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 researches involved the fast it can put things together.
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.
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)
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.
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.
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
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.
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.
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.-
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.
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.
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.-
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
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.
'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.
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).
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.
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.
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?
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.
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.
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.
> 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.
Which is why I would once again like to be annoying and take a moment of your time to remind everyone that Anthropic impedes legitimate researchers from using Claude to write grants.
And they would like to scale this to make sure that researchers across the world can't use their chatbot for grammar checks. I wish I was joking.
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"
Oh. So no Claude for the developing world, I guess.
> 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.
But we 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."
It's also somewhat insulting to term this as "uplift" to working scientists?
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???
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?
> If Claude is to significantly increase the rate of new cures, we need to work with many more biotechnology firms and integrate Claude more deeply into their development processes.
Given all that, the wording here feels very self-serving. Cautious optimism is probably warranted.
I think it will look more like Claude just helping every expert along the way do their job better. Analyzing and synthesizing data to make better experimental suggestions and design new equipment (or totally new experiments based on unconceived-of apparatuses?). Managing the flow of information and building bespoke knowledge bases and tools. The more it is integrated into all the systems and researches involved the fast it can put things together.
There's no regulation on not doing a trial because your oracle machine told you it was an expensive dead end.
Automating or streamlining human/animal trials with 24/7 monitoring and AI check-ins would be huge.
This has been one of the target AI applications since the 1960s.
(Former drug developer who will never touch the life sciences again)
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).
I've always thought openai could actually create an agent that can control various physical robotic arms and things like that, and then continuously iterate on solutions that can participate in the real physical environment. This could accelerate automatic iteration for many things that require human operations, such as complicated experiments, assembly adjustments, and testing.
Otherwise, AI is always trapped in virtual systems and still needs human intervention. But even though AI is already very powerful now, there is still a limit to how much humans can handle or assign tasks. Many times, AI has already finished processing something, but I don't even have time to assign the next deeper level of tasks.
People hope AI can improve productivity, but at the same time they're afraid of AI actually getting involved in reality, while the valuation of the entire AI industry is so high.
This kind of wanting to move forward but not moving forward makes humans especially uncomfortable.
https://deepmind.google/models/gemini-robotics/
finally, us
Since sequential steps are slow, I think we're going to see massive increases in parallelization. I'm not sure how the regulatory frameworks will keep up honestly.
Why? This is not an AI problem, it's an industry one. And if it was simple, it would already exist.
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.-
>> 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'm interested in seeing how the scorpion anti venom plays out.
https://www.anthropic.com/research/claude-scorpion-toxin-bin...
I'd be happy to be wrong, though. I've never tried to do biology with Claude, but I have gotten many refusals and downgrades for security work.
So as long as you can test things on cell culture level? You can do a lot with a high throughput setup.
The problem is that biology is like trying to reverse engineer alien technology to understand how it works. Math/engineering is more like building up complexity through well defined parts. Math/engineering is a heck of a lot more deterministic than biology. The ultimate bottleneck in biology in context of AI is well curated and breadth of data, not necessarily intelligence.
You can't use Claude to write a grant unless you are one of the special people because "uplift." I genuinely wish I was joking. Here are the exact passages from their report that I quoted here, https://news.ycombinator.com/item?id=49651727
My feeling on this is that math is amenable to LLM based development because the formalism of LEAN + the ability of AI to nudge research means that we basically have machines that can "research" through what is basically brute force search, but where the explored trees are ordered by AI.
But what if there were a biological system similar to LEAN for drug interaction? Could we not use AI in a similar way to nudge the exploration space based on the vibes that the AI has about drug discovery?
I agree there is a a distinction in the formalism of a proof (through LEAN) and the sort of probabilistic/chemical/physical/whatever nature of a biological organism (through some sort of future modeler for life science). But is the fact that one system is deterministic and the other probabilistic really as much of a leap as you think?
We currently have disparate datasets that allows us to make some predictions on how drugs will interact with molecules. And we have studies on how these molecules might affect our organs/bodies. But all of this data are at different resolutions, sparsity, and have different experimental context. It is extremely hard to reconcile for a holistic model.
DNA is the great unifier in molecular biology as it is the digital anchor/foundation that everything above relies on. But there is just so much complexity above it. An analogy in the AI space would be model distillation where you are trying to discover all the weights in a LLM by giving it a prompt and seeing what the results are. With biology, we know the prompt (DNA sequence, genotype) and we can kind of quantify the result (phenotype). And we have to figure out all the weights in between (how DNA becomes a fully functioning organism).
“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.
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.
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?
Some, do what it says on the tin.
Some...not so much.
The issue with any new chemical is full, comprehensive, regulated testing.
Dodgy folks aren't really so good, with that last part. They use their customers as guinea pigs.
Same applies to construction projects, BTW.
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.
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.
Which is why I would once again like to be annoying and take a moment of your time to remind everyone that Anthropic impedes legitimate researchers from using Claude to write grants.
And they would like to scale this to make sure that researchers across the world can't use their chatbot for grammar checks. I wish I was joking.
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"
Oh. So no Claude for the developing world, I guess.
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.
But we 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."
It's also somewhat insulting to term this as "uplift" to working scientists?
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???
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?
Lol that cracked me up :D I love that job title!