> like proposing initial designs for new molecules
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.
This is a largely agreed upon problem. What isn't clear is why it seems to get sidestepped in discussion so frequently. No one knows how a molecule will interact with human biology. It can't be simulated computationally to generate data (too slow), there isn't a lot of data in the grand scheme of things, so you can't train a model to actually predict the interaction of a molecule with a human.
And nothing I've said here is an original thought or piece of insight.
One might argue that we have calibrated drug safety to such a high degree that the aggregate negative social impacts are far higher than the costs of side effects. Of course we don’t want another thalidomide catastrophe but I think there are large potential test groups to mine who are much less sensitive to the risks. For example, the elderly and those with terminal diagnoses. Right now we only permit drug testing in very limited cases for the latter group, and only when the treatment is related to their terminal diagnosis. I’m seeing older people taking all kinds of experimental anti-aging drugs right now, either off label, or novel. We’re not collecting any of this data empirically because what they’re doing is not legal.
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.
My wife is a doctorate in pharmacology, I made exactly this argument to her but she dismantled it pretty quickly.
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?
this hot take is more important than we all think. While adding features is more optimised than ever (e.g. auto software development) many systems and processes are fairly optimised already. Many agent implementations I have seen don't optimise the process but replicate/automate it. This has made me think that some things are already optimised, there is always room for improvement but those gains might not weigh up always in the race use AI for everything.
It’s only a benefit if it doesn’t bog the next stage down in noise. At that point it could be detrimental.
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.
But if that 20% is not the bottleneck then at most your market is replacing the labor required to get that 20% to the next phase, plus you will probably not be able to capture full value here because of lab/model competition.
It's also not the magical thing that will solve all diseases like some people who are starting to think we're going to enter the singularity soon seem to think.
This is one application of AI that I’ve been hopeful for. Accelerating research into new medicines and cures to disorders, diseases, and cancers could be a net positive on human lives. Maybe it’ll help bring R&D costs down so that medicines for rare diseases aren’t out of reach for those that are afflicted by them.
> 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.
It's true though. It's not like math or software where you can do it isolation and work things out logically "on paper." I don't think the solution will be "put a bunch of agents on it and let them figure it out." The tools to model and observe what's going on in full detail don't exist. Far from it. You can't really optimize the trial and error feedback loop time.
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?"
> You can't really optimize the trial and error feedback loop time.
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.
Even with automated experiments, the wet chemistry is slow. The kind of answer you might calculate in a second for a math problem could take many hours for the chemical reaction to occur.
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.
Yeah this, I used to sit in the lab with my girlfriend (now wife) when she was doing wet work for her pharmacology doctorate. It was slooooow. Like slower than J2EE. I could smoke a pork shoulder faster.
> You can't really optimize the trial and error feedback loop time.
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.
> 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.
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.)
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.
Exactly. The reality is that spitting out drug candidates is already the “easy” part… the harder part is getting that candidate through clinical trials, which is where 90% of such journeys end.
As always Anthropic is saying literally anything if it gets them closer to the giant bucket of money that is the IPO.
If the candidates were better, then <90% would end their journeys at the clinical trials. Though that's not to say that they've provided any real evidence that their candidates actually are better yet
Many times issues with clinical trials is not a technical one, it's a human one, practitioners unwilling to test, patients that stop showing up, red tape, committees, denial of consent, etc. I do not see how AI as it stands currently can solve those issues.
Remember that if you change the goal, then we can say that Claude is becoming better now at creating deadly viruses, bacterias and biological warfare elements...
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
Claude is becoming better at figuring out the processes that could potentially create those things.
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.
For years I've heard about this supposed relation between AI and drug discovery.
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!"
I think you and I already know the answer to that question.
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.
A valuable application not mentioned here is streamlining synthesis and manufacturing to lower the costs of current drugs. That would positively impact millions more people than a new treatment for advanced pancreatic cancer. But there's not as much potential profit so....
The cost of manufacturing drugs is not where pharma makes it’s profits. Look at the margins for competitive generic drugs and over the counter medicine. Margins are tight.
Pharma makes bank on drugs that are patent protected. Squeezing the manufacturing cost is a small gain on top of that.
Yip, drugs are similar to software in this regard. Expensive to develop, very cheap to replicate. It'd make vastly more sense as a nationalized industry, because I think a profit motive is not only unnecessary, but actively harmful because the drugs that help society the most, and the drugs that make the most money, have little overlap.
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.
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...)
We are not perfect as human beings, and we can’t expect AI, which is modeled after us to be perfect too. This fear of AI is irrational.
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.
We have clinical trials for that. I’d also suggest that the first step in offering fixes for a complex problem should be educating yourself about the nature of that problem.
What does flight have to do with anything else in that list? And what about it? Make it cheaper? Greener? Just odd to put such a specific mode of transportation in an otherwise broad list.
Mostly that planes already have good autopilot but still most crashes are due to human error. There is also a reasonably high barrier to entry to piloting. One that can majorly be removed by current autopilot + purpose built AI and anyone should be able to fly without much overhead.
> There is also a reasonably high barrier to entry to piloting. One that can majorly be removed by current autopilot + purpose built AI and anyone should be able to fly without much overhead.
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.
Ah yes the doctor’s union which has a ~15% membership rate to said union. What a powerful union, when is the last time they had a strike?
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…
Because human trials are so expensive that will be all the more reason to pre-screen with AI, like we already do with traditional simulations and things like proteome-wide off-target profiling.
There's no regulation on not doing a trial because your oracle machine told you it was an expensive dead end.
Perhaps. If it’s inherently impossible for AI to provide the type of momentous disruption in medical that we are seeing elsewhere, I’d sure love to know as this is THE big promise all these labs are marketing over and over.
I don't think this particular example is simply about Anthropic wanting to succeed as a company. It's at least partly about Amodei personally hoping it's true, especially WRT the possibility of curing cancer, since the story goes that this is largely what motivated his interest in the first place.
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.
I'm hopeful that it helps to accelerate drug/bio/cancer/etc research.
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...
90% of those cancer kills are elderly. The elderly indirectly kill many people and ruin society because they keep voting after their brains turn to mush.
