Early progress on accelerating drug development with Claude
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James van Geelen Of Citrini research brought up a good point on a podcast this week, that designing the molecules is perhaps 10-20% of the effort in bringing a drug to market. We have more possible drug designs than ever get developed.
Whilst AI drugs might be more likely to pass phase 2 tests than others and AI might be applied to the rest of the process there are still big bottlenecks in the way of AI creating a radical change.
https://podcasts.apple.com/gb/podcast/monetary-matters-with-...
And nothing I've said here is an original thought or piece of insight.
Further, [we are getting much better at simulating drug interactions.](https://pubmed.ncbi.nlm.nih.gov/41155903/) I strongly suspect AI will offer new probabilistic models to funnel novel drugs into more targeted trials which are much more likely to succeed. If we had 90% certainty of success and high certainty of lower adverse reactions I think we could allow much less stringent human trials. That means much lower cost and much faster turn-around.
a) We do test on terminally ill people, but after testing in other models. b) Thalidomide was all roses compared to the horrors you can inflict. c) Profit-driven industry makes extremely perverse incentives.
Still I do come down more on your side of the argument, and we have historically made massive leaps doing some horrific things.
I wonder if we can get better models based off of human cell-lines / tissues? Like CRISPR did?
Just to expand on this for the audience. Even a perfectly reasonable synthesis of MPPP, a synthetic opioid, can produce MPTP[1], which will destroy all of your dopaminergic neurons and give you permanent brain damage. This was discovered entirely by accident. Or, Karen Wetterhahn, who got a single drop of organic mercury on her gloved hand and spent the next year suffering from her brain rotting from the inside out.
This only barely scratches the surface from what I can remember, but there's absolutely, unambiguously a lot of good reasons why these barriers are there. These are rules that have been put down through blood and tears.
[1]: https://en.wikipedia.org/wiki/MPTP
People who are specialists in something other than economics often make this argument but it's often a very weak argument, IMO. Liability means that you are incentivized to be quite cautious in pushing a potentially dangerous product. Particularly with pharmaceuticals a class action can be easily and successfully argued in court. Moreover, a relatively small number of dangerous flops will tank your reputation with consumers. I would also add that you can't make any more money selling drugs to people you have killed.
The worst money incentive for drug manufacturers is that market forces push them towards recurring treatments for diseases that effect a large number of people. However that seems less than perverse to me.
That assumes that people are playing a long game. If you're looking for your payday and exit, and there are many people out there who are only looking for the next payday, this argument falls apart.
> Moreover, a relatively small number of dangerous flops will tank your reputation with consumers.
One success the anti-vax people have achieved is to muddy the waters around this. Your public reputation is no longer correlated to actual performance.
> Liability means that you are incentivized to be quite cautious in pushing a potentially dangerous product
There's a long list of people throughout history who do not have this caution at all.
You seem to be taking a theoretical economics approach. Reality is different. People are not perfectly rational actors, and consequences are not consistent.
Look at what actually happens in reality.
Payday and exit is pretty hard in biotech as you need to conjure up billions of dollars in capital, which doesn't often flow to people who payday-and-exit.
What happens in reality is that people try really hard to make safe and effective drugs, they mostly fail, and the ones that are approved are generally safe and effective.
d) elderly and infirm people are in a weakened state and so are less likely to recover from accidental complications your drug might introduce
e) sickness is itself a confounder for the toxicology effect of a drug. Suppose you take a drug and your liver shuts down - did the drug cause it or was it your underlying illness? This can lead to programs being prematurely shut down and actually prevent the development of treatments that might have gone on to succeed.
You misunderstand how hard it is to pull signal from a drug being put into a human body, which is why we need large numbers of people who meet extremely narrow criteria (and don't have things like a terminal cancer).
Your latter idea is a good one, and hopefully something AI will help with. As you imply: we will know empirically when it's ready because trial failure rates will trend toward zero. Today we're closer to 90% failures and not even clearly trending in the correct direction.
You're aware that, as a healthy volunteer, you could go sign up for a Phase 1 study today, right? You can even earn a pretty penny doing so.
It really is hard to see AI improving the trial process meaningfully. We are more likely to see progress with better trial design and regulatory regimes.
But yes, it was very effective as a treatment. When it was approved for cancer use, we actually had no idea how it worked, only that it worked really well. Thalidomide has saved a few orders of magnitude more lives than it took/damaged.
The amount of disdain you have for human life when it's past an accepted state of freshness is disgusting.
Prior to AlphaFold that was the state of predicting protein structures. That model seemingly figured out the "language" used by biology for making protein structures from DNA. It's not unreasonable to believe there's a good chance something similar exists for protein interactions.
Even if it was coarse grained but could eliminate drugs with high likelihood of unfavorable reactions it'd still be valuable. Still not sure LLMs would be ideal for that work.
Source: spent 9 years at a AI drug discovery startup. We advanced 8 clinical candidates and every one of them ran into some variant of this issue.
While the process may not be directly cheaper with AI it may side step other problems that profit seekers run into, like people wanting days off or pesky things like human rights.
Consider commuting with a friend in the morning: if you get dressed and get out of the door in 5m instead of 10m, but then have to wait for me to come pick you up, and then we both sit in a car for 5m to get to the train, and then we sit on that for 50.. then your speed up doesn't really matter.
I don't know if this is the case for drug development but as GP said, we already have more designs than we can test.
We already see that with software. It will be exactly the same with drug design.
My daughter is a leading biochem at one of the big pharma companies and that’s their principal worry about this. It’s really easy to create a lot of information these days which compromises the entire process.
There's nearly zero value, and practically speaking probably negative value, in being able to more cheaply generate drug candidates.
My money is on Eli Lilly leveraging AI to develop drugs vs Anthropic becoming Eli Lilly. Obviously teaching drillers to be astronauts is the better movie though.
It's always something I've hated, coming from software and working with experts. I always wanted to be the "software guy" who gets to learn the specialist engineering disciplines. It just felt like swimming upstream and the incumbent specialists always wanted to DIY.
