> “…the focus of AI in drug discovery must shift from doing what can be done - such as modelling data that is readily available, but that is unlikely to move the needle - to doing what should be done, even if this requires, for example, substantial data generation…” It’s a worthy goal, but I think that many involved in this work might be thinking, even unconsciously, “You first”.
This is the problem with AI for all of science - not just drug discovery. Applied ML has spread like wildfire through academia over the past decade - this started well before the LLM hype. It’s the perfect honey trap: research is painstaking and slow, ML offered a shortcut, and best of all, it just needs data. Research produces lots and lots of data! Surely this will be a match made in heaven.
I’ve watched the same pattern play out at least four or five times now in various roles.
(1) Propose an ML-guided approach to material/chemistry discovery/optimization.
(2) Gather existing data (real, experimental data).
(3) Realize there’s less than about 50 true rows of data on the outputs of interest.
At this point, you either:
(4a) revert to traditional methods but keep the veneer of using ML to save face, or
(4b) pivot to computational/simulation work or a high-throughput system that’s very far removed from your original problem, but allows you to keep playing with ML toys
It’s really bad. I left the industry. I don’t know how long it will take for people doing real science to take back the reins (and the funding).
Here is an article by Pat Walters on the usefulness of ML in drug discovery. This article is a response to another one making the case that utility of ML models are very limited in drug discovery
> (4a) revert to traditional methods but keep the veneer of using ML to save face
I haven't worked in the industry side of things but in academia everyone kind of agrees that gradient boosting trees are some of the best models to do these things.
The real value right now is in figuring out how to generate robust data cheaply and quickly. I'd wager that the effect of a good model on marginal data is small, but the effect of a marginal model on great data is probably quite large.
I worked on some of the very best funded plant research out there. When it comes down to it, there's enough variation caused by confounding factors, and it takes so long to capture more data, that almost everything anyone tries cannot be called a success or a failure for years, because the individual measurements for one small plot of land somewhere just don't mean anything. Once you do an entire experiment for a season, which takes months, and you grab the little noisy data you have, and turn it into real rows, we were down to very little.
You can do more tests on smaller things, like checking if some protein will kill some cells of a pest, but making sure a plant produces it enough that it actually does something significant to the real, live pests, that it's not toxic, and it doesn't harm the plant's yield massively (as it's now spending time producing your pesticide) is still going to take years. We might be able to fold proteins, but the kind of things we'd need to really simulate plant biology well enough to not need years of failures are still very far away.
And it's far worse in medicine, as with plants at least nobody has ethical concerns if they fail and die, and nobody needs to get consent from a corn seed. Getting to 50 actual data points from many medical studies is already a lot of effort. And imagine when it's a long term study, and you need to follow patients for 30 years, as theym move, or die, or decide to stop participating, or who knows what.
In chemicals and materials, 50 rows of good data is a really solid study. That’s e.g. a 3x4x4 experimental design (assuming replicates for each condition get averaged into a single row). If you managed to prep that many samples correctly and obtain consistent characterization data across all properties of interest, you’ve easily got a paper. It’s also kind of malpractice to jam this type of data (few samples, wide rows) into modern ML models. There are plenty of simpler statistical methods that will tell you what’s going on, and even then a well-made plot might be good enough. The difficulty is not in drawing insight from the final numbers, it’s almost always in how those numbers came to be in the first place.
Thus the reticence of science-oriented companies to invest heavily in these mass data-gathering exercises to feed ML. It’s damn expensive, and almost always leads you back to raw data issues, not breakthrough discovery. Doing it without a set purpose in mind is even more likely to yield garbage.
I'm a structural biologist at a mid-sized biotech. I use AI tools daily. They make accomplishing the same things I was able to accomplish before quite a lot faster and easier. They don't help me magically accomplish new things that I couldn't previously.
For example, it helps me install academic software, debug things. It helps me take a large dataset and write scripts to ask questions. It helps me go through experiment drafts to see if I'm missing things. It helps me remember obscure formulas I use every 6 months. It has not, at least in my experience, come up with anything truly novel.