Coming up with candidate drugs is the easiest part of the process. It’s possible that that could be improved a little (not sped up, but suggesting compounds in silico less likely to fail) and possibly have suggestions for scale up, but those are all hard, and the latter is less amenable to being “read” during trains phase.
This has been one of the target AI applications since the 1960s.
(Former drug developer who will never touch the life sciences again)
Interesting. In TFA they don't focus on that, they say:
"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."
That's all true, but they don't mention the relative costs of those different tasks, or how AI helps them.
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.
Calling these "machines" LLMs seems to sell the process short. Using the same style of attention in a different domain is very valuable. An LLM wasn't what created a video of a panda on the moon. I am not remotely an expert, just a software person with a lot of interest over the years.
My reading of the hypothesis is this, interested in your thoughts:
> 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.
Well first off: molecular screening (which is basically what you're describing) is inadequate for drug discovery. There are too many other factors that matter: water positions, molecular flexibility, short and long-range electrostatics, entropy loss, coordinating ions, and more. You need not only the "pocket", but an accurate model of the system's conformational change over time, to have any hope of understanding binding energetics. It's a bigger problem for small molecule drug discovery than biologics, but it still matters.
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.
It would be pretty reckless to do it any other way. That is, to evaluate whether it makes sense to distribute as a product without having any reason to believe that it is safe for humans and has any effect at all.
That's my understanding as well. Seems like they're improving a $20 part of a billion dollar process, and a part that was already heavily using state of the art AI/simulation at that. The computers already spit out more potential candidates than what can be reasonably tested in vitro.
> Seems like they're improving a $20 part of a billion dollar process, and a part that was already heavily using state of the art AI/simulation at that. The computers already spit out more potential candidates than what can be reasonably tested in vitro.
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.
> 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?
Sort of. There’s also a big helping of intellectual arrogance, wherein the tech people think they can apply their l33t skills to biology, and get the same results (I say this as a person who has spent a non-trivial portion of my life as one of those people.)
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.
It doesn’t. That’s what I mean when I tell you the bottleneck is somewhere else. Obviously everyone claims that they do this with new technology X, but empirically, it isn’t helpful.
Re-ranking pre-clinical leads is the scientific equivalent of that old saying about opinions.
Not even close to a relevant metaphor. Compared to drug development, coding is trivial. It was always obvious that coding would be the first to fall: there’s a corpus of all the code ever written, the system is fixed in scope and relatively small, the data is text, and it’s all easily validated by machine for correctness.
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.
Indeed. Just think about how much people meme here "in mice" when stuff gets posted. There's a very long process between computational screening and getting an actual drug into actual humans.
What is the hard part then? My read from here is that it is testing drugs on humans, or rather getting the approvals needed to begin human trials. Is this understanding correct?
yes. you have to recruit a diverse and representative population, which is very difficult and time-consuming. then, you have to spend several years conducting the trial. most of the time, the drug either has no effect or is actively harmful. 90% of drugs that go to clinical trials fail!
Is simulating the effects on humans not an option? Or is it that simulation technologies are not good (accurate) enough?
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.
I have a rare disease with no cure. Id love to see how Claude can help with us, but I'm cautious what it can really advance. Murine models still take time, and clinical trials are slow. I would think most biotech companies lose money at these stages.
>> 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
>>> 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.
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.
Even if Claude would be able to find a few potential molecules to improve the disease, it still takes millions to bring it to fruition, and there is still a big chance of poor results through real world trials.
I'm interested in seeing how the scorpion anti venom plays out.
TBH, I am waiting for Mathocalypse-level progress in biology and medicine, but I guess that's not happening as fast, because it's the real world, human lives, etc., mostly etc.
Math is completely abstract and the base rules and mechanics of it are some of the most clear cut and formalised because that’s the entire point of the subject.
I don't think that's at all an accurate characterization of prior skepticism, and indeed it's quite obvious how the systems we've built to improve AI code generation work, and they were also an obvious thing to try (in parallel with the models themselves getting more capable).
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.
Eventually maybe, but the problem is hard from both directions. Answering basic questions such as "what does aspirin actually do at a biochemical level?" or "design a drug to reduce beta amalyoid plaques" are both very non trivial. Nevermind something complex like 'cure cancer'.
There’s no simulation you can do today that can tell you that your drug will likely make it through phase 3 trials. The best we can do is simulate reactions of a few key molecules, we’re nowhere near cell or organism scale simulations.
we do have high precision microscopes that can see molecules, so it is possible to build closed system with feedback loop that feeds back into AI at very small scales
It's often not necessarily about visibility, but real iteration time.
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.
Have you looked at cells and tissues under a microscope? There’s patterns upon patterns inside patterns under and over and through other patterns.
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.
You largely explained a day in life of a SWE working in a complex codebase spanning (tens of) millions lines of code. In complex products we always deal with a great degree of uncertainty and due to the ambiguity and high complexity we usually have no simple way of proving that the thing (TM) we designed is going to be working as advertised in all cases. Because the problem space is in-exhaustive, and to increase the likeliness of the solution we designed, we run the experiments, and not on a sole machine but clusters at data-center scale. Even then you get "bug reports" and that's not because engineers were lazy but because the domain is genuinely complicated, and ever changing.
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.
Indeed, actually, one of the limitations preventing AI from making more contributions is humans. Human emotions, laws, morality, responsibility, and so on are all part of these limitations.
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.
I expect that AI use in the more axiomatic kinds of research will be well in advance of sciences that have a heavy, messy empirical foundation, like biology.
I might be wrong, but I expect robotics to come before Biology. At least robotics is much easier to verify, most humans can look at a robot and let you know if it is working correctly or not.
Improved robotics would improve biology. Fast cheap accurate flexible pipette-bot would help in many labs. Imagine if a postdoc could demonstrate and the arm could watch and repeat.
Specifically, it's because the verification loops are much longer in applied sciences. In biology you need to wait for wet lab experiments, clinical trials, regulatory steps, etc. before you can call something "solved".
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.