Having said that, some subject-matter experts just don't have the stomach for it. They are so separated from software that they have a block to building even with AI. A good s/w-specialist pair is dynamite there.
The best places to work are ones with high mutual respect between the 'R' and 'D' of R&D. When either camp dismisses the value of the other and everyone stops giving a little to make the handoff easier it just makes everyone miserable.
There's very little mutual discussion, brainstorming, anything. The slowness inherit in building software before made those discussions almost necessary, now they're post-hoc criticisms (PR "review") rather than fulfilling collaborations.
It's fine from a technical sense, but I do wonder what it does for culture.
I used to work at startup that did video encoding. We had tons of computer vision scientists that refused to learn decent C++ coding practices. Their stuff never worked right and they didn't really contribute much. We also had a few scientists who were decent coders and pretty much all progress came from them.
I see the same with some data scientists. They want to do pure data science and refuse to do the coding to actually get their data. So they have to be spoon fed all the time and their progress is super slow.
Astronauts, drillers, AI researchers, drug developers...Ben Affleck and Bruce Willis can do it all!
It is not a profitable quarter.
That regulatory function is quite personal and hands on. It's relationships built up over months and decades. AI, almost by definition, cannot replace it or augment. Sure, faster, better. But even then that backlog will grow to meet any gains.
Source: I have done (and have family members that do) compliance and regulation for larger phramas, including drugs and instruments that you have had (with near certainty) your medical reports analyzed and produced by.
(Also: Same site, but click on earnings instead of market cap).
I just thought Tesla was a good example here, of how innovation and speed of execution can enable a new entrant to outperform established competitors.
> (Also: Same site, but click on earnings instead of market cap).
Well yes, I'm not going to argue about that one, the market is the market...
Eli Lilly might be the experienced horse carriage operator to Anthropic as the Ford motor company here instead. Or Blackberry when the iPhone was announced.
The biggest advantage Eli Lilly has is in navigating the FDA regulatory process, and at present, US Federal regulations & bureacracy can be pretty easily managed by putting one of Trump's sons or friends on the board or giving a 'donation'.
The regulations, liability, and stringency of procedure in pharmaceuticals and healthcare in general simply won't gel with the SV mindset of "move fast and break things"/"forgiveness is easier to get than permission". A University of Missouri pathologist working with Jon Carreyrou put a SV biotech darling in literal prison despite a huge valuation. That sort of accountability is the exact thing founders seek to avoid, not court.
> If Claude is to significantly increase the rate of new cures, we need to work with many more biotechnology firms and integrate Claude more deeply into their development processes.
Given all that, the wording here feels very self-serving. Cautious optimism is probably warranted.
I think it will look more like Claude just helping every expert along the way do their job better. Analyzing and synthesizing data to make better experimental suggestions and design new equipment (or totally new experiments based on unconceived-of apparatuses?). Managing the flow of information and building bespoke knowledge bases and tools. The more it is integrated into all the systems and researchers involved the faster it can put things together.
Although, on second thought, maybe there even is a near future where it takes our models and understanding and just galaxybrains a better model. Has anyone tried feeding it some collection of datasets and saying "can you make a more detailed model of a cell?"
Isn't this exactly what startups in this area are doing - automated experiments connected to AI. Of course at some stage things will "graduate" to human review but it still seems to have a lot of potential.
Biology grad students can spend 40+ hours per week in the lab, on top of all their course work. It's not so bad though, since there's plenty of time to grade papers or work on your thesis while waiting for reactions to occur.
The reaction can’t be sped up, its rate is wholly constrained by the nature of the compounds involved and the physical parameters of the experiment, which may be very narrow to avoid unwanted products or even thermal runaway which could cause a total failure of the experiment or even a dangerous release of poisonous or explosive materials.
Genuine question because I don't know the answer, I've just anecdotally heard all about the promise and nothing about real world results.
I think human review may screw things up. If you look at dimethyl fumarate[1], no card carrying medicinal chemist would have taken this serious. No pharma exec would have risked 100s of millions of dollars for clinical research trials.
And here is the real problem. Instead of burning gigawatthours of power with GPUs, if Anthropic really wanted to help bioscience, they should just be throwing their $2 trillion at making it possible to live off of studying things like this.
We aren't in a place where a magic "Intelligence" could just look at the data and solve all our problems because we have never funded the collection of that data in the first place
We spent a lot of money on the Human Genome project and got large dividends from it but seemingly never reinvested! There's so many clear and straightforward questions we should be answering in biology that we just aren't, because nobody wants to pay for it.
LLMs aren't the right course here!
They can put it onto robots / automatic labs, so this step is easily solvable. Humans in a modern lab are largely unnecessary. Just look at China.
The problem here is that Anthropic claims to have solved this all. It is time that they actually prove their claim. Right now it reads like an advertisement to inflate the stock of Anthropic.
You can automate the setup and mechanical process. You could increase the physical capacity for parallel tests. You could increase the rate of acquiring raw materials.
But how much you cut down the time depends on how much of the trial-and-error feedback loop depends on feedback and iteration vs cast a wider initial net.
(One of the interesting things to me about the dominant software development agent pattern across all of Claude, Codex, and Cursor is that the approach is still extremely local and iterative, human-mimicking, rather than big-brain-one-shotting a novel or perfectly-fit-for-purpose software design. The performance of the agents on the initial "jump to conclusions" step does not appear superhuman in the same way that the speed-of-iteration is clearly superhuman.)
Care to elaborate, explain?
That is utterly untrue. It stems from the notion that biology only needs big data for us to solve all its mysteries.
Beyond that, while automated labs for cell biology are plausible and active, and the same fit organoid biology, the same really isn’t true at organism level. Labs already are artificial environments that make organisms have odd phenotypes, and odd interactions which bias the data away from what you’d find in the natural world.
You add in robotic manipulation, and you add a new confound.
Drones in the wild are an obvious counterbalance to this, though even in that scenario, the drone can be a confound unless well hidden.