A concrete example: AlphaFold is great...to come up with a starting model for a chimeric fusion or something. What would have taken me 1-2 hours fumbling around in PDB or CIF files is now a quick prompt.
Yes, and it's relatively good for on-rails data collection and data processing pipelines that would have previously needed occasional human intervention. Especially now that I can run something like DeepSeek v4 Flash on a couple of RTX 6000s and just script an API to hammer away without having to worry about racking up a huge bill.
In some ways, but it's tough to say if that's my ADHD or not. It's far too easy to leave one branch of reasoning now and jump to another whenever progress gets difficult.
Though in some areas where I can sustain interest, AI is helping me go deeper. For instance, I've been putting myself to sleep at night by just asking it questions about expectation maximization and Bayesian statistics. This has seriously boosted my understanding of cryo-EM alignment algorithms in a way I couldn't do in grad school because there was no professor that understood enough to help me when I got stuck reading literature.
> In some ways, but it's tough to say if that's my ADHD or not. It's far too easy to leave one branch of reasoning now and jump to another whenever progress gets difficult.
I have a co-worker who doesnt feel like ADHD helps him because he sits down and just starts... doing work and typing. Assign him a complex task, he will just start on it. Mind blowing he does this day in and day out. an absolute machine.
C) not smart enough to be a self-taught bio-hacker
Everyone hears "AI is going to cure disease" and pictures some cure-all pill from a bio lab which is what I feel this paper is hinting at is missingb but that's the top of the funnel; I'm at the bottom where patients live and that is where AI is already quietly working. Its just not being benchmarked.
I built https://crohns.ai. I set out to make an AI-native clinical-trial manager with a feedback loop (DDP) and ended up somewhere completely different: instead of chasing a new "drug" which is totally out of my grasp; financially, intellectually etc... I used it to codify a care protocol that helped me avoid a flare after I got laid off, lost my insurance, and lost access to Skyrizi.
If given the chance, I might go back on it because my protocol can be a little strict at times but either way I do see a significant shift to tools like this given the state of the US Healthcare system.
Derek Lowe as a science communicator, and others like him, is sorely needed to understand the real meaning and significance of the study and others. I say that as someone with a PhD in chemistry who's been to plenty of presentations on drug discovery topics.
It's difficult to calibrate statements made by other scientists unless you're well embedded within a field: Is this someone whose opinions matter? Are they the subject matter expert they make themselves out to be? Is this research itself truly impactful? Is it really 5 years until it will be realized outside of academic labs? Etc...
It's difficult to decipher questions around credibility because they rely on real-world interactions and associations that extend beyond the physical tokens of paper counts, publication venues, citations, and author lists that typically lag behind the front of human knowledge which is generated from real-world interactions. It can be simple things, like the insightful question a grad student, with minimal publication history, asks in a seminar.
Of course, the paywall is also unhelpful too, but a good, brief commentary by an appropriate commentator is a better link for 99% of prospective readers compared to most "peer reviewed" (scare quotes because that's a real question nowadays) articles.
Not that I'm a Derek Lowe fanboy or anything but the entirety of your comment is like "that guy needs to check himself" without, like, any specific context, any specific argument he's wrong on this specific question or like anything. It's like "deciphering questions around credibility" is hard ... all the way down. Where's yours? What are you saying?
Isn't the comment in fact praising Derek Lowe as a science communicator? In the second paragraph OP is just posing the questions that one might have when reading about a field not your own, that highlight the importance of reliable science communicators.
It's a very confusing comment since Derek Lowe is a chemist working in pharma but is being referred to only as a science communicator which I suspect most people would consider to be an implicit insult.
I think it says more about the person reading it as an implicit insult than it does OP. I am, I think, a fairly competent scientist, and know many great scientists. I know very few great scientific communicators.
OP here: the title of the thread still links to Derek Lowe's blog post, but the article discussed in the blog post was added to the body of the original post (not by me).