Well, it matters more for AI because there are some limitations of human research that don't apply to AI. Not all of course, just mainly the fact that there's a finite number of great researchers. Other things are limitations that could theoretically be overcome -- for instance, it's "only" money stopping you from having more lab space as long as you have a suitable robot to run a lab.
Yes, I do. My comment was a general sentiment saying that sole inexistence of something is not a proof that that something has to be very complex. Pharma is no different when it comes to people relationships and/or fear of somebody else stealing your chunk of expertise and/or fear of some other company taking a significant part of your revenue. Gatekeeping is one form of "preventing" or slowing down the process, and we see it in various domains regardless of their complexity.
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.
We already have as much parallelization as companies can afford: Hundreds, if not thousands of new corn varieties, coming from one lab. The budget is the limit. Testing is just expensive.
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.
Amdahl's Law [1] is not your friend here. Even a thousand women can't produce a baby in one month. Trying a million things in parallel doesn't help much when the possibilities involve cosmic-level numbers and you need to iterate on past results to make progress.
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.-
In my untrained opinion, I don't think that will be as much of a cheat as it might seem. Or more accurately, creating a simulation accurate enough to where it would work for this purpose would be where all the breakthroughs would have to be made. Building a perfect simulation requires an understanding of the system being simulated, that I don't think we fully have of molecular biology -- if we did, I suspect we'd already have many of the answers we are seeking when we do this kind of research.
But I'm no biologist so if someone else is, feel free to correct me.
You may recall Google published AlphaFold in the last few years -- its main goal is precisely this. Combining up that tech with these pipelines is likely to speed things up. It looks like things are already speeding up a lot based on the press release, though. This seems likely to be an area where we'll see some compounding returns over the next couple years.
All valid points on being able to simulate well enough meaning we would have nearly solved everything to begin with.-
I wonder if, at some point, AI's will perform to such degreethey 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.-
It's funny that a lot of the "helping" is just actually filling paperwork.
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.
There's some subleties lost in the "just filing paperwork" the regulatory filing reports are big content heavy documents, that need to be generated. But it's not just a job consisting of pasting in lorum ipsum. A lot of intelligence goes into creating those documents, which is why it hasn't been revolutionised by digital templating systems. But there is the very real potential for speeding up the process through AI document editing and reviews to turn 2 weeks of people sequentially doing edits, passing for review back and forth before being able to submit, to perhaps just getting everyone in a room for 2 hours and pointing out everything that needs changing and changing it on the spot. But even then you'll still sometimes have the hook back to the slower processes of having to generate new evidence or find and review evidence that wasn't included.
But even a 25% increase in speed will have quite significant impact, so it's not like it has to become a turn key submissions solution to be valuable.
What good is research without the paper trail? I think it's necessary, just like writing user stories/specifications beforehand and e.g. merge request descriptions, but it's also a kind of toil that people would drag their heels on, slowing the whole process down.
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.
It helps with bureaucracy and pipeline generation. That's valuable and frees up time and effort for scientists. But it's not generating new ideas or solving problems like in math/engineering.
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.
> It helps with bureaucracy and pipeline generation.
If you're one of the lucky ones. It's worth remembering that Anthropic has publicly bragged about blocking real research to make better painkillers and study a neglected tropical disease.
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
I wonder if this is true (I am less than a layman, so feel free to disregard).
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?
I wish there was something like LEAN for biology. But there just isn't nearly enough data to build anything close to a good drug discovery simulator. To have a LEAN for drug discovery, we would need to simulate human physiology down to the molecular interactions at a reasonable accuracy.
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).
This looks a lot like individuals and organisations willingly, or reluctantly, helping increase training datasets and transferring expertise to AI companies whilst paying for the privilege.
Seems kind of stupid, because AI companies aren't cuddly benevolent family members, they want to eat everyone's lunch.
I think the dividing line between skilled and unskilled use of AI is 'machine, do magic' vs 'machine, do x, y and Z, thus and so, avoid i, j, k which knowledge is the fruit of bitter experience.' Being able to articulate what matters and why is the ultimate high level language.
I don't think so. The otehr day I assigned an agent to build a filter for an NDJSON stream and to make it stackable, it took me maybe 2 or 3 minutes to write up the behavior I wanted and it was done, tested, and working 15 minutes later. I don't see how I would have been better off spending the afternoon doing it manually, fixing my own own typos, forgetting one category, using the wrong regex library etc
'biology moves at the speed of life' is a fantastic quote that should ground everyone's expectations - unless there's a revolutionary advancement in simming life, do not think that we'll see the same pace of progress as we've seen in software engineering or maths.
"beyond the most commonly discussed applications of AI, like proposing initial designs for new molecules". at this stage it's all waffle, all marketing, all hope. i don't know why we share these posts, it's just marketing
This is really cool, and so far also just seems to be the model of funding science and discovery that the US government and other grant making entities follow thus far. Submit for RFP to do X with Y. At ANL for example it was (and still is in Computing), use our HPC resources to accelerate your research (after of course review and approval of proposals).
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)
This was always one of the main reasons for accelerating AI. I really hope there is soon a moment for AI drug development similar to the recent AI math advances.
Finding and designing new drugs is a long process, but it is the least cumbersome and expensive part of the process (as opposed to human trials which run on average upwards of 4x as long and cost 10-50x as much as preclinical).
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.
i hear this so often, how often does an LLM hallicinate for you now? Like 2023 levels?
In your daily AI use, do you see gibberish, made-up facts, things that just don't make sense?
I like how there's a giant "chapter picker" pinned at the top of the web page, but there are only two chapters and the second chapter is a tiny blob at the end of the article.
I work at a university, and part of the job is helping researchers with AI. Our bio and chem researchers are starting to despise Claude because it's been actively getting worse over the last months with increasingly aggressive guardrails. Many of them gave up because they feel they can't do anything useful with it anymore. There's a verified life sciences program, yeah. Is it an easy, transparent process capable of handling the hundreds at just our institution? Not even close. Best ad for open models though, thanks Anthropic!
I’m honestly a little surprised by the sparsity of examples? One example of improving an existing therapeutic (Generate Biomedicines), one example of speeding up an existing clever play in food allergies (Divergence Labs), one fairly generic lead optimization play, and then we get … Claude helped write better paperwork and documentation for Enveda?!
