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.
>>> 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.
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.
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.
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.
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.
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
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.
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.
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.
Yes, this is how it would work. This has been attempted essentially since scientific thought has existed. The less “deterministic” fields exist because the approach you mention has not worked for hundreds of years.
tech people often say stuff like this, but do not realize the degree to which we do not understand all the interactions in a biological system. we are so, so, so far away from "simulation of biology" being anything but a ludicrous proposal.
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.
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
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.
And I think this community is better off without advertisements littering the top of the page simply because it’s associated with someone’s favorite zero-effort thought substitute, but I choose to channel my distaste into cheap jokes instead of sour replies.
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.
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.
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.
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.
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.
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.
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.
Problem with those expensive treatments is that there are not that many cases. So profit from each is big but overall they add up to less. Getting everyone who can afford it on planet on some relatively cheap drug would be lot more profitable overall.
Get million out of thousand people or ten out of billion people... Later would be preferable.
Well there's also the third option of get a million out of a billion people. And that's only one side of the perverse equation. The other is rent-seeking. A treatment that needs to be redone or repeatedly boosted is far more profitable than a treatment that simply solves something once and for all, especially if that 'solving' treatment can be done cheaply.
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 perhaps design new equipment. 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.
> 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.
tldr; No sane drug professionals would have designed a relatively simple compound (144 Atomic Mass) as a Multiple Sclerosis drug
I think human review may screw things up. If you look at Dimethyl fumarate[1], no card carrying medicinal chemist would have taken this serious. No pharma exec would have risked 100s of millions of dollars for clinical research trials.
- 1950s people notices that a wood polish cleared psoriasis. Topical formulation worked.
- 1990s an oral formulation was created.
- 2000s people noticed people treated for psoriasis that had MS, had fewer MS symptoms while taking this molecule. Successful clinical trials followed.
[1] https://en.wikipedia.org/wiki/Dimethyl_fumarate
> 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.
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.
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.
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.
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.
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.
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).
> 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’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)
'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.
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.
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.
> 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 same 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.)
I'd take your overly dismissive words as credible (but still wrong) only if you did the career in both disciplines which I assume you didn't. You're wrong, and your view of SWE discipline is a great example of DK effect. Not something one would expect from a PhD in computational biology, yes?
Because all of discovery is only ~7% of the total cost of development. So even if you knew with 100% confidence that you could pick the right candidate and skip all the other testing steps for all other candidates, you only save 7% of total costs.
The rest of the 93% has to happen regardless anyway.
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."
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 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 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.
I appreciate you diving into the details, that's helpful to me. I've reread their post. They do say Claude many times so yes it's clear they are framing their work this way.
We have seen how these large models learn high order levels of understanding that is unexpected, e.g. physics in video generation. That makes me not want to dismiss the work in this area particularly when paired with orders of magnitude more computation being applied than us normies have access to.
This almost seems like catch-up to Google DeepMind stuff where it is research for research sake. So in that sense I can see the skepticism where one can look at this as taking Claude and pouring it over a problem like sweet LLM syrup that will make it all taste great.
Hmm.. I think it's my terminology issue. I just meant pocket as in informal word for "subspace", that is, generators can learn to generate good candidates within some subspace without needing to be good classifiers across the whole space.
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.
Humans are incredibly complex systems (and systems of systems) and our knowledge of how these systems work and interact is very limited despite the amount of work put into improving our understanding of them.
We can try to simulate the interaction between a drug and a protein, but most of the time the data to say what the downstream effects (if any) are simply does not exist. This is further complicated by the fact that these downstream effects can be extremely varied depending on both the tissue/disease you are targeting and the person you are treating.
Of course we are always generating more data from more diverse populations and more conditions, but there is a very very long way to being able to simulate drug trials like this.
The good: smartest people I have ever worked with, though also some of the dumbest (think of a saddle curve rather than a bell curve :-). Really interesting, fun, and really super-hard technical problems.