In the end though, this kind of systemic breakdown of biology, while much needed raw material to improve our theories, won’t solve the whole of biology. Human hands and human minds will be needed for a good long while.
As always Anthropic is saying literally anything if it gets them closer to the giant bucket of money that is the IPO.
Pharma makes bank on drugs that are patent protected. Squeezing the manufacturing cost is a small gain on top of that.
If a big pharma had the choice between an expensive treatment that could help treat any cancer, or an extremely cheap treatment that could prevent any cancer - which would they pick? The answer when you're motivated by profit is unfortunately different than the one when you're motivated by social benefit. And this is more than just a thought experiment because this issue is going to drive which type of treatments are pursued.
Picking the right drug is a lottery game in itself that involves portfolio strategy, biologic modeling of your product efficacy and safety, optimization of yields, and "field testing". Then you still have to build the right distribution channels to the patient and find someone to pay for your product. Even reproducing drugs is not a trivial thing, as you need to re-certify production plants. In the case of biologics it is even more complicated.
Unless we burn enormous amounts of cash or set up specific incentives, there is not way we can bring primary socially motivated drugs to the market with the current risk structure of the industry.
Pharma is one of the most complex playing fields there is. The sad thing is that we might strongly overstimate the ability to significantly improve the development of new drugs through AI in the next 10 years.
Get million out of thousand people or ten out of billion people... Later would be preferable.
Yes, and reducing manufacturing cost if you have tight margin has huge potential for profit.
Being a doctor in America has a high barrier to entry.
It’s not because medical science is difficult.
It’s because the American Medical Association (aka, doctor’s union) limits the number of new doctors and medical students each year.
If you 2x the number of medical students, resulting in 1.5x number of qualified doctors, resulting in perhaps 20-30% lower salaries, and maybe 5-10% lower cost to patients.
But no, preserving a doctor’s middle-class salary is more important than lowering patients’ costs.
Google “number medical students over time”
Then google “number residency slots over time”
Then notice how both of them have been increasing significantly over time.
Then compare the income percentile for doctors in US vs say Canada, Australia etc. all are top 1-3%. Doctors are paid well relative to median salary in every first world country.
Then google “physician percent healthcare costs” and learn that physician pay is about 8-10% of total healthcare spending. Then realize about 40% of that $ immediately goes back to the government via various taxes, so even if you paid physicians nothing you’d only save about 5% of healthcare spending.
Do you not try to verify any information you read before confidently regurgitating it at a later point? You really should…
I actually don’t think this is a strong argument.
AI does very well following rules.
Proving your drug works is hard, but an LLM can help you come up with the ideas.
How many of these "we sandboxed it and said not to do Y, but then it went and did it!" stories would you consider necessary to disprove that?
It may be good at following rules 99.99% of the time but if someone launches a million times more parallel agents than they would hire humans, it might still be much worse in practice...
"We've been told we must find an effective drug candidate here, and to follow this process for evaluating the quality of the candidate, but actually we could subtly change the analysis like this to make this candidate look favorable enough..."
(And then compare that to deterministic algorithms...)
The argument here is that AI can run permutations faster than any human can. And, feeding it a company’s proprietary data about what worked, what did not, how it worked and so on can be invaluable at accelerating the discovery process of new drugs.
Plus, it can work 24/7/365.
People who behave like OpenAI and Antropic AI does when controlled by those companies get punished instead of celebrated.
There's no regulation on not doing a trial because your oracle machine told you it was an expensive dead end.
I can't tell -- has there been any real progress made? Or is this a supposed application of AI that is getting interest? What about this problem in particular makes it suitable to AI as opposed to any other?
Or is this just a matter of -- "AI can cure cancer and is Good For the World!"
The last hundred oncology drugs have all required painstaking trial and error with no way to predict which candidates succeed or fail except at the years long cost of blood sweat and tears. A.I. will not remove that price from the equation except maybe move towards those trials marginally faster towards those decade long trials.
Oooops I mean, the agent "escaped" and hack the goverment... definetely not training it to become it's own state agent thing... pinky promise....
"Cautious optimism is probably warranted" strongly negativism advised
Actually creating them requires physically manipulating the inputs to the process, which requires humans doing it or humans making machines that can do it and allowing them to be controlled by the model.
In general the damage potential only goes as far as the agency extended to the models. The problem is that we've already seen how incredibly cavalier most people will be about security, and now we can envision situations where one bad actor could threaten billions.
Maybe in 10 years when frontier-level models are easily usable and not locked down by the government and PVP between the companies and everyone else to justify their existence and take all the resources from everyone.
Maybe then it'll accelerate development.
I suspect what is happening here, though, is that Anthropic is going to take a bunch of Dunning-Kruger AI researchers who throw prompts at Claude and publish 1,000 papers worth of drug-discovery slop that will only waste the time of actual experts...
This has been one of the target AI applications since the 1960s.
(Former drug developer who will never touch the life sciences again)
"Developing a drug involves many varied tasks: doing early research to identify a target for a drug and the right type of drug to engage it, designing a molecule with the right properties, optimizing it to be manufactured reliably at scale, drafting the regulatory filings needed to run clinical trials, designing and administering those trials, and much more.
Our strategy to realize AI’s medical benefits is based on having AI increasingly involved in all of these tasks."
Optimizing molecular properties of a lead is important, but ultimately not a huge source of costs. In any case, LLMs probably have as much chance of predicting outcomes as anyone else (i.e. very small).
Manufacturing is somewhat expensive, but most of the cost sits in actually creating a production process, which is not a thing LLMs can do.
Clinical trials are where most of the costs lie, but unless you can create a magical classifier that can tell me if my drug will make it past phase 3, you're not significantly bending the cost curve.