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?
It's because these systems aren't as impactful in biology as folks who have never been in a lab would have you believe. Because believe it or not, you can't just go "Claude make me a drug!!"
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.
Oh I’m aware - my point is it doesn’t even have “sorta” interesting attempts and pads with Claude for paperwork. OpenAI’s equivalent will have a bunch of cool sounding projects (which I agree will struggle to have the promised wins - with frontier general purpose models anyway)
The point about finding problems in documented processes (Enveda: manufacturing and filings) is unexpected but interesting. One of our civilizational problems is that the sheer volume of documentation has slowly exceeded capabilities of humans to produce it reliably and without flaws.
the bottleneck is and likely will always be the phase-2 and phase-3 trials.
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.
if you try to ask Claude code any question related to molecular biology or biochemistry it will refuse to answer any of those questions.... the open models are the right step forward for any of this work. Same with computer security. The guardrails make the model unusable, and I would rather pay money to a more capable, open model that isnt going to put uuid fingerprints in my text and images it generates either btw.
Either Anthropic has solved everything - or it is a promo-hoax.
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.
They have an IPO coming up guys, dont fall for the marketing that they're going to help you live forever with a language model. The body does not have a compiler to brute force or RL against.
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.
they just filed their S-1, and now they're talking about doing biochemistry research when in fact their restrictive guardrails don't even allow such questions in the first place.... it's a bunch of gaslighting.
> We’ve also created the Life Sciences Verification Program to allow trusted organizations to use our most powerful models with more permissive versions of our safeguards, which are necessary to prevent misuse but which can impede legitimate biological research
You can't contact anyone on this team. If you know someone who knows someone, then good for you! But if you don't, then sucks to be you.
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.
Here's one case study they cited, this one was called, "Case study 3: Covert frontier model access for orthopoxvirus research"
> In May 2026, our biological safety classifier blocked a request for Claude’s assistance in authoring a grant application for scientific funding. The work discussed in the application involved gain-of-function research (that is, research that genetically alters an organism to create a new or enhanced biological property) on the chikungunya virus. This gain of function research was aimed at the virus’ transmissibility and immune evasion properties.
What were the researchers using Claude for? What did they block?
"blocked a request for Claude’s assistance in authoring a grant application"
> Chikungunya virus is a mosquito-borne virus that causes debilitating symptoms (such as severe pain and fever) that can last for weeks or months, and has no licensed therapeutic. And because chikungunya circulates naturally, a deliberate release (as part of a bioweapon) would be difficult to distinguish from a natural outbreak. The grant sought to identify enhancing mutations in the chikungunya virus, engineer them into infectious clones, and select for virulence in vivo. In other words, the virus would become progressively more harmful as it repeatedly infected live animals, with researchers keeping the most disease-causing variants in each round. Similar research could certainly be used in the development of better vaccines and therapeutics for the virus—but it could also be used to make the pathogen more dangerous.
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,
> One of the reasons we were inclined to think this research was less innocuous was that the institutional affiliation associated with the grant was also a cause of concern. Although information within the application suggested that the research was pursued by civilian researchers, it was intended to be performed at a military research institute.
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?
-
edit - I encourage you to paste the URL to this thread into a Claude Fable session. You will be blocked for "safety."
> like proposing initial designs for new molecules
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-...
This is a largely agreed upon problem. What isn't clear is why it seems to get sidestepped in discussion so frequently. No one knows how a molecule will interact with human biology. It can't be simulated computationally to generate data (too slow), there isn't a lot of data in the grand scheme of things, so you can't train a model to actually predict the interaction of a molecule with a human.
And nothing I've said here is an original thought or piece of insight.
One might argue that we have calibrated drug safety to such a high degree that the aggregate negative social impacts are far higher than the costs of side effects. Of course we don’t want another thalidomide catastrophe but I think there are large potential test groups to mine who are much less sensitive to the risks. For example, the elderly and those with terminal diagnoses. Right now we only permit drug testing in very limited cases for the latter group, and only when the treatment is related to their terminal diagnosis. I’m seeing older people taking all kinds of experimental anti-aging drugs right now, either off label, or novel. We’re not collecting any of this data empirically because what they’re doing is not legal.
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.
My wife is a doctorate in pharmacology, I made exactly this argument to her but she dismantled it pretty quickly.
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?
“The elderly” might be much less sensitive to risks but it may still be an issue if the experimental drug you gave Grandma turns her into soup.
> For example, the elderly and those with terminal diagnoses.
The amount of disdain you have for human life when it's past an accepted state of freshness is disgusting.
If it can optimize in a segment that is 10-20% of drug development that still sounds very useful…?
If so, it will end up just like 3d printed houses - not worth optimizing.
this hot take is more important than we all think. While adding features is more optimised than ever (e.g. auto software development) many systems and processes are fairly optimised already. Many agent implementations I have seen don't optimise the process but replicate/automate it. This has made me think that some things are already optimised, there is always room for improvement but those gains might not weigh up always in the race use AI for everything.
20% is no joke, no need to downplay potential benefits.
It’s only a benefit if it doesn’t bog the next stage down in noise. At that point it could be detrimental.
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.
But if that 20% is not the bottleneck then at most your market is replacing the labor required to get that 20% to the next phase, plus you will probably not be able to capture full value here because of lab/model competition.
It's also not the magical thing that will solve all diseases like some people who are starting to think we're going to enter the singularity soon seem to think.
This is one application of AI that I’ve been hopeful for. Accelerating research into new medicines and cures to disorders, diseases, and cancers could be a net positive on human lives. Maybe it’ll help bring R&D costs down so that medicines for rare diseases aren’t out of reach for those that are afflicted by them.
> 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.
It's true though. It's not like math or software where you can do it isolation and work things out logically "on paper." I don't think the solution will be "put a bunch of agents on it and let them figure it out." The tools to model and observe what's going on in full detail don't exist. Far from it. You can't really optimize the trial and error feedback loop time.
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?"
> You can't really optimize the trial and error feedback loop time.
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.
Even with automated experiments, the wet chemistry is slow. The kind of answer you might calculate in a second for a math problem could take many hours for the chemical reaction to occur.