Obviously the missing part (which we already have for software and math) is that we need agents to be able to run automated loops in the real world. That basically requires robots. I think we'll be there in less than 5 years.
there is a AI designed drug for hair loss that i know of.
its slow-release oral minoxidil formulation called MINX. AI helped with the formulation [1].
its in in similar category as VDPHL01. Hundreds of millions if not a billion dollars has been invested into Veradermics, and their main product is VDPHL01 (also an extended-release oral formulation).
I’ve taken low dose oral min for a few years and it’s systemic, meaning it’ll make all your hair grow. I now have body hair where before it was never noticeable. “I’m hairy like monkey” as my kids say.
For everyone struggling with hair loss but concerned about side effects of Fin. Look into topical fin. It produces higher concentrations of the medication in the scalp and lower systemic concentration. Not perfect, but better.
if you think the state of the art in this area is something you'll hear about from a guy that looks like santa in an academic journal, your investments deserve what's about to happen to them.
The lack of comparable data and testability really does seem to be a challenge. I wonder if people would be more willing to collect and share lots of health data if the collecting company was a non-profit dedicated to anonymizing it.
> "The paper goes on to make recommendations for AI companies and investigators, and these are well worth reading. The common theme is that people need to think more about why they’re doing certain techniques or using certain technologies, rather than just using them because they’re newly available."
Please. Please let some people with power and influence understand this lesson sooner rather than later. I understand the reasons that's unlikely to occur, but usually the impact isn't quite so drastic and expensive as this is. Just because something is new and shiny doesn't mean that it'll produce the outcomes you need at the other end, and until it's shown that capability your approach to it should be MODERATE.
Drug discovery scientists think about what they're doing and why ALL THE TIME. AI stuff is just another tool.
It's also worth mentioning that drug development timelines typically exceed the interval in which these technologies have been available (or at least effective). Measuring impact will take a long time.
nerd-fanboi proposal: Articles by national treasures (like Derek Lowe, Raymond Chen) should be highlighted with specific identifiers on HN - like a distinctive title font or an ascii diamond ◊.
> “…the focus of AI in drug discovery must shift from doing what can be done - such as modelling data that is readily available, but that is unlikely to move the needle - to doing what should be done, even if this requires, for example, substantial data generation…” It’s a worthy goal, but I think that many involved in this work might be thinking, even unconsciously, “You first”.
This is the problem with AI for all of science - not just drug discovery. Applied ML has spread like wildfire through academia over the past decade - this started well before the LLM hype. It’s the perfect honey trap: research is painstaking and slow, ML offered a shortcut, and best of all, it just needs data. Research produces lots and lots of data! Surely this will be a match made in heaven.
I’ve watched the same pattern play out at least four or five times now in various roles.
(1) Propose an ML-guided approach to material/chemistry discovery/optimization.
(2) Gather existing data (real, experimental data).
(3) Realize there’s less than about 50 true rows of data on the outputs of interest.
At this point, you either: (4a) revert to traditional methods but keep the veneer of using ML to save face, or (4b) pivot to computational/simulation work or a high-throughput system that’s very far removed from your original problem, but allows you to keep playing with ML toys
It’s really bad. I left the industry. I don’t know how long it will take for people doing real science to take back the reins (and the funding).
Here is an article by Pat Walters on the usefulness of ML in drug discovery. This article is a response to another one making the case that utility of ML models are very limited in drug discovery
https://patwalters.github.io/Response-to-Peter-Kenny/
> (4a) revert to traditional methods but keep the veneer of using ML to save face
I haven't worked in the industry side of things but in academia everyone kind of agrees that gradient boosting trees are some of the best models to do these things.
The real value right now is in figuring out how to generate robust data cheaply and quickly. I'd wager that the effect of a good model on marginal data is small, but the effect of a marginal model on great data is probably quite large.
(3) seems like a problem in its own right? Basing science, traditional or newfangled ML, on such small amounts of data looks pretty weak.