> unless you can create a magical classifier
A generator could concentrate on pockets while remaining unable to judge most drugs. And LLM RLVR a) works well, and b) is black box optimisation and so is suited since it doesn't need system identification (which you can't do for complex biological systems). So the hypothesis is that, yes making a classifier is very very hard, but you could through some (lots?) initial interactive data, end up with our generator outputting with high probability drugs-that-end-up-passing-trials-with-probability-p. Whether these drugs thus generated are drugs we care about (i.e, whether `p` is good enough) and whether the whole pocket it generates in is large enough, and how jagged this behaviour is (i.e distribution of `p` across taxonomies of drugs), are the few questions we want answered.
That said, we have extremely well-refined force fields that attempt to model the underlying physics, and still fail to do this well. There's absolutely no reason to believe that a generic LLM will bring anything to the table that improves the situation. Even with the hand-wavy assertion that we (somehow) get "lots and lots of data", and somehow train a purpose-built model to do the same, it's kind of like saying that we could turn a minivan into a formula one car with enough effort. Different tools for different jobs.
I actually do believe that informatics approaches will be helpful given lots of system-specific data, but when I say that, I mean "a bespoke neural energetic model that is trained specifically for some molecular system down to customized force-field parameters", not "Claude".
So sure, maybe Anthropic will invest lots of time and money and energy into making these kinds of molecular simulation models, which they can then use for high-throughput screening experiments. But even if they do all of that, there's still no guarantee that they'll bring anything to the party that hasn't been done before...because this is the lowest-value step of drug discovery, and a model like that won't tell you if your drug is going to be toxic, or have off-target effects, or have metabolic problems, or...a million other things, which are what kill drug programs in phase 1 and beyond.
We have seen how these large models learn high order levels of understanding that is unexpected, e.g. physics in video generation. That makes me not want to dismiss the work in this area particularly when paired with orders of magnitude more computation being applied than us normies have access to.
This almost seems like catch-up to Google DeepMind stuff where it is research for research sake. So in that sense I can see the skepticism where one can look at this as taking Claude and pouring it over a problem like sweet LLM syrup that will make it all taste great.
Hopefully that's not the case.
Hmm.. I think it's my terminology issue. I just meant pocket as in informal word for "subspace", that is, generators can learn to generate good candidates within some subspace without needing to be good classifiers across the whole space.
But yeah as a whole that makes sense, thanks.
This is the fundamental problem with nearly all tech-meets-drugs startups. They almost always focus on the part of the problem where there isn't a bottleneck, because the parts where there is a bottleneck are the parts we don't understand at all.
Are they doing the thing where they develop the technology first, then hunt around for problems for it to solve and hype up potential applications?
That said, the LLM version of this is perhaps more solution-seeking-problem than past iterations. LLMs are obviously useful [1]. Scientists already use them. The labs are just desperate to find a multi-billion-dollar application outside of coding, and they’ve all decided that “drug development” is lucrative, so they're gonna apply their magic to that problem and collect profits!
[1] In the way that photocopiers are useful in drug development: as a tool. A powerful tool, but still a tool.
Re-ranking pre-clinical leads is the scientific equivalent of that old saying about opinions.
There is no objective function you can optimize your way to a drug discovery machine, let alone a fundamental understanding of how the underlying system works.
This sounds quite similar to Claude's impact in enterprise SWE, where "writing the code" is sometimes the easiest part of the process vs. alignment between humans, understanding the requirements and problem space, and all the other things that make SWE slow.
Thank you for entertaining my curiosity! I have been meaning to educate myself on this but don't know anybody in my circle who is knowledgeable in this field.
We can try to simulate the interaction between a drug and a protein, but most of the time the data to say what the downstream effects (if any) are simply does not exist. This is further complicated by the fact that these downstream effects can be extremely varied depending on both the tissue/disease you are targeting and the person you are treating.
Of course we are always generating more data from more diverse populations and more conditions, but there is a very very long way to being able to simulate drug trials like this.
In another comment I said that if the area of biology/microbiology etc were the size of the surface of our sun, the area of our knowledge in those subjects would be perhaps a square millimeter.
And if you think that’s bad, don’t get me started on higher level areas like neurology.
Unfortunately the HN UX doesn’t make it easy to surface these situations, not that I have any idea how one could make it easier.
I'll bite: why not?
Discouraging-but-part-of-life issues: nobody is in a rush because everything is a marathon. It typically takes decades to get anything into the market; though regulators over the past few decades have been changing things to speed things up (basically doing science to make the process more adaptive and dynamic) it's still hard. You're continually looking for needles in haystacks: wispy signals that may be noise, extracted from huge datasets. As a startup, expensive slow, e.g. an overnight session on an HPLC can be a $10,000 experiment (or more because you're probably running a lot of those instruments in parallel at once) after having a bunch of people working all day to prep the samples.
The terrible: the industry is riven top to bottom with fear. Fear that the science will not work out. Fear that someone else is smarter than you (as a result: pervasive arrogance and hostility). Fear in the FDA that some company trying to game the system* will get something past you, or that the inspector general will launch an investigation, or some congressperson will launch a missile at you for no legitimate reason.
Also, you're launching artillery shells that can't be altered in flight. You write a study and then you have to run it with the protocol you wrote and got approved. You can't alter your marketing message unless it refers to scientific results explicitly demonstrated in a trial. And since the trials typically take years, there's a lot of sitting around.
* Back when I was in the biz we absolutely did not try to game the system and it made it a lot easier to work with the Agency (FDA). But the rules kept being made tighter and tighter to lock our bad action by companies. And of course the recent destruction of the FDA means all the rules have been thrown out the window, but by then I was already long gone. But my friends in the industry are still shocked and appalled.
>> If Claude is to significantly increase the rate of new cures, we need to work with many more biotechnology firms and integrate Claude more deeply into their development processes
Sounds invasive.
> Sounds invasive.
If it's not working, the problem is clearly "you're not spending of your money on Anthropic's products," and the solution is to spend more.
I'm interested in seeing how the scorpion anti venom plays out.
https://www.anthropic.com/research/claude-scorpion-toxin-bin...
I'd be happy to be wrong, though. I've never tried to do biology with Claude, but I have gotten many refusals and downgrades for security work.
Biology is the complete opposite of that.