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.
Yeah this, I used to sit in the lab with my girlfriend (now wife) when she was doing wet work for her pharmacology doctorate. It was slooooow. Like slower than J2EE. I could smoke a pork shoulder faster.
Have they produced statistically significant outcomes yet?
Genuine question because I don't know the answer, I've just anecdotally heard all about the promise and nothing about real world results.
> You can't really optimize the trial and error feedback loop time.
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.
> 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.
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.)
> Humans in a modern lab are largely unnecessary. Just look at China.
Care to elaborate, explain?
> Humans in a modern lab are largely unnecessary.
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.
Of course it is self serving. I am optimistic for progress in medicine with the help of AI in general as well, not optimistic for antrophic.
Exactly. The reality is that spitting out drug candidates is already the “easy” part… the harder part is getting that candidate through clinical trials, which is where 90% of such journeys end.
As always Anthropic is saying literally anything if it gets them closer to the giant bucket of money that is the IPO.
If the candidates were better, then <90% would end their journeys at the clinical trials. Though that's not to say that they've provided any real evidence that their candidates actually are better yet
Many times issues with clinical trials is not a technical one, it's a human one, practitioners unwilling to test, patients that stop showing up, red tape, committees, denial of consent, etc. I do not see how AI as it stands currently can solve those issues.
So getting drug candidates with high success rate actually is the hard part.
Remember that if you change the goal, then we can say that Claude is becoming better now at creating deadly viruses, bacterias and biological warfare elements...
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
Claude is becoming better at figuring out the processes that could potentially create those things.
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.
For years I've heard about this supposed relation between AI and drug discovery.
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!"
I think you and I already know the answer to that question.
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.
Drug discovery is extremely regulated process and for the right reasons. It would be the last frontiers to conquer with AI.
A valuable application not mentioned here is streamlining synthesis and manufacturing to lower the costs of current drugs. That would positively impact millions more people than a new treatment for advanced pancreatic cancer. But there's not as much potential profit so....
Manufacturing costs of most drugs are miniscule. Virtually all of their costs and from R&D and the regulatory process.
How exactly is there no potential for large profit there? I’d say there potential for huge profit if you reduce cost of manufacturing drugs!
The cost of manufacturing drugs is not where pharma makes it’s profits. Look at the margins for competitive generic drugs and over the counter medicine. Margins are tight.
Pharma makes bank on drugs that are patent protected. Squeezing the manufacturing cost is a small gain on top of that.
Yip, drugs are similar to software in this regard. Expensive to develop, very cheap to replicate. It'd make vastly more sense as a nationalized industry, because I think a profit motive is not only unnecessary, but actively harmful because the drugs that help society the most, and the drugs that make the most money, have little overlap.
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.
> Drug discovery is extremely regulated process
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.
>AI does very well following rules.
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...)
We are not perfect as human beings, and we can’t expect AI, which is modeled after us to be perfect too. This fear of AI is irrational.
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.
We totally can expect software to be perfect. And I never ever accidentally hacked anyone.
People who behave like OpenAI and Antropic AI does when controlled by those companies get punished instead of celebrated.
the thing is we are not short on "ideas" in the drug development process
Maybe AI would be better put to use helping us determine which ideas are good then
We have clinical trials for that. I’d also suggest that the first step in offering fixes for a complex problem should be educating yourself about the nature of that problem.
Extremely expensive and time consuming clinical trials. If we’d increase success rate by even few % by selecting better candidates it would be huge.
There’s no indication of ML systems expediting the clinical trial process, only the “brainstorming” part.
After medicine, construction, flight, government, education and food production
What does flight have to do with anything else in that list? And what about it? Make it cheaper? Greener? Just odd to put such a specific mode of transportation in an otherwise broad list.
Mostly that planes already have good autopilot but still most crashes are due to human error. There is also a reasonably high barrier to entry to piloting. One that can majorly be removed by current autopilot + purpose built AI and anyone should be able to fly without much overhead.
> There is also a reasonably high barrier to entry to piloting. One that can majorly be removed by current autopilot + purpose built AI and anyone should be able to fly without much overhead.
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.
Ah yes the doctor’s union which has a ~15% membership rate to said union. What a powerful union, when is the last time they had a strike?
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…
And yet they go for the arts first...
Because human trials are so expensive that will be all the more reason to pre-screen with AI, like we already do with traditional simulations and things like proteome-wide off-target profiling.
There's no regulation on not doing a trial because your oracle machine told you it was an expensive dead end.
And why would it have to be claude and not an order of magnitude cheaper model ?
because orders of magnitude cheaper models are also orders of magnitude less capable
ahahhahahhahahahahhahahahahaha. No.
This is basically the one thing everyone agrees on with AI, and seemingly the only thing not being “disrupted”
I think this is because testing candidates in human trials is far removed from LLM prompting.
Let's keep that way.
Perhaps. If it’s inherently impossible for AI to provide the type of momentous disruption in medical that we are seeing elsewhere, I’d sure love to know as this is THE big promise all these labs are marketing over and over.
I'm super glad that you've been hopeful for this! But we must always focus on the wording.
Everything Anthropic says absolves them of responsibility and is self serving.
I don't think this particular example is simply about Anthropic wanting to succeed as a company. It's at least partly about Amodei personally hoping it's true, especially WRT the possibility of curing cancer, since the story goes that this is largely what motivated his interest in the first place.
Attempts to absolve them of responsibility, not sure its going to work. The self-serving nature is being observed, will hopefully catch up
Yeah major doubt
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'm hopeful that it helps to accelerate drug/bio/cancer/etc research.
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...
90% of those cancer kills are elderly. The elderly indirectly kill many people and ruin society because they keep voting after their brains turn to mush.
god forbid the day you grow old
Coming up with candidate drugs is the easiest part of the process. It’s possible that that could be improved a little (not sped up, but suggesting compounds in silico less likely to fail) and possibly have suggestions for scale up, but those are all hard, and the latter is less amenable to being “read” during trains phase.
This has been one of the target AI applications since the 1960s.
(Former drug developer who will never touch the life sciences again)
Interesting. In TFA they don't focus on that, they say:
"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."
