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.
dyauspitr · 2026-10-27 21:30:00 UTC
Have you tried it yourself on their best available public model yet?
SwellJoe · 2026-10-27 21:58:04 UTC
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.
rtsil · 2026-10-27 22:25:45 UTC
I was downgraded for asking about a poisonous plant in my yard (poisonous for my cats, not for humans).
SwellJoe · 2026-10-27 22:50:13 UTC
That tracks. Fable refuses anything interesting, and Opus from 5 onward also refuses pretty regularly. It's one big reason I have other subscriptions.
adamredwoods · 2026-10-27 22:39:23 UTC
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.
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.
vatsachak · 2026-10-27 21:39:36 UTC
That's standard blog design though
Sarkie · 2026-10-27 21:28:58 UTC
New cocaine?
ChrisMarshallNY · 2026-10-27 21:47:57 UTC
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.
yewenjie · 2026-10-27 21:31:37 UTC
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.
redanddead · 2026-10-27 21:37:46 UTC
That plus organs on a chip could be scalable and generate interesting data
enraged_camel · 2026-10-27 21:38:31 UTC
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.
slashdave · 2026-10-27 21:41:56 UTC
> 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.
menaerus · 2026-10-27 21:49:14 UTC
> And if it was simple, it would already exist.
Not necessarily. Many things are simply gatekept behind the door.
xp84 · 2026-10-27 22:04:27 UTC
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.
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.-
xp84 · 2026-10-27 22:08:11 UTC
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.
jurgenburgen · 2026-10-27 22:08:34 UTC
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.
neuronexmachina · 2026-10-27 22:57:19 UTC
"All models are wrong, but some are useful."
vessenes · 2026-10-27 22:39:04 UTC
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.
Bluestein · 2026-10-27 22:41:34 UTC
Indeed, I do recall, now that you mention it.-
Bluestein · 2026-10-27 22:41:06 UTC
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.-
slashdave · 2026-10-27 21:41:04 UTC
Keep waiting
xhevahir · 2026-10-27 21:43:01 UTC
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.
dennysora-main · 2026-10-27 21:57:38 UTC
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.
aleksiy123 · 2026-10-27 22:07:00 UTC
Google does this research not sure if the others are doing it. Embodied reasoning
They should put AI in a car so it can drive itself or create robots that use world models...
ungovernableCat · 2026-10-27 22:23:27 UTC
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.
bijowo1676 · 2026-10-27 23:27:33 UTC
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
hungryhobbit · 2026-10-27 23:34:30 UTC
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).
sumanthvepa · 2026-10-27 23:56:05 UTC
It can start to reason about what it seems under the microscope. You just need to train it on images and data from microscopes.
bijowo1676 · 2026-10-28 00:03:36 UTC
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
lifeisloving · 2026-10-27 23:35:33 UTC
No its not, this is complete dunning kruger, please provide an example.
jareklupinski · 2026-10-27 22:33:17 UTC
first math; now its first integral w.r.t. time: physics, then second integral: chemistry, then third: biology
finally, us
shiandow · 2026-10-27 23:00:15 UTC
Oh I'd hope we can do better. Cures with only theoretical evidence and an inscrutable explanation for why they work sounds terrible.
sashank_1509 · 2026-10-27 23:05:39 UTC
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.
DANmode · 2026-10-27 23:23:43 UTC
Interesting topic, considering how many humans are unable look at themselves and know if they're working correctly or not!
fn-mote · 2026-10-27 23:55:01 UTC
In what way is robotics a frontier research area? (What sub-fields are considered frontier?)
ForHackernews · 2026-10-27 23:55:57 UTC
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.
jon128 · 2026-10-27 21:36:59 UTC
Crossing fingers for LSD 2.0
ruther4d · 2026-10-27 21:41:01 UTC
> Six Generate scientists used Claude Code to propose new designs for this part
“Generate scientist” … is that a real job like “prompt engineer”
traceroute66 · 2026-10-27 21:45:28 UTC
"Invent a new drug, make no mistakes"
mysterydip · 2026-10-27 21:54:49 UTC
“One that won't make me nervous, wondering what to do.