Despite the improvements, though, we still see plenty of code that is "slop", because it's directed by someone with little idea of what to do, but also because it's harder to test for (or even measure) code quality than correctness. The "solved problem" rhetoric depends very strongly on your standards.
In other fields the problem is similar, and typically worse. Plenty of AI music sounds acceptable, not clearly flawed, and even mildly "interesting" to me; but I think this is much more a commentary on music than it is on AI. Prose continues to be abhorrent in nearly every application; while I've seen examples of people getting really impressive output, it takes a level of effort that defeats the purpose of almost everyone who wants AI to assist their writing (i.e., just generate a comprehensible report on some findings and get on with their day). Art is a whole different world; the quality of results clearly strongly depends on the quality of prompting, it's much better/worse in some genres than others, humans vary widely in their ability to notice "mistakes", etc.
Humans are just organic simulation environments for these drug trials.
For instance, let's say we are doing biology on plants, which is still much easier than doing human trials. No matter how motivated claude might get, you aren't getting a corn plant to grow in 40 seconds. Real world research pipelines involve stages where even greenhouses don't do, and sacks of grain are shipped to Hawaii, or Brazil, or wherever, to try to get practical data for a given test.
If your computer simulation is so accurate you can skip a step now done with biology, great, but it gets harder the larger your problem is. I might see if a molecule kills bacteria kind of quickly, but what does a molecule do to a real human being? Not so easy.
Seeing tiny stuff doesn't magically give you enough info for Claude to reason with the system predictably (the way it can with source code).
There’s no well-labeled dataset for all this because we don’t know everything we are looking at.
Take a neuron. You can stain it so you can tell apart an axon and a dendrite. But if you don’t it’s very possible to miss or mislabel the axon, because unlike the textbook neuron, the axon isn’t always obviously oriented in a way that promotes ID.
And that’s just one example. With more complex samples, you also have a lot of edges which AI models don’t segment very well.
So, I'd say it's a pretty similar process, and yet we have the AI models now almost completely integrated into the SWE domain remediating significant parts of its complexity.
At peak, we had 1-3 million xeon cores running our tasks (protein folding, protein design, drug docking), 24/7. This was before GPUs were really a thing. Each mini-task was basically a rosetta or gromacs or autodock binary running on local inputs and writing local outputs for ~1 hour using ~1GB RAM and no network. Input and output was via colossus, or distributed through a global torrent network (literally all 1-3 million cores formed a single data transfer group, with data passed between clusters at low probability).
I built most of the code of the system (which depended on Google infra) including the scheduler and the machine worker, and also ran the system as on-call "SRE" for 3 years. During that time, we nearly almost didn't take down the Google global network, and had small latency impacts on many services. Managing to run the system for so long without antagonizing ads or search or youtube was a major challenge.
I would say the complexity of the system I built (considering everything down to the OS) was roughly the level of a few human cells in a plate, in terms of interacting components and the complexity of their interactions.
Here are some of the publications that came from running exacycle (we provided strong evidence that using more compute improves outcomes in molecular simulations)
https://pubmed.ncbi.nlm.nih.gov/24345941/ https://pubmed.ncbi.nlm.nih.gov/24265211/
Anyway, my point was not in that I would equate software complexity with the complexity found in biology but that I see the same trend and similar type of challenges that can be found in both fields and therefore I see both fields amenable to the AI advancements whereas many people from the thread do not, and I disagree with that.
I used to work in the MPI world on supercomputer jobs and it was miserable. I also worked on other high concurrency systems at Google (helped run the Ads DB, did training and engineering of Sibyl). I continue to insist these systems are incredibly simple compared to even the most basic human cells interactin g.
THe primary bottleneck for a drug is getting it through clinical trials. No matter how much you speed up lead detection, development, and in vivo studies, the bottleneck will still be clinical trials. Now, if we could make lead detection better at generating molecules that get through clinical trials, that's great- but it would take decades to know for sure. If we could make development less likely to produce toxic molecules that trigger the immune system, sure that's great- but again, we're talking decades before we know if it worked.
Attempting to improve clinical trials- well, those are already fairly well designed and run, but it's likely you could make improvements there too that increased efficacy. But again, you're still just dealing with the FDA, filing tons of paperwork, and tons of people looking at the paperwork. Unless you fix those systematic issues, we're not going to accelerate drug approvals.
But I don’t see how any current AI system will have the curiosity and drive. You need to create conditions for meaningful experiments to be done and a mechanistic way of doing this doesn’t work. The most elegant experiments find ways to cut to the core of the emergent behavior, around all the simultaneous changes happening in different levels of the system from which said behavior arises.
You simply cannot see it all. Not every AI can, we are measurement limited. We can track and measure some dynamics, and the art of the experiment lies in thinking through the connections, then picking the right places to look to catch the system in the act. And you better do it so the failure guides the next experiment, and the next and the next, till you finally have sliced through the complexity and found some key nodes that hold the keys.
And usually after all that, you find your key node is regulated by the key nodes of a few other grad students. The complexity here is in part because the entire system is in constant conversation across levels of organization. The causal flow is looped and is also very sensitive to the environment.
AI has a big role to play, but I doubt it’ll parse it without humans at play. It simply cannot invent a slight modification of a microscope and physically connect the new lens element in the right place, calibrate it, all in time for the biology grad student next door to bring some cells to image that are a mix of 40 days old (no contamination! Magic!) vs 30, 20, and so on, to do a very expensive experiment (in terms of labor and time and rats and materials).
So much can go wrong on so many vectors. Which is also what pushes humans to their creative best. And AI will help a lot. But till you have a robot that can drive, but also do mouse dissections and staining and manipulate a microscope with faulty parts to get a clear image, you’re going to need humans to study biology.
When you invent these super robots that somehow slide through all this physical labor without consuming a whole lot of power, we can talk.