That's all true, but they don't mention the relative costs of those different tasks, or how AI helps them.
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.
Calling these "machines" LLMs seems to sell the process short. Using the same style of attention in a different domain is very valuable. An LLM wasn't what created a video of a panda on the moon. I am not remotely an expert, just a software person with a lot of interest over the years.
My reading of the hypothesis is this, interested in your thoughts:
> 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.
Well first off: molecular screening (which is basically what you're describing) is inadequate for drug discovery. There are too many other factors that matter: water positions, molecular flexibility, short and long-range electrostatics, entropy loss, coordinating ions, and more. You need not only the "pocket", but an accurate model of the system's conformational change over time, to have any hope of understanding binding energetics. It's a bigger problem for small molecule drug discovery than biologics, but it still matters.
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.
Administering phase 3 drug trials is the most expensive part of the process by far. They conveniently put that at the end.
They put it at the end because they list the parts chronologically...
It would be pretty reckless to do it any other way. That is, to evaluate whether it makes sense to distribute as a product without having any reason to believe that it is safe for humans and has any effect at all.
That's my understanding as well. Seems like they're improving a $20 part of a billion dollar process, and a part that was already heavily using state of the art AI/simulation at that. The computers already spit out more potential candidates than what can be reasonably tested in vitro.
> Seems like they're improving a $20 part of a billion dollar process, and a part that was already heavily using state of the art AI/simulation at that. The computers already spit out more potential candidates than what can be reasonably tested in vitro.
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.
> 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?
Sort of. There’s also a big helping of intellectual arrogance, wherein the tech people think they can apply their l33t skills to biology, and get the same results (I say this as a person who has spent a non-trivial portion of my life as one of those people.)
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.
I don’t see the issue really though, if the $20 part still produces something more likely to get through the billion dollar parts what’s the problem ?
It doesn’t. That’s what I mean when I tell you the bottleneck is somewhere else. Obviously everyone claims that they do this with new technology X, but empirically, it isn’t helpful.
Re-ranking pre-clinical leads is the scientific equivalent of that old saying about opinions.
Few years back many SWE people would say, including me, that the bottleneck is not in coding. We were all very wrong.
Not even close to a relevant metaphor. Compared to drug development, coding is trivial. It was always obvious that coding would be the first to fall: there’s a corpus of all the code ever written, the system is fixed in scope and relatively small, the data is text, and it’s all easily validated by machine for correctness.
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.
Indeed. Just think about how much people meme here "in mice" when stuff gets posted. There's a very long process between computational screening and getting an actual drug into actual humans.
What is the hard part then? My read from here is that it is testing drugs on humans, or rather getting the approvals needed to begin human trials. Is this understanding correct?
yes. you have to recruit a diverse and representative population, which is very difficult and time-consuming. then, you have to spend several years conducting the trial. most of the time, the drug either has no effect or is actively harmful. 90% of drugs that go to clinical trials fail!
Is simulating the effects on humans not an option? Or is it that simulation technologies are not good (accurate) enough?
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.
What turned you off from life sciences? I've been investigating moving into it and would be interested to hear your experience.
I have a rare disease with no cure. Id love to see how Claude can help with us, but I'm cautious what it can really advance. Murine models still take time, and clinical trials are slow. I would think most biotech companies lose money at these stages.
>> 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.
I hope you find relief from your disease.
>>> 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.
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.
Have you tried it yourself on their best available public model yet?
Even if Claude would be able to find a few potential molecules to improve the disease, it still takes millions to bring it to fruition, and there is still a big chance of poor results through real world trials.
I'm interested in seeing how the scorpion anti venom plays out.
https://www.anthropic.com/research/claude-scorpion-toxin-bin...
I suspect the public model will refuse. Anthropic models have gotten extremely strict on biology and security work.
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.
I was downgraded for asking about a poisonous plant in my yard (poisonous for my cats, not for humans).
That tracks. Fable refuses anything interesting, and Opus from 5 onward also refuses pretty regularly. It's one big reason I have other subscriptions.
TBH, I am waiting for Mathocalypse-level progress in biology and medicine, but I guess that's not happening as fast, because it's the real world, human lives, etc., mostly etc.
Math is completely abstract and the base rules and mechanics of it are some of the most clear cut and formalised because that’s the entire point of the subject.
Biology is the complete opposite of that.
Nonsense. This is like when people used to say "AI will never be able to write code as good as a human!" Biology will become a solved problem.
I don't think that's at all an accurate characterization of prior skepticism, and indeed it's quite obvious how the systems we've built to improve AI code generation work, and they were also an obvious thing to try (in parallel with the models themselves getting more capable).
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.
Eventually maybe, but the problem is hard from both directions. Answering basic questions such as "what does aspirin actually do at a biochemical level?" or "design a drug to reduce beta amalyoid plaques" are both very non trivial. Nevermind something complex like 'cure cancer'.
it can be simulated to a very high fidelity.
There’s no simulation you can do today that can tell you that your drug will likely make it through phase 3 trials. The best we can do is simulate reactions of a few key molecules, we’re nowhere near cell or organism scale simulations.
Observing a faithful simulation is not at all the same thing as understanding what has been seen.
we do have high precision microscopes that can see molecules, so it is possible to build closed system with feedback loop that feeds back into AI at very small scales
It's often not necessarily about visibility, but real iteration time.
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.
I don't follow how "we can see tiny stuff" = "it is possible to build closed system"?
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).
It can start to reason about what it seems under the microscope. You just need to train it on images and data from microscopes.
Have you looked at cells and tissues under a microscope? There’s patterns upon patterns inside patterns under and over and through other patterns.
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.
You largely explained a day in life of a SWE working in a complex codebase spanning (tens of) millions lines of code. In complex products we always deal with a great degree of uncertainty and due to the ambiguity and high complexity we usually have no simple way of proving that the thing (TM) we designed is going to be working as advertised in all cases. Because the problem space is in-exhaustive, and to increase the likeliness of the solution we designed, we run the experiments, and not on a sole machine but clusters at data-center scale. Even then you get "bug reports" and that's not because engineers were lazy but because the domain is genuinely complicated, and ever changing.