One that makes me feel like I feel when I'm with you.”
anigbrowl · 2026-10-27 21:51:31 UTC
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.
gonzalohm · 2026-10-27 22:00:25 UTC
At that point you are just defining the requirements, you may as well write the code for it
gonzalohm · 2026-10-27 22:03:54 UTC
I think they meant six scientists from "Generate Biomedicines" (the company)
Either that or six degenerate scientists. Both options work just fine
klustregrif · 2026-10-27 22:26:36 UTC
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.
inglor_cz · 2026-10-27 21:41:55 UTC
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.
fwlr · 2026-10-27 21:51:38 UTC
Claude, you are a senior drug dealer. Get me my fix, make no mistakes.
throw-qqqqq · 2026-10-27 22:20:40 UTC
> senior drug dealer
Lol that cracked me up :D I love that job title!
ngai_aku · 2026-10-27 22:23:39 UTC
Not trying to be a dick, but I think this community is better off without low effort jokes like this littering the post.
gonzalohm · 2026-10-27 22:07:22 UTC
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.
1970-01-01 · 2026-10-27 22:16:29 UTC
Sorting and filing paperwork won't go away until AGI rightfully declares bureaucracy as the worst evil produced by humanity.
klustregrif · 2026-10-27 22:31:21 UTC
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.
orthogonal_cube · 2026-10-27 22:10:38 UTC
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.
lukan · 2026-10-27 22:41:37 UTC
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.
talon8635 · 2026-10-27 22:43:40 UTC
This is basically the one thing everyone agrees on with AI, and seemingly the only thing not being “disrupted”
josh-wrale · 2026-10-27 23:04:44 UTC
I think this is because testing candidates in human trials is far removed from LLM prompting.
mlboss · 2026-10-27 23:18:28 UTC
Drug discovery is extremely regulated process and for the right reasons. It would be the last frontiers to conquer with AI.
gnabgib · 2026-10-27 23:21:30 UTC
After medicine, construction, flight, government, education and food production
judge2020 · 2026-10-27 23:49:41 UTC
And yet they go for the arts first...
cma · 2026-10-27 23:26:59 UTC
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.
fn-mote · 2026-10-27 23:52:29 UTC
> 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.
devindotcom · 2026-10-27 23:58:44 UTC
the thing is we are not short on "ideas" in the drug development process
gradus_ad · 2026-10-28 00:02:56 UTC
Maybe AI would be better put to use helping us determine which ideas are good then
jrowen · 2026-10-27 23:29:11 UTC
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?"
daemonk · 2026-10-27 22:13:12 UTC
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.
stephen_cagle · 2026-10-27 22:47:00 UTC
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?
daemonk · 2026-10-27 23:30:31 UTC
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).
areoform · 2026-10-27 23:47:20 UTC
> 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
rao-v · 2026-10-27 22:29:51 UTC
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?
areoform · 2026-10-27 23:56:59 UTC
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.
rao-v · 2026-10-28 00:06:45 UTC
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)
blmarket · 2026-10-27 22:34:32 UTC
(sarcastic) Not too long until we see designer babies by Claude?
baq · 2026-10-27 22:36:43 UTC
'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.
ACCount39 · 2026-10-27 23:25:12 UTC
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.
gumby · 2026-10-27 23:10:47 UTC
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)
jghn · 2026-10-27 23:38:52 UTC
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.
kenhwang · 2026-10-27 23:59:38 UTC
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.
rcr-anti · 2026-10-27 23:14:27 UTC
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!
Mistletoe · 2026-10-27 23:16:38 UTC
I’d love to read Derek Lowe from In The Pipeline eviscerate this.
areoform · 2026-10-27 23:39:43 UTC
> 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 scientist 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?
lifeisloving · 2026-10-27 23:40:04 UTC
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.
yaoke259 · 2026-10-27 23:47:14 UTC
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.
Comments
>> 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'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'm interested in seeing how the scorpion anti venom plays out.
https://www.anthropic.com/research/claude-scorpion-toxin-bin...
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.
Since sequential steps are slow, I think we're going to see massive increases in parallelization. I'm not sure how the regulatory frameworks will keep up honestly.
