[HN Gopher] AI and Drug Discovery: Attacking the Right Problems
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AI and Drug Discovery: Attacking the Right Problems
Author : mhb
Score : 71 points
Date : 2021-03-20 13:23 UTC (9 hours ago)
(HTM) web link (blogs.sciencemag.org)
(TXT) w3m dump (blogs.sciencemag.org)
| text70 wrote:
| The last time I was working with molecular modeling, proteins
| were still the main target. With the advent of cryoEM,
| glycobiology of proteins can now be discerned. Now, AI is
| starting to be able to predict entire secondary structures from
| sequence alone. It will take massive computational power, but I
| can see how toxicology may soon be able to be predicted from in
| silico experiments.
| ramraj07 wrote:
| The point of this post (and what I realised during my PhD) was
| that for the most part this doesn't matter; choice of targets
| and predicting the human efficacy of a therapy that works in a
| tube or mice is far more important and typically more ignored.
| People don't focus on that because it's a hard problem
| especially for math and computer people who think their absence
| is the reason cancer isn't cured yet and come over to try and
| win the Nobel prize (and don't; cue relevant xkcd).
| 1cvmask wrote:
| AI is just a tool like Excel and not a panacea. It might create
| some efficiencies but it is not a short cut to the real grind and
| the too often overlooked serendipity involved.
|
| Serendipity plays such a big role in many "discoveries" but we
| don't want to ascribe too much to it because it is just potluck
| by brute forcing OTHER problems.
|
| Perhaps serendipity should be rephrased as "no method to our
| madness".
| randcraw wrote:
| Unlike Excel, AI truly can deliver a competitive advantage when
| used judiciously. In the right situation, it can deliver that
| small catalytic boost that lets you see just a little farther
| than before. And incremental advancement is the basis for
| almost all scientific or commercial advancement.
|
| That said, deep CNNs and LSTMs have delivered only a fairly
| narrow set of catalytic advancements to date, even fewer of
| which have changed the status quo in big businesses, where R&D
| ROI really adds up. Accurately identifying the next use of AI
| that will meaningfully move the needle is beyond hard.
|
| But hasn't that always been true of all forms of innovation?
| That's why so many are keen on AI today; as hard as it is to
| wield, it still seems to be the sharpest arrow in our quiver.
| dumb1224 wrote:
| I agree with you. I transited from computing to drug
| discovery. A lot of projects attempt to harness the power of
| ML but there still seem to be a lot of misunderstanding. A
| lot of the time the valuable ML methods are supporting /
| assisting hypothesis generation rather than 'doing it all'.
| The domain experts will judge the value of ML methods by
| their usual merits in the drug discovery world. If the more
| sophisticated methods can predict hit compounds no better
| than the traditional standard deviation based cut off, or if
| the models can not explain key insight of any indication of
| mechanism of action, then it may not be a good method after
| all. But meanwhile if it opens up a new way (e.g a new
| phenotype based screening) then it is worth pursuing.
| ta988 wrote:
| An advice for the VC among us. Don't get fooled by all these
| startups promising to revolutionalize drug-discovery with AI. Go
| talk to scientists that are specialists but have no stakes in
| those approaches. I've seen so much money diverted from projects
| (many that turned out to work once sanity came back) just to pay
| for some over-confident startupers shitty solution... Yes AI will
| help drug discovery no doubt about that, but those companies
| telling you they can learn from existing data are lying, most
| data available is of really low quality, acquired in conditions
| adequate only for specific types of questions... One I know
| closely will promise you a knowledge graph based on a specific
| slice of drug sources that can supposedly answer every question.
| It will almost certainly end up like many of those companies that
| hire humans to do the work in the back and sell it to you as
| AI... And their source data which I know well is of really low
| quality so we can't expect much more from their models either...
| varispeed wrote:
| Problem is that the big corporations and execs pay far less tax
| than small to medium corporations and workers. They have
| extremely distorted view of money and at some point it feels
| they grow money effortlessly while it is a month to month
| struggle for workers. This feeling of ease makes them spend
| money on a whim because it will grow anyway most of the time
| and if it won't? Oh well there is plenty of billions in the
| coffers. I think once organisations that are supposed to fight
| corruption pull their heads out of their and start making
| politicians accountable and make them plug the tax holes. When
| those big corporations get a dose of reality, how much effort
| it costs small business to earn that money, they'll spend it
| more carefully, but more importantly because of increased tax
| take it will be possible to lower the tax burden for everyone
| and level the playing field.