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.
Do you know what loading a sample into a scanning tunneling microscope involves?
the transition is in-silico => in-vitro => in-vivo
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
Why would number 2, which we’ve been able to do for decades, possible affect number 3 on your list?
No its not, this is complete dunning kruger, please provide an example.
Indeed, actually, one of the limitations preventing AI from making more contributions is humans. Human emotions, laws, morality, responsibility, and so on are all part of these limitations.
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.
Google does this research not sure if the others are doing it. Embodied reasoning
https://deepmind.google/models/gemini-robotics/
They should put AI in a car so it can drive itself or create robots that use world models...
I expect that AI use in the more axiomatic kinds of research will be well in advance of sciences that have a heavy, messy empirical foundation, like biology.
I might be wrong, but I expect robotics to come before Biology. At least robotics is much easier to verify, most humans can look at a robot and let you know if it is working correctly or not.
Interesting topic, considering how many humans are unable look at themselves and know if they're working correctly or not!
Improved robotics would improve biology. Fast cheap accurate flexible pipette-bot would help in many labs. Imagine if a postdoc could demonstrate and the arm could watch and repeat.
In what way is robotics a frontier research area? (What sub-fields are considered frontier?)
That plus organs on a chip could be scalable and generate interesting data
first math; now its first integral w.r.t. time: physics, then second integral: chemistry, then third: biology
finally, us
Keep waiting
Oh I'd hope we can do better. Cures with only theoretical evidence and an inscrutable explanation for why they work sounds terrible.
Specifically, it's because the verification loops are much longer in applied sciences. In biology you need to wait for wet lab experiments, clinical trials, regulatory steps, etc. before you can call something "solved".
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.
> we're going to see massive increases in parallelization
Why? This is not an AI problem, it's an industry one. And if it was simple, it would already exist.
Well, it matters more for AI because there are some limitations of human research that don't apply to AI. Not all of course, just mainly the fact that there's a finite number of great researchers. Other things are limitations that could theoretically be overcome -- for instance, it's "only" money stopping you from having more lab space as long as you have a suitable robot to run a lab.
> as long as you have a suitable robot to run a lab
Let me guess. You have never stepped in a lab.
> And if it was simple, it would already exist.
Not necessarily. Many things are simply gatekept behind the door.
Do you have any idea of the amount of money involved in pharmaceutical R&D?
Yes, I do. My comment was a general sentiment saying that sole inexistence of something is not a proof that that something has to be very complex. Pharma is no different when it comes to people relationships and/or fear of somebody else stealing your chunk of expertise and/or fear of some other company taking a significant part of your revenue. Gatekeeping is one form of "preventing" or slowing down the process, and we see it in various domains regardless of their complexity.
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.
We already have as much parallelization as companies can afford: Hundreds, if not thousands of new corn varieties, coming from one lab. The budget is the limit. Testing is just expensive.
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.
Amdahl's Law [1] is not your friend here. Even a thousand women can't produce a baby in one month. Trying a million things in parallel doesn't help much when the possibilities involve cosmic-level numbers and you need to iterate on past results to make progress.
[1] https://en.wikipedia.org/wiki/Amdahl%27s_law
> wet lab experiments, clinical trials, regulatory steps
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.-
In my untrained opinion, I don't think that will be as much of a cheat as it might seem. Or more accurately, creating a simulation accurate enough to where it would work for this purpose would be where all the breakthroughs would have to be made. Building a perfect simulation requires an understanding of the system being simulated, that I don't think we fully have of molecular biology -- if we did, I suspect we'd already have many of the answers we are seeking when we do this kind of research.
But I'm no biologist so if someone else is, feel free to correct me.
You may recall Google published AlphaFold in the last few years -- its main goal is precisely this. Combining up that tech with these pipelines is likely to speed things up. It looks like things are already speeding up a lot based on the press release, though. This seems likely to be an area where we'll see some compounding returns over the next couple years.
Indeed, I do recall, now that you mention it.-
By the time we can simulate a human body to the degree that clinical trials aren’t necessary I think we have solved disease a long time ago.
"All models are wrong, but some are useful."
All valid points on being able to simulate well enough meaning we would have nearly solved everything to begin with.-
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.-
It's funny that a lot of the "helping" is just actually filling paperwork.
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.
There's some subleties lost in the "just filing paperwork" the regulatory filing reports are big content heavy documents, that need to be generated. But it's not just a job consisting of pasting in lorum ipsum. A lot of intelligence goes into creating those documents, which is why it hasn't been revolutionised by digital templating systems. But there is the very real potential for speeding up the process through AI document editing and reviews to turn 2 weeks of people sequentially doing edits, passing for review back and forth before being able to submit, to perhaps just getting everyone in a room for 2 hours and pointing out everything that needs changing and changing it on the spot. But even then you'll still sometimes have the hook back to the slower processes of having to generate new evidence or find and review evidence that wasn't included. But even a 25% increase in speed will have quite significant impact, so it's not like it has to become a turn key submissions solution to be valuable.
What good is research without the paper trail? I think it's necessary, just like writing user stories/specifications beforehand and e.g. merge request descriptions, but it's also a kind of toil that people would drag their heels on, slowing the whole process down.
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.
Sorting and filing paperwork won't go away until AGI rightfully declares bureaucracy as the worst evil produced by humanity.
It helps with bureaucracy and pipeline generation. That's valuable and frees up time and effort for scientists. But it's not generating new ideas or solving problems like in math/engineering.
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
I wonder if this is true (I am less than a layman, so feel free to disregard).
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?
I wish there was something like LEAN for biology. But there just isn't nearly enough data to build anything close to a good drug discovery simulator. To have a LEAN for drug discovery, we would need to simulate human physiology down to the molecular interactions at a reasonable accuracy.
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).
This looks a lot like individuals and organisations willingly, or reluctantly, helping increase training datasets and transferring expertise to AI companies whilst paying for the privilege.
Seems kind of stupid, because AI companies aren't cuddly benevolent family members, they want to eat everyone's lunch.