Why? This is not an AI problem, it's an industry one. And if it was simple, it would already exist.
Not necessarily. Many things are simply gatekept behind the door.
I wonder what the unlock would be, if any, of having simulations substitute any (or all, in time) of these steps. Akin to the method used to train FSD cars, or robots.-
But I'm no biologist so if someone else is, feel free to correct me.
I wonder if, at some point, AI's will perform to such degree they are able to construct "black box" simulations as needed, that nobody understands BUT that are sufficient to empirically experiment on ...
... much in the same way LLM's function now: In an aggregate sense, they are bloack boxes.-
I've always thought openai could actually create an agent that can control various physical robotic arms and things like that, and then continuously iterate on solutions that can participate in the real physical environment. This could accelerate automatic iteration for many things that require human operations, such as complicated experiments, assembly adjustments, and testing.
Otherwise, AI is always trapped in virtual systems and still needs human intervention. But even though AI is already very powerful now, there is still a limit to how much humans can handle or assign tasks. Many times, AI has already finished processing something, but I don't even have time to assign the next deeper level of tasks.
People hope AI can improve productivity, but at the same time they're afraid of AI actually getting involved in reality, while the valuation of the entire AI industry is so high.
This kind of wanting to move forward but not moving forward makes humans especially uncomfortable.
https://deepmind.google/models/gemini-robotics/
Biology is the complete opposite of that.
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).
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
finally, us
“Generate scientist” … is that a real job like “prompt engineer”
Either that or six degenerate scientists. Both options work just fine
The company is called Generate Biomedicines, and they reference it in short as Generate.
Same applies to construction projects, BTW.
Lol that cracked me up :D I love that job title!
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.
> 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.
There's no regulation on not doing a trial because your oracle machine told you it was an expensive dead end.
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.
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?"
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.
My feeling on this is that math is amenable to LLM based development because the formalism of LEAN + the ability of AI to nudge research means that we basically have machines that can "research" through what is basically brute force search, but where the explored trees are ordered by AI.
But what if there were a biological system similar to LEAN for drug interaction? Could we not use AI in a similar way to nudge the exploration space based on the vibes that the AI has about drug discovery?
I agree there is a a distinction in the formalism of a proof (through LEAN) and the sort of probabilistic/chemical/physical/whatever nature of a biological organism (through some sort of future modeler for life science). But is the fact that one system is deterministic and the other probabilistic really as much of a leap as you think?
We currently have disparate datasets that allows us to make some predictions on how drugs will interact with molecules. And we have studies on how these molecules might affect our organs/bodies. But all of this data are at different resolutions, sparsity, and have different experimental context. It is extremely hard to reconcile for a holistic model.
DNA is the great unifier in molecular biology as it is the digital anchor/foundation that everything above relies on. But there is just so much complexity above it. An analogy in the AI space would be model distillation where you are trying to discover all the weights in a LLM by giving it a prompt and seeing what the results are. With biology, we know the prompt (DNA sequence, genotype) and we can kind of quantify the result (phenotype). And we have to figure out all the weights in between (how DNA becomes a fully functioning organism).
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 know Anthropic are brilliant at knowing when to focus where, but I genuinely expected them to be talking about 3-5x more serious scaled up drug discovery plays at this stage.
Is this not a focus, or did the big plays not want to be part of a roundup post?
Neural network driven drug candidate creation has been around since the 1980s. The blockbuster GLP-1 agonists were actually derived from the peptide in Gila monster venom by algorithmic search.
But you can't, and I can't emphasize this enough, just go "Claude make me a virus" and have it work. It just doesn't work that way. And the only reason why people think it does is because they haven't spent enough time in a lab, https://news.ycombinator.com/item?id=49620521
Don't get me wrong, this technology is worth fighting for because it will have incredible impact on the long time scale. But just not in ways they're talking about and expect. Nor the ways in which they're trying to dictate top down.
So as long as you can test things on cell culture level? You can do a lot with a high throughput setup.
This has been one of the target AI applications since the 1960s.
(Former drug developer who will never touch the life sciences again)
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 scientist 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?
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.
Automating or streamlining human/animal trials with 24/7 monitoring and AI check-ins would be huge.