51–60 of 93 posts
Here is the code repository for the model 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 :).
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> 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 lo…
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 met…
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?
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…
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.
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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
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.
If that isn't going to drive resistance to that particular antibiotic, I don't know what will.
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 profic…
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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, generat…
> 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 lo…
Industrial-scale sample processing -> putting more people a little farther down the pipeline to actually look at what's interesting rather than doing unnecessary/low-skill field work.
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 different…
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 s…
That doesn't sound like luck.
Thank goodness it was "aided by machine learning". Otherwise, would anybody be reading this article ;)?
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.
> 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…
> 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…
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.