I worked on some of the very best funded plant research out there. When it comes down to it, there's enough variation caused by confounding factors, and it takes so long to capture more data, that almost everything anyone tries cannot be called a success or a failure for years, because the individual measurements for one small plot of land somewhere just don't mean anything. Once you do an entire experiment for a season, which takes months, and you grab the little noisy data you have, and turn it into real rows, we were down to very little.
You can do more tests on smaller things, like checking if some protein will kill some cells of a pest, but making sure a plant produces it enough that it actually does something significant to the real, live pests, that it's not toxic, and it doesn't harm the plant's yield massively (as it's now spending time producing your pesticide) is still going to take years. We might be able to fold proteins, but the kind of things we'd need to really simulate plant biology well enough to not need years of failures are still very far away.
And it's far worse in medicine, as with plants at least nobody has ethical concerns if they fail and die, and nobody needs to get consent from a corn seed. Getting to 50 actual data points from many medical studies is already a lot of effort. And imagine when it's a long term study, and you need to follow patients for 30 years, as theym move, or die, or decide to stop participating, or who knows what.
In chemicals and materials, 50 rows of good data is a really solid study. That’s e.g. a 3x4x4 experimental design (assuming replicates for each condition get averaged into a single row). If you managed to prep that many samples correctly and obtain consistent characterization data across all properties of interest, you’ve easily got a paper. It’s also kind of malpractice to jam this type of data (few samples, wide rows) into modern ML models. There are plenty of simpler statistical methods that will tell you what’s going on, and even then a well-made plot might be good enough. The difficulty is not in drawing insight from the final numbers, it’s almost always in how those numbers came to be in the first place.
Thus the reticence of science-oriented companies to invest heavily in these mass data-gathering exercises to feed ML. It’s damn expensive, and almost always leads you back to raw data issues, not breakthrough discovery. Doing it without a set purpose in mind is even more likely to yield garbage.
I'm a structural biologist at a mid-sized biotech. I use AI tools daily. They make accomplishing the same things I was able to accomplish before quite a lot faster and easier. They don't help me magically accomplish new things that I couldn't previously.
For example, it helps me install academic software, debug things. It helps me take a large dataset and write scripts to ask questions. It helps me go through experiment drafts to see if I'm missing things. It helps me remember obscure formulas I use every 6 months. It has not, at least in my experience, come up with anything truly novel.
A concrete example: AlphaFold is great...to come up with a starting model for a chimeric fusion or something. What would have taken me 1-2 hours fumbling around in PDB or CIF files is now a quick prompt.
Serious question - have you tried applying AI tools to more of your job, and in a goal-seeking fashion? Have you hit roadblocks?
Yes, and it's relatively good for on-rails data collection and data processing pipelines that would have previously needed occasional human intervention. Especially now that I can run something like DeepSeek v4 Flash on a couple of RTX 6000s and just script an API to hammer away without having to worry about racking up a huge bill.
do you feel this is the same trade off of UI builders like android studio (or msvb6). you do in minutes what you previously did in 2, 3 hours.
is it all the work? no, but it's a part that's early on and have high perceived impact.
then, as you progress, that tool actually gets in the way and a new feature that would take 2 hours, now is around 2 days.
In some ways, but it's tough to say if that's my ADHD or not. It's far too easy to leave one branch of reasoning now and jump to another whenever progress gets difficult.
Though in some areas where I can sustain interest, AI is helping me go deeper. For instance, I've been putting myself to sleep at night by just asking it questions about expectation maximization and Bayesian statistics. This has seriously boosted my understanding of cryo-EM alignment algorithms in a way I couldn't do in grad school because there was no professor that understood enough to help me when I got stuck reading literature.
So it's a double edged sword for sure.
> In some ways, but it's tough to say if that's my ADHD or not. It's far too easy to leave one branch of reasoning now and jump to another whenever progress gets difficult.
I have a co-worker who doesnt feel like ADHD helps him because he sits down and just starts... doing work and typing. Assign him a complex task, he will just start on it. Mind blowing he does this day in and day out. an absolute machine.