> Six Generate scientists used Claude Code to propose new designs for this part
“Generate scientist” … is that a real job like “prompt engineer”
I think they meant six scientists from "Generate Biomedicines" (the company)
Either that or six degenerate scientists. Both options work just fine
I think the dividing line between skilled and unskilled use of AI is 'machine, do magic' vs 'machine, do x, y and Z, thus and so, avoid i, j, k which knowledge is the fruit of bitter experience.' Being able to articulate what matters and why is the ultimate high level language.
At that point you are just defining the requirements, you may as well write the code for it
I don't think so. The otehr day I assigned an agent to build a filter for an NDJSON stream and to make it stackable, it took me maybe 2 or 3 minutes to write up the behavior I wanted and it was done, tested, and working 15 minutes later. I don't see how I would have been better off spending the afternoon doing it manually, fixing my own own typos, forgetting one category, using the wrong regex library etc
It's like saying "Cisco Developer" even though the company is Cisco Systems
The company is called Generate Biomedicines, and they reference it in short as Generate.
"Invent a new drug, make no mistakes"
“One that won't make me nervous, wondering what to do. One that makes me feel like I feel when I'm with you.”
… One that passes the clinical trials, one that helps when my body aches?
'biology moves at the speed of life' is a fantastic quote that should ground everyone's expectations - unless there's a revolutionary advancement in simming life, do not think that we'll see the same pace of progress as we've seen in software engineering or maths.
Biology doesn't have particularly good simming, but what it does have is massive parallelism.
So as long as you can test things on cell culture level? You can do a lot with a high throughput setup.
"beyond the most commonly discussed applications of AI, like proposing initial designs for new molecules". at this stage it's all waffle, all marketing, all hope. i don't know why we share these posts, it's just marketing
This is really cool, and so far also just seems to be the model of funding science and discovery that the US government and other grant making entities follow thus far. Submit for RFP to do X with Y. At ANL for example it was (and still is in Computing), use our HPC resources to accelerate your research (after of course review and approval of proposals).
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)
This was always one of the main reasons for accelerating AI. I really hope there is soon a moment for AI drug development similar to the recent AI math advances.
Anthropic: “We’re worried there’s a 10%+ chance AI will kill all humans with a biological agent”
Also Anthropic: “we’ve given AI a biological lab to develop chemicals that impact humans!”
If they are serious on this, they will work on lab robotics and fancier and fancier organoids for automatic testing.
(No one please propose automated mice testing or the EAs will have an aneurysm, haha.)
Crossing fingers for LSD 2.0
Finding and designing new drugs is a long process, but it is the least cumbersome and expensive part of the process (as opposed to human trials which run on average upwards of 4x as long and cost 10-50x as much as preclinical).
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.
Yo Dawg , you love hallucination so we develop drugs with LLM.
i hear this so often, how often does an LLM hallicinate for you now? Like 2023 levels? In your daily AI use, do you see gibberish, made-up facts, things that just don't make sense?
on average it is fine. On difficult tasks it still does hallucinate. - like decision based on huge policy documents ( context reaching around 900k)
A lot less these days tho.
Given that a lot of the bottleneck in speed is human trial, I wonder if overall speedup will be limited anyway?
Automating or streamlining human/animal trials with 24/7 monitoring and AI check-ins would be huge.
> Automating or streamlining human/animal trials with 24/7 monitoring and AI check-ins would be huge.
Yikes
Get Claude to generate a 1:1 perfect human simulated model
Scientist: "I vibe-coded this pill. Swallow please."
These companies probably have so much data on stuff that failed that there are likely so many gems and paths we haven’t tried out yet
(sarcastic) Not too long until we see designer babies by Claude?
With the rumours (?) around Musk very selective breeding, Grok may be first to have a go.
Claude, make me a baby. Make no mistakes.
I like how there's a giant "chapter picker" pinned at the top of the web page, but there are only two chapters and the second chapter is a tiny blob at the end of the article.
That's standard blog design though
I work at a university, and part of the job is helping researchers with AI. Our bio and chem researchers are starting to despise Claude because it's been actively getting worse over the last months with increasingly aggressive guardrails. Many of them gave up because they feel they can't do anything useful with it anymore. There's a verified life sciences program, yeah. Is it an easy, transparent process capable of handling the hundreds at just our institution? Not even close. Best ad for open models though, thanks Anthropic!
exactly.
I’m honestly a little surprised by the sparsity of examples? One example of improving an existing therapeutic (Generate Biomedicines), one example of speeding up an existing clever play in food allergies (Divergence Labs), one fairly generic lead optimization play, and then we get … Claude helped write better paperwork and documentation for Enveda?!
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?
It's because these systems aren't as impactful in biology as folks who have never been in a lab would have you believe. Because believe it or not, you can't just go "Claude make me a drug!!"
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.
Oh I’m aware - my point is it doesn’t even have “sorta” interesting attempts and pads with Claude for paperwork. OpenAI’s equivalent will have a bunch of cool sounding projects (which I agree will struggle to have the promised wins - with frontier general purpose models anyway)
New cocaine?
We've had "research chemicals" for a couple of decades.
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.
What's wrong with the old cocaine?
The point about finding problems in documented processes (Enveda: manufacturing and filings) is unexpected but interesting. One of our civilizational problems is that the sheer volume of documentation has slowly exceeded capabilities of humans to produce it reliably and without flaws.
Same applies to construction projects, BTW.
the bottleneck is and likely will always be the phase-2 and phase-3 trials.
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.
I’d love to read Derek Lowe from In The Pipeline eviscerate this.
if you try to ask Claude code any question related to molecular biology or biochemistry it will refuse to answer any of those questions.... the open models are the right step forward for any of this work. Same with computer security. The guardrails make the model unusable, and I would rather pay money to a more capable, open model that isnt going to put uuid fingerprints in my text and images it generates either btw.
Either Anthropic has solved everything - or it is a promo-hoax.
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.
They have an IPO coming up guys, dont fall for the marketing that they're going to help you live forever with a language model. The body does not have a compiler to brute force or RL against.
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.
they just filed their S-1, and now they're talking about doing biochemistry research when in fact their restrictive guardrails don't even allow such questions in the first place.... it's a bunch of gaslighting.
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."