| [deleted]
| ramraj07 wrote:
| A lot of times they are not lying, they are genuinely clueless.
| Part of the problem with academia is that they follow the edict
| set by George Constanza: "the best liar truly believes in their
| own lie." So often they truly believe that their field's method
| of choice is how we will use AIDS to cure cancer, because the
| grants-system ensures that anyone who doesn't truly believe in
| their vomit won't get their grant approved (you need to write
| them with conviction, not doubt, if you want them awarded).
| Then mediocre postdocs with an "entrepreneurial flair" decide
| they're ready to take their nanobots and cure breast cancer and
| come to SV to pitch to VCs who are either clueless or are being
| advised by the same heads that are pouring the Kool-aid in
| academia to begin with!
|
| Exceptions exist of course, and some of these bets turn good
| purely because of luck (if you take twenty random chemicals and
| inject, one of them might just cure cancer; incidentally how
| stuff like paclitaxel & vinblastine was discovered anyway). But
| this only seems to reinforce the conviction of these
| institutions to continue their methods.
|
| One of the most important differences I feel VCs should
| acknowledge as a contrast between tech and biotech: with tech
| you can often choose one method of solving a problem and make
| it work. With biology however, nature will eventually give you
| a roundhouse kick if you try to force anything but an actual
| solution down it's throat.
| sjg007 wrote:
| >Exceptions exist of course, and some of these bets turn good
| purely because of luck (if you take twenty random chemicals
| and inject, one of them might just cure cancer; incidentally
| how stuff like paclitaxel & vinblastine was discovered
| anyway).
|
| Vinblastine was discovered from folk tea remedies for
| diabetes. In animal models they saw decreased white blood
| cell counts.
|
| Paclitaxel was discovered from the NCI plant screening
| program. The chemical was observed to be cytotoxic in
| screening studies.
|
| So both of your examples have pre-clinical evidence. These
| were not randomly injected and developed through scientific
| research and the clinical trial program.
|
| https://en.wikipedia.org/wiki/History_of_cancer_chemotherapy
| ramraj07 wrote:
| Vinblastine was discovered when they were testing a folk
| remedy for diabetes and found cytotoxic effects so it was
| completely tangential to the study's goals, and in modern
| parlance, an instance of a "task failed successfully."
|
| Paclitaxel was discovered literally from a random spray and
| pray scan of plant samples from every taxonomic class, here
| I will quote a literal sentence from a review: " Barclay
| collected T. brevifolia, an evergreen, apparently because
| the strategy was to collect at random. Not much was known
| about the tree. It belonged to the genus Taxus..." (https:/
| /www.acs.org/content/acs/en/education/whatischemistry... )
|
| So I still believe in what I said, which is random screens
| or complete serendipity often has been the best ways to get
| t a lot of our therapies, though I'm not advocating for
| more of that necessarily.
| sjg007 wrote:
| Where you say random I think you mean systematic.
| ta988 wrote:
| You are right I assigned to malice what is probably just lack
| of skills and knowledge. Your are also right on the fact that
| what works is grind and find. You can do it at the scale of
| one lab or the scale of many labs, but in the end what works
| is to try a lot of things because we are still really
| shooting in the dark for a lot of targets or diseases we
| don't know any druggable target yet.
| ramraj07 wrote:
| I'll propose that we actually kind of know a lot of things
| about many (not all) diseases, we really just need an
| objective look at how to address them. The majority of
| scientists today are mostly quite narrow minded in terms of
| scientific thinking, every one of them wants to use the
| method that they are experts at to solve the problem. When
| the Manhattan project was deployed all the top physicists
| came and tackled the problem that needed to be solved with
| the most suitable methods, but there's been "cancers
| Manhattan project" or "moonshot" initiatives in biology
| numerous times and they all fail because the scientists
| incharge invariably push their own agendas in these.
