in many countries you can buy them
without a prescription.

~~~
mnw21cam
I heard someone (in the correct area of expertise) describing how he had
visited a developing country and tested the water in a river downstream of an
antibiotic manufacturing plant, and found the concentration of a particular
antibiotic to be approximately the same as what you would typically want to
achieve in the blood of a patient.

If that isn't going to drive resistance to that particular antibiotic, I don't
know what will.

~~~
perl4ever
People say things like this, but how many antibiotic compounds are in the soil
naturally? Living creatures exist in an environment with countless bacteria
and evolve to survive, and some of the compounds they produce are turned into
human drugs. So I feel like there's something missing in the ordinary
narrative about antibiotic resistance. Where did penicillin come from again?