Discouraging-but-part-of-life issues: nobody is in a rush because everything is a marathon. It typically takes decades to get anything into the market; though regulators over the past few decades have been changing things to speed things up (basically doing science to make the process more adaptive and dynamic) it's still hard. You're continually looking for needles in haystacks: wispy signals that may be noise, extracted from huge datasets. As a startup, expensive slow, e.g. an overnight session on an HPLC can be a $10,000 experiment (or more because you're probably running a lot of those instruments in parallel at once) after having a bunch of people working all day to prep the samples.
The terrible: the industry is riven top to bottom with fear. Fear that the science will not work out. Fear that someone else is smarter than you (as a result: pervasive arrogance and hostility). Fear in the FDA that some company trying to game the system* will get something past you, or that the inspector general will launch an investigation, or some congressperson will launch a missile at you for no legitimate reason.
Also, you're launching artillery shells that can't be altered in flight. You write a study and then you have to run it with the protocol you wrote and got approved. You can't alter your marketing message unless it refers to scientific results explicitly demonstrated in a trial. And since the trials typically take years, there's a lot of sitting around.
* Back when I was in the biz we absolutely did not try to game the system and it made it a lot easier to work with the Agency (FDA). But the rules kept being made tighter and tighter to lock our bad action by companies. And of course the recent destruction of the FDA means all the rules have been thrown out the window, but by then I was already long gone. But my friends in the industry are still shocked and appalled.
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!
> 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.
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 sid...
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.
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.
262 comments
[ 0.17 ms ] story [ 19.4 ms ] thread>> 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...
> 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.
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.
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.
Biology is the complete opposite of that.
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.
Humans are just organic simulation environments for these drug trials.
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.
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.
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.
> 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.
As always Anthropic is saying literally anything if it gets them closer to the giant bucket of money that is the IPO.
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.
How many of these "we sandboxed it and said not to do Y, but then it went and did it!" stories would you consider necessary to disprove that?
It may be good at following rules 99.99% of the time but if someone launches a million times more parallel agents than they would hire humans, it might still be much worse in practice...
"We've been told we must find an effective drug candidate here, and to follow this process for evaluating the quality of the candidate, but actually we could subtly change the analysis like this to make this candidate look favorable enough..."
(And then compare that to deterministic algorithms...)
The argument here is that AI can run permutations faster than any human can. And, feeding it a company’s proprietary data about what worked, what did not, how it worked and so on can be invaluable at accelerating the discovery process of new drugs.
Plus, it can work 24/7/365.
People who behave like OpenAI and Antropic AI does when controlled by those companies get punished instead of celebrated.
Pharma makes bank on drugs that are patent protected. Squeezing the manufacturing cost is a small gain on top of that.
If a big pharma had the choice between an expensive treatment that could help treat any cancer, or an extremely cheap treatment that could prevent any cancer - which would they pick? The answer when you're motivated by profit is unfortunately different than the one when you're motivated by social benefit.
Get million out of thousand people or ten out of billion people... Later would be preferable.
Yes, and reducing manufacturing cost if you have tight margin has huge potential for profit.
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 perhaps design new equipment. 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.
Isn't this exactly what startups in this area are doing - automated experiments connected to AI. Of course at some stage things will "graduate" to human review but it still seems to have a lot of potential.
Biology grad students can spend 40+ hours per week in the lab, on top of all their course work. It's not so bad though, since there's plenty of time to grade papers or work on your thesis while waiting for reactions to occur.
Genuine question because I don't know the answer, I've just anecdotally heard all about the promise and nothing about real world results.
I think human review may screw things up. If you look at Dimethyl fumarate[1], no card carrying medicinal chemist would have taken this serious. No pharma exec would have risked 100s of millions of dollars for clinical research trials.
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.
Care to elaborate, explain?
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.
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 can't tell -- has there been any real progress made? Or is this a supposed application of AI that is getting interest? What about this problem in particular makes it suitable to AI as opposed to any other?
Or is this just a matter of -- "AI can cure cancer and is Good For the World!"