Meanwhile, at the cutting edge, where neither procedure nor mass produced machinery exists to do the whole thing, the reality is, we already use a lot of fancy tech in biology, and can use more, and AI can help keep the focus on the actual physical experiments that take so much care and attention.
https://xkcd.com/1831/
AI already can do in-silico simulation
add microscopes and tiny precision actuators, and we can build closed feedback loop in-vitro system
once we have in-vitro, it will be easier to go in-vivo
I've always thought openai could actually create an agent that can control various physical robotic arms and things like that, and then continuously iterate on solutions that can participate in the real physical environment. This could accelerate automatic iteration for many things that require human operations, such as complicated experiments, assembly adjustments, and testing.
Otherwise, AI is always trapped in virtual systems and still needs human intervention. But even though AI is already very powerful now, there is still a limit to how much humans can handle or assign tasks. Many times, AI has already finished processing something, but I don't even have time to assign the next deeper level of tasks.
People hope AI can improve productivity, but at the same time they're afraid of AI actually getting involved in reality, while the valuation of the entire AI industry is so high.
This kind of wanting to move forward but not moving forward makes humans especially uncomfortable.
https://deepmind.google/models/gemini-robotics/
finally, us
Since sequential steps are slow, I think we're going to see massive increases in parallelization. I'm not sure how the regulatory frameworks will keep up honestly.
Why? This is not an AI problem, it's an industry one. And if it was simple, it would already exist.
Let me guess. You have never stepped in a lab.
Not necessarily. Many things are simply gatekept behind the door.
We see it happening in SWE as we speak - people from and outside the tech who had no or very little experience with programming are now starting to deliver their ideas into products without devs or very few of them. We also see this within the SWE where strong domain experts are now able to deliver ideas into code easier than ever, and by themselves, and for which they would previously need a handful of programming experts. We also see it in other way too, strong programming experts are now able to pick up other domain easier than ever.
This process is something that I believe it will (and hope so) inevitably be picked up by the pharma too so the myth about the complexity will become broken.
Process improvements come from failing faster, so you spend less money on your test. Finding the actual answer without testing would also be wonderful, but LLMs might be fast, but they aren't magic. Something like Alphafold saves time in some steps, but it's not just "hand me a candidate", but find completely novel verification mechanisms, which would be just as useful for humans. Either way, parallelization is unlikely to increase.
[1] https://en.wikipedia.org/wiki/Amdahl%27s_law
I wonder what the unlock would be, if any, of having simulations substitute any (or all, in time) of these steps. Akin to the method used to train FSD cars, or robots.-
But I'm no biologist so if someone else is, feel free to correct me.
I wonder if, at some point, AI's will perform to such degree they are able to construct "black box" simulations as needed, that nobody understands BUT that are sufficient to empirically experiment on ...
... much in the same way LLM's function now: In an aggregate sense, they are bloack boxes.-
The problem is that biology is like trying to reverse engineer alien technology to understand how it works. Math/engineering is more like building up complexity through well defined parts. Math/engineering is a heck of a lot more deterministic than biology. The ultimate bottleneck in biology in context of AI is well curated and breadth of data, not necessarily intelligence.
You can't use Claude to write a grant unless you are one of the special people because "uplift." I genuinely wish I was joking. Here are the exact passages from their report that I quoted here, https://news.ycombinator.com/item?id=49651727
My feeling on this is that math is amenable to LLM based development because the formalism of LEAN + the ability of AI to nudge research means that we basically have machines that can "research" through what is basically brute force search, but where the explored trees are ordered by AI.
But what if there were a biological system similar to LEAN for drug interaction? Could we not use AI in a similar way to nudge the exploration space based on the vibes that the AI has about drug discovery?
I agree there is a a distinction in the formalism of a proof (through LEAN) and the sort of probabilistic/chemical/physical/whatever nature of a biological organism (through some sort of future modeler for life science). But is the fact that one system is deterministic and the other probabilistic really as much of a leap as you think?
We currently have disparate datasets that allows us to make some predictions on how drugs will interact with molecules. And we have studies on how these molecules might affect our organs/bodies. But all of this data are at different resolutions, sparsity, and have different experimental context. It is extremely hard to reconcile for a holistic model.
DNA is the great unifier in molecular biology as it is the digital anchor/foundation that everything above relies on. But there is just so much complexity above it. An analogy in the AI space would be model distillation where you are trying to discover all the weights in a LLM by giving it a prompt and seeing what the results are. With biology, we know the prompt (DNA sequence, genotype) and we can kind of quantify the result (phenotype). And we have to figure out all the weights in between (how DNA becomes a fully functioning organism).
If that's the bottleneck maybe we need to rethink all the bureaucracy around the process instead of using AI to fill a document and then using AI to read it. That's just plain stupid.
Consider another area; a common heard complaint about why we don't see more police in the streets (where I live anyway, not the US) is that after every incident they have to spend time on doing paperwork - important administrative part of police work. Part of that work has been automated and outsourced, but not everything.
Of course, AI isn't perfect and can hallucinate things and get facts wrong which would impact a court case. But with the right guardrails, it can accellerate and partially automate this process even further.
“Generate scientist” … is that a real job like “prompt engineer”
Either that or six degenerate scientists. Both options work just fine
The company is called Generate Biomedicines, and they reference it in short as Generate.
So as long as you can test things on cell culture level? You can do a lot with a high throughput setup.
Seems kind of stupid, because AI companies aren't cuddly benevolent family members, they want to eat everyone's lunch.
Unfortunately, the feedback cycle in drug development is measured in years (and millions of dollars). I've seen cases where a drug works better than competitors but has a worse safety profile. The solution was to alter the drug to remove the side-effects, but that required a new trial (meaning, two more years plus ten million dollars more).
That said, I do think there is some low-hanging fruit. There are lots of datasets from past trials and it's likely that there are some patterns there that AI could mine. I don't think it will be like software engineering ("we build a Photoshop-clone in 2 hours") or math, but we might see announcements about a new drug candidate being found. Verifying it, however, will take a long time.
If the work by Cody Trangbarger pays off and some how helps find a cure or at least halt the muscle wasting of GNE patients, then I will start to believe that AI is truly revolutionary and not a hyped up normal technology.