I think the real win here is for idiots like me:
A) no education
B) no resources
C) not smart enough to be a self-taught bio-hacker
Everyone hears "AI is going to cure disease" and pictures some cure-all pill from a bio lab which is what I feel this paper is hinting at is missingb but that's the top of the funnel; I'm at the bottom where patients live and that is where AI is already quietly working. Its just not being benchmarked.
I built https://crohns.ai. I set out to make an AI-native clinical-trial manager with a feedback loop (DDP) and ended up somewhere completely different: instead of chasing a new "drug" which is totally out of my grasp; financially, intellectually etc... I used it to codify a care protocol that helped me avoid a flare after I got laid off, lost my insurance, and lost access to Skyrizi.
> Skyrizi
How are those biologics? Did you have to visit the doctor to get injections frequently?
Hands down the best drug I have been on EVER; but its 11k a month with no insurance.
The 1st 2 injections where done by a nurse that came to my home, the others were done as self injections using their njection kits.
Ironically, now I have several people that are on it tracking their infusions etc...
Intent: https://wiki.crohns.ai/agent/posts/ibd-biologic-switch-decis...
Program: https://crohns.ai/program/71168-biologic-therapy-initiation
Protocol: https://crohns.ai/protocol/71168
If given the chance, I might go back on it because my protocol can be a little strict at times but either way I do see a significant shift to tools like this given the state of the US Healthcare system.
Derek Lowe discusion of the paper https://www.science.org/content/blog-post/so-how-ai-drug-dis...
I think that was originally linked but got changed to the £30 to Elsevier version for some reason.
Derek Lowe as a science communicator, and others like him, is sorely needed to understand the real meaning and significance of the study and others. I say that as someone with a PhD in chemistry who's been to plenty of presentations on drug discovery topics.
It's difficult to calibrate statements made by other scientists unless you're well embedded within a field: Is this someone whose opinions matter? Are they the subject matter expert they make themselves out to be? Is this research itself truly impactful? Is it really 5 years until it will be realized outside of academic labs? Etc...
It's difficult to decipher questions around credibility because they rely on real-world interactions and associations that extend beyond the physical tokens of paper counts, publication venues, citations, and author lists that typically lag behind the front of human knowledge which is generated from real-world interactions. It can be simple things, like the insightful question a grad student, with minimal publication history, asks in a seminar.
Of course, the paywall is also unhelpful too, but a good, brief commentary by an appropriate commentator is a better link for 99% of prospective readers compared to most "peer reviewed" (scare quotes because that's a real question nowadays) articles.
Not that I'm a Derek Lowe fanboy or anything but the entirety of your comment is like "that guy needs to check himself" without, like, any specific context, any specific argument he's wrong on this specific question or like anything. It's like "deciphering questions around credibility" is hard ... all the way down. Where's yours? What are you saying?
Isn't the comment in fact praising Derek Lowe as a science communicator? In the second paragraph OP is just posing the questions that one might have when reading about a field not your own, that highlight the importance of reliable science communicators.
It's a very confusing comment since Derek Lowe is a chemist working in pharma but is being referred to only as a science communicator which I suspect most people would consider to be an implicit insult.
I think it says more about the person reading it as an implicit insult than it does OP. I am, I think, a fairly competent scientist, and know many great scientists. I know very few great scientific communicators.
OP here: the title of the thread still links to Derek Lowe's blog post, but the article discussed in the blog post was added to the body of the original post (not by me).
Obviously the missing part (which we already have for software and math) is that we need agents to be able to run automated loops in the real world. That basically requires robots. I think we'll be there in less than 5 years.
Need one for hair loss ASAP.
there is a AI designed drug for hair loss that i know of.
its slow-release oral minoxidil formulation called MINX. AI helped with the formulation [1].
its in in similar category as VDPHL01. Hundreds of millions if not a billion dollars has been invested into Veradermics, and their main product is VDPHL01 (also an extended-release oral formulation).