|
| Take Janelia farms for example- without reading their
| editorialised website if you try to gander what the hell
| this institution is trying to do, from just the papers they
| publish, you would fail miserably. Not saying that
| institution sucks, well I suppose I am saying that, but
| only insofar because all of academia seems to suck in the
| same way.
| oivey wrote:
| It seems kind of odd to blame academia for this. Almost all
| founders are clueless in all fields, and, additionally,
| they're pretty much supposed to be aggressive about their
| idea in order to get investments. The role of the VC is to
| invest wisely. If they're less knowledgeable about tech
| outside of the niche of computers and they lose money on
| biotech, that's their fault.
| ramraj07 wrote:
| I suppose one of the points I was trying to make is that in
| tech it's probably okay (maybe even beneficial) to be
| clueless, but that's not true for biology or hard science
| tech - if you float a rocket company, stuff has to fly, and
| you better get some rocket scientists in there. One can't
| just Travis Kalanick their way to Mars because you're not
| fighting some cab unions you're fighting the laws of
| physics.
| ta988 wrote:
| I know less about founders in computer tech but yes, many
| founders in biotechs that I know have just had a few years
| of experience in labs, not really successful
| scientifically, even if they know how to market it. And
| thats far from enough to really understand the nooks and
| crannies of drug discovery projects. Especially since most
| academic labs are only doing a really thin slice of the
| whole process. There is a reason why you dont give millions
| of dollars to a postdoc that submit their first grant
| proposal, why we do that to postdocs that are founding
| biotechs is beyond my understanding. The products I've seen
| from companies that raised $5mil are not worth 1/10 of
| that, and thats just in dev costs, their stuff could not
| even scale so they are trying to survive until the big prey
| comes and swallow them before anybody notices its all smoke
| and mirrors.
| dnautics wrote:
| > There is a reason why you dont give millions of dollars
| to a postdoc that submit their first grant proposal, why
| we do that to postdocs that are founding biotechs is
| beyond my understanding
|
| TINA. If you're a fund that needs to put an ass in a seat
| to say "we have a biotech investment", you go to whatever
| you can find. You have no way of doing due diligence in
| your portfolio and even if you hired someone like me to
| ultrafilter your proposals you'd probably come up empty,
| because the only postdocs that play this game are those
| that know they are good at parlaying their social
| capital, obtaining favorable reviews from their PIs
| (social validation), and have less scruples.
|
| The heads down brilliant and honest scientist postdocs
| have already burnt out, and often aren't socially savvy
| enough to play the VC game, if they were, they'd be
| gunning for faculty positions instead.
|
| If VC wants a better return they need to have a different
| heuristic. I'd pick postdocs that did years of grueling
| work with no results and ask them what they'd really
| prefer working on instead of their PI's hairbraned idea.
| oivey wrote:
| Generally that's the trajectory computer tech startups
| take too, except with even less experience. That's
| probably a reflection of the difference in the barriers
| of entry.
| Judgmentality wrote:
| > An advice for the VC among us.
|
| I wish I could find it now, but I once read a comment on HN
| where someone was approached by a VC for technical advice. To
| make a long story short, the engineer being consulted noted
| that in order for their growth to continue as the company
| proclaimed, they would have to break the speed of light before
| reaching profitability. The VC invested anyway and inevitably
| the company failed.
|
| VCs chase trends just as much as anyone. In my experience, they
| are _more_ susceptible to herd mentality than regular people.
| rscho wrote:
| If you want to see people who understand the why and the how:
|
| https://www.uab.edu/medicine/pmi/
|
| Lead by Matt Might and Will Byrd. As an MD, I must say MediKanren
| is the only convincing use of medical AI I've seen.
| srckinase123 wrote:
| Quick tangential question: Do you see a benefit in incoming
| medical students knowing programming? If so, which specialty do
| you see it benefiting from the most (e.g., radiology)?
| bionhoward wrote:
| One hundred percent! I did this, and I'm glad I did.
| Programming changes your perspective. When you learn to code
| you learn to value abstraction. That's valuable in medicine
| because medicine is pathologically reductionist...
| arolihas wrote:
| Matt Might is the real deal.
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