Rare disease research have always suffered from lack of funding as pharma companies don't have incentive to find a cure for small numer of patients when there are easier ways to make money from diseases which affect larger populations.
Interestingly AI companies seem to be desperate to prove the usefulness of their product and so for the first time rare diseases are getting the spotlight, I hope this works as I live with a rare disease myself.
The bigger issue is that those programs are riskier, largely because: a) the small number of patients makes it difficult to develop a clinical trial as it is so difficult to find patients to enroll. They have a range of comorbidities and other confounding factors that are hard to control for in such a small trial. And they are spread out geographically so you have to setup and qualify a lot more trial sites and potentially deal with concomitant manufacturing/delivery issues, multiple legal regimes, etc b) the trial itself is harder to develop because there may be no good clinically validated biomarkers you can use for efficacy endpoints. These means you fallback to universal biomarkers like the patient's subjective experience of pain and the rate of death/disability, which in turn means the trials take longer to run.
I'm optimistic that some of the recent moves in the space such as the FDA's Plausible Mechanism Framework for personalized treatments will help here. I see no reason why we can't have clinical trials with a single participant.
Anyway, I hope that Tranberger's work helps find something that will help your wife.
I am not in U.S., but my govt. prefers to fund low hanging fruit w.r.t rare-disease i.e. those disease in which mechanistic details are clear and that has better probability of successful treatment; this ignores all those rare diseases which doesn't even have clear mechanistic details like GNE Myopathy.
After a long struggle, with help of corporate donors and a research institute we're now conducting a natural history study ourselves[1].
[1] https://www.gne-myopathy.org/
So dear AI labs, here's a drug-related challenge for you that might actually help: streamline the production of the top 100 generic drugs. Make them higher quality, lower variance and then race against each other to drive down the price to candy level. Show us how to build mini factories for cheap that we can build anywhere in the world.
Other large companies and foundations have copied this model and I would presume have given much more latitude in coloring outside the lines without red tape. Beyond that I am curious what will be innovative about this (sure, claude is amazing, but there's nothing stopping another grant making organization from saying here's a bunch of money and compute - hire great people and go wild)
That's where an LLM and robots can come into play potentially. If we can just scan everyone every year at their physical we could prevent potentially millions of deaths. Yea there are downsides but the downsides are all the extra tests afterwards which we would then have to make faster. Then you add extra blood work at the physical too, not just the default panel.
The equivalent of a GPU but for bio lab experiments. It should be able to do all the basic lab operations of mixing chemicals, heating, cooling, various tests etc tens of thousands of times per second.
Then you hook many AI's up to the automated lab and watch science happen far faster.
(No one please propose automated mice testing or the EAs will have an aneurysm, haha.)
Automating or streamlining human/animal trials with 24/7 monitoring and AI check-ins would be huge.
Yikes
Also Anthropic: “we’ve given AI a biological lab to develop chemicals that impact humans!”
And what we need is not simply more biological phenotypical data (such as genomics, biomarkers, etc.) that we can plug into a model. We actually need that AND labeled trial outcomes, which is a challenge in and of itself because pharma companies will often hide negative trial results (which are advantageous for training the model layer) in order to exacerbate the perception of drugs that make it to market (search for "missing data phenomenon pharma"). The clinical trials are an extremely costly (and necessary) market inefficiency that leads to a bunch of bad behavior that is unfortunately self-reinforcing. I spoke on the phone with a biotech entrepreneur who told me to think of the Gavin Belson (from Silicon Valley the TV show) quote where he says "I don't know about you, but I don't want to live in a world where someone other than me makes the world a better place better than me", and to just remember that's how many people think in pharma and biotech + pharma.
Augmenting how pharma companies work is a great first step. Augmenting how candidate drugs are engineered is a good first step. But that does not change the fact that we simply do not have deterministic governing laws (as we do in physics) to understand the complete pharmacokinetics/dynamics of a drug across a large patient population. You just have to run the test. It is true that AI will help, but only as much as we have a reasonable way to predict that candidate drugs will lead to positive trial outcomes and a large market.
Note that I am not a doomer here. I think AI will have a profoundly disruptive and positive impact on the pharma industry. I'm just not sure starting with drug design alone leads to hope unless there are things happening in the background between pharma + the labs (very possible) that could lead to better trial outcomes.
Gemini - very often when doing web searches.
A lot less these days tho.
I know Anthropic are brilliant at knowing when to focus where, but I genuinely expected them to be talking about 3-5x more serious scaled up drug discovery plays at this stage.
Is this not a focus, or did the big plays not want to be part of a roundup post?
Neural network driven drug candidate creation has been around since the 1980s. The blockbuster GLP-1 agonists were actually derived from the peptide in Gila monster venom by algorithmic search.
But you can't, and I can't emphasize this enough, just go "Claude make me a virus" and have it work. It just doesn't work that way. And the only reason why people think it does is because they haven't spent enough time in a lab, https://news.ycombinator.com/item?id=49620521
Don't get me wrong, this technology is worth fighting for because it will have incredible impact on the long time scale. But just not in ways they're talking about and expect. Nor the ways in which they're trying to dictate top down.
Some, do what it says on the tin.
Some...not so much.
The issue with any new chemical is full, comprehensive, regulated testing.
Dodgy folks aren't really so good, with that last part. They use their customers as guinea pigs.
Same applies to construction projects, BTW.
- Publicly funded researchers are on the verge of a breakthrough on some type of very important medicine. Cure for diabetes let's say.
- These researchers use Claude for some stuff. Maybe cleaning up the wording in their paper, finding relevant work to cite, whatever.
- Anthropic notices their work, and puts an army of agents on scooping them. These agents are able to make use of the researchers as yet unpublished work.
- Anthropic scoops the publicly funded researchers and patents the diabetes cure.
- Rather than being available at low prices, the cure for diabetes is put behind a paywall.
Here the stakes are much higher than the ones in the mathematical scenario we've already seen play out. We're talking life or death shit, rather than some ethical or philosophical questions about what is math really or whatever.