[1] https://x.com/anagenxyz/status/2071601868841595082
I’m on 0.5 to 1mg oral minoxidil daily for a few years now and it’s working great. Blood pressure benefits too.
Any other side effects? I’ve never even heard of this.
I’ve taken low dose oral min for a few years and it’s systemic, meaning it’ll make all your hair grow. I now have body hair where before it was never noticeable. “I’m hairy like monkey” as my kids say.
Indeed. My plans for a youthful Mohawk are being stymied by the lack of AI promised medical breakthroughs.
Wheres my follicles dammit?
Track KX-826, clascoterone, and VDPHL01
PP-405 too
A good set of clippers.
Already exists (finasteride), only problem is that it castrates you chemicallym
For everyone struggling with hair loss but concerned about side effects of Fin. Look into topical fin. It produces higher concentrations of the medication in the scalp and lower systemic concentration. Not perfect, but better.
About 2% of finasteride users experience these side effects, and they are reversible after discontinuation.
Apparently some users report persistent side effects even years after stopping the medication (post-finasteride syndrome).
How many of them can be accounted for by the natural rate of sexual dysfunction?
I've been taking it without significant side effects for ~15 years, so I'm not worried, although at this point it's losing its main effect as well.
yep that would be me
Yep. Not everybody but enough to warrant vigilant monitoring and titration.
Absci has a good asset coming out pretty soon that you could try to get on the trial.
Absci is bullshit
We're all about to come face to face with this reality. This dance can only last so long.
>We're all about to come face to face with this reality. This dance can only last so long.
Only for values of 'all' that exclude well-connected members of the billionaire class and their select associates.
if you think the state of the art in this area is something you'll hear about from a guy that looks like santa in an academic journal, your investments deserve what's about to happen to them.
[AI drug discovery] was never the hard part.
This made me laugh, more than expected, but I did visit LinkedIn just before so that could explain it. Thanks!
How refreshing was this article vs. all the slop?
The lack of comparable data and testability really does seem to be a challenge. I wonder if people would be more willing to collect and share lots of health data if the collecting company was a non-profit dedicated to anonymizing it.
need one for brain plasicity. it would be nice to be able to easily learn a foreign language or musical instrument naturally.
Easily? Have you ever actually learned a second language or an instrument as a child?
Vs as an adult. Yes it's hard as a kid, but as an adult it's even harder.
There is one!
Ketamin should have huge impacts on neuro/brain plasticity when used properly (i.e. in therapy)
Psychedelics are several orders of magnitude stronger on the plasticity front.
In therapy as well.
Psilocybin has this effect. Source: I can't remember where I read it, so low confidence.
> "The paper goes on to make recommendations for AI companies and investigators, and these are well worth reading. The common theme is that people need to think more about why they’re doing certain techniques or using certain technologies, rather than just using them because they’re newly available."
Please. Please let some people with power and influence understand this lesson sooner rather than later. I understand the reasons that's unlikely to occur, but usually the impact isn't quite so drastic and expensive as this is. Just because something is new and shiny doesn't mean that it'll produce the outcomes you need at the other end, and until it's shown that capability your approach to it should be MODERATE.
Drug discovery scientists think about what they're doing and why ALL THE TIME. AI stuff is just another tool.
It's also worth mentioning that drug development timelines typically exceed the interval in which these technologies have been available (or at least effective). Measuring impact will take a long time.
nerd-fanboi proposal: Articles by national treasures (like Derek Lowe, Raymond Chen) should be highlighted with specific identifiers on HN - like a distinctive title font or an ascii diamond ◊.
No thanks. Social media needs less hero worship. Just RSS whoever you like.
Make the feature a per user list of flagged sources.
I would have agreed in past, but the new random "blog" at whatever.etc being written by LLMs is making me skip clicking most links here now.
I agree. I won't do heroes. Ever.
A National Treasure, on the other hand - they enrich life without being a vector for tribalism (eg:Michael Kramer/Kate Reading).
Has this yet produced a treatment for AI psychosis?
No? Well fancy that! :)