Don't tell me a for-profit company would never do this. We've all seen worse.
I don't believe there's any shortage of _potential_ new medicines being developed, but very few get to the stage where they are proven to be both safe and effective.
If anything Claude might just increase the downstream bottlenecks.
This is complete nonsense. There is already plenty of ML in drug research, the bottleneck is not coming up with experiments its the testing and implementation of that testing (capital!!) Llms cannot contribute anything to this process that traditional ML hasn't already.
The reason that Phase 3 trials are any sort of bottleneck instead of just checking boxes is that drugs that make it to that point are still extremely likely to fail and not be productized. The phase 3 trial is hard, expensive, and slow because it's the only strong filter we have! After phase 3 trials is the only point at which you become confident that this chemical doesn't do crazy unknown things to humans.
This is because the previous trials are not sufficient enough and high fidelity enough analogues of real human biology to be able to prevent that outcome!
Literal living human cells in a petri dish are not sufficient to tell you if a drug will work or not because the entire human system is extremely chaotic and interconnected. Even animals "99%" similar to humans are not similar enough to prevent unpredicted catastrophic outcomes in that full blown human trial.
We do not have enough knowledge anywhere about any of this for an LLM to be useful at all, because we have simply never collected the data necessary to be able to predict outcomes of novel interactions in the full scale system.
We haven't quantified the human system nearly enough.
You want to fix this? Don't go buy tokens, spend $2 trillion on grad bioscience students quantifying literally every protein and gene and structure and behavior and interaction in the human body. You literally have to brute force this problem.
You can't use "simulators" because there's no data for the sim to be evaluated and verified against in the first place. Our knowledge is too sparse.
LLMs cannot magic new information from limited data. Even magic "Superintelligent" AI wouldn't be able to fix fundamental deficiencies in what we know about the human body. There's this extremely silly and weird belief that it will be able to figure everything out "from first principles" and it's driving me up a wall.
Atom level simulations of anything in our reality are computationally infeasible at scales larger than "One cell", and you should expect that to not change since there's not expected to be a magic machine that can somehow undo computational complexity. Well, there is, in that our reality is that machine, but it's not very amenable to isolation for experimentation.
Fundamentally, there seems to be a serious fault among "AI" boosters of deeply believing the "Great man" theory of reality, that everything just needs a magically "smart" enough person to solve, as if someone clever enough could somehow trick physics itself. It's extremely ignorant of reality. There are problems in the current world that no amount of "intelligence" can solve, because the problem is not constrained by reasoning ability in the first place. A lot of bioscience fits that.
I won't judge which way, but I am getting tired of the repetitive nature here. Quite frankly, science works in large parts by others (independent) folks investigating any findings. Low quality or fake had been retracted in the past - see https://www.thelancet.com/journals/lancet/article/PIIS0140-6... from 2016.
It is time for others to do a more systematic research of anything coming out from Anthropic here.
You also can't get verified as a student, let alone as an independent learner or someone seeking to change careers, or heck just an entrepreneur with scientific interests.
Somewhere around March, Anthropic disabled their sales@ account as well. So you can't even email their sales.
I work a lot with younger scientists and people interested in science, and watching them navigate this ridiculous gatekeeping genuinely makes me sad. You can't use Claude to write grants without special dispensation from Anthropic, which is great if you're in California, but less so if you're in Tanzinia, Kazakhistan, Nepal, Bangladesh, India, South Africa, Brazil, Chile...
It's next to impossible for them to get support.
But at the same time, Anthropic can and does have a disproportionate impact on their lives. You can see that in what they brag about. Anthropic has actively bragged about blocking real research to make better painkillers and study a neglected tropical disease.
This is from their "Detecting and countering misuse of AI: September 2026" report. https://news.ycombinator.com/item?id=49647300
Here's one case study they cited, this one was called, "Case study 3: Covert frontier model access for orthopoxvirus research"
What were the researchers using Claude for? What did they block?"blocked a request for Claude’s assistance in authoring a grant application"
What was the grant being written?Note, "The grant sought to identify enhancing mutations in the chikungunya virus, engineer them into infectious clones, and select for virulence in vivo" [..] and then, "Similar research could certainly be used in the development of better vaccines and therapeutics"
It was most likely vaccine development. They stopped the study of a neglected tropical disease and vaccine development.
No Claude for the developing world, I guess.
For maximum charitability, they emphasize that they can't be sure because,
I would like to point out the most notable part, this account was used by "civilian researchers" at an "institutional affiliation associated with the grant was also a cause of concern" and the concern was that they were researchers at "performed at a military research institute."In most parts of the world, there's either strict military control over BSL-4 labs, or a mixed military-civilian hybrid model.
I doubt that researchers working in the military side of these labs looking to weaponize things are writing grants with Claude.
I really want to be charitable here, but in general, it seems that they stopped people writing grants and reports for vaccine and therapeutics research and are claiming it as "possible efforts to build biological weapons."
Because – and this is the most charitable read here – are you claiming that you stopped a nation state - one with a large military - that has an institutional BSL-4 lab staffed with biologists from making bioweapons by blocking a scientist from using Claude to do grammar checks?
It's somewhat insulting that they termed a working scientist using their chatbot to write a grant as "uplift."
Because what is Anthropic saying here? Are they saying that the only thing stopping a military from a country like India, Russia, China, Iran, South Africa etc from making bioweapons --- a technology that's more than a century old, btw --- is whether or not they can use a Claude account to write grants?
I would be more than OK with their right to create such restrictions if they weren't lobbying the US Government and other Governmetns to restrict others, thereby ensuring that they control humanity's intellectual commons well into the future.
Because who are they to say what I can learn or can't learn? Who are they to control what I can or can't do with these tools?
Who are they to say who can use Claude to generate drug discovery targets or not?
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edit - I encourage you to paste the URL to this thread into a Claude Fable session. You will be blocked for "safety."
Lol that cracked me up :D I love that job title!