The last hundred oncology drugs have all required painstaking trial and error with no way to predict which candidates succeed or fail except at the years long cost of blood sweat and tears. A.I. will not remove that price from the equation except maybe move towards those trials marginally faster towards those decade long trials.
Oooops I mean, the agent "escaped" and hack the goverment... definetely not training it to become it's own state agent thing... pinky promise....
"Cautious optimism is probably warranted" strongly negativism advised
Actually creating them requires physically manipulating the inputs to the process, which requires humans doing it or humans making machines that can do it and allowing them to be controlled by the model.
In general the damage potential only goes as far as the agency extended to the models. The problem is that we've already seen how incredibly cavalier most people will be about security, and now we can envision situations where one bad actor could threaten billions.
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...
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)
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.
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?
(I say this as a person who has spent a non-trivial portion of my life as one of those people.)
Re-ranking pre-clinical leads is the scientific equivalent of that old saying about opinions.
Um. Hate to disappoint you, but... What's that old saying about assumptions?
The rest of the 93% has to happen regardless anyway.
"Developing a drug involves many varied tasks: doing early research to identify a target for a drug and the right type of drug to engage it, designing a molecule with the right properties, optimizing it to be manufactured reliably at scale, drafting the regulatory filings needed to run clinical trials, designing and administering those trials, and much more.
Our strategy to realize AI’s medical benefits is based on having AI increasingly involved in all of these tasks."
Optimizing molecular properties of a lead is important, but ultimately not a huge source of costs. In any case, LLMs probably have as much chance of predicting outcomes as anyone else (i.e. very small).
Manufacturing is somewhat expensive, but most of the cost sits in actually creating a production process, which is not a thing LLMs can do.
Clinical trials are where most of the costs lie, but unless you can create a magical classifier that can tell me if my drug will make it past phase 3, you're not significantly bending the cost curve.
> unless you can create a magical classifier
A generator could concentrate on pockets while remaining unable to judge most drugs. And LLM RLVR a) works well, and b) is black box optimisation and so is suited since it doesn't need system identification (which you can't do for complex biological systems). So the hypothesis is that, yes making a classifier is very very hard, but you could through some (lots?) initial data, end up with our generator outputting with high probability drugs-that-end-up-passing-trials-with-probability-p. Whether these drugs thus generated are drugs we care about (i.e, whether `p` is good enough) and whether the whole pocket it generates in is large enough, and how jagged this behaviour is (i.e distribution of `p` across taxonomies of drugs), are the few questions we want answered.
That said, we have extremely well-refined force fields that attempt to model the underlying physics, and still fail to do this well. There's absolutely no reason to believe that a generic LLM will bring anything to the table that improves the situation. Even with the hand-wavy assertion that we (somehow) get "lots and lots of data", and somehow train a purpose-built model to do the same, it's kind of like saying that we could turn a minivan into a formula one car with enough effort. Different tools for different jobs.
I actually do believe that informatics approaches will be helpful given lots of system-specific data, but when I say that, I mean "a bespoke neural energetic model that is trained specifically for some molecular system down to customized force-field parameters", not "Claude".
So sure, maybe Anthropic will invest lots of time and money and energy into making these kinds of molecular simulation models, which they can then use for high-throughput screening experiments. But even if they do all of that, there's still no guarantee that they'll bring anything to the party that hasn't been done before...because this is the lowest-value step of drug discovery, and a model like that won't tell you if your drug is going to be toxic, or have off-target effects, or have metabolic problems, or...a million other things, which are what kill drug programs in phase 1 and beyond.
We have seen how these large models learn high order levels of understanding that is unexpected, e.g. physics in video generation. That makes me not want to dismiss the work in this area particularly when paired with orders of magnitude more computation being applied than us normies have access to.
This almost seems like catch-up to Google DeepMind stuff where it is research for research sake. So in that sense I can see the skepticism where one can look at this as taking Claude and pouring it over a problem like sweet LLM syrup that will make it all taste great.
Hopefully that's not the case.
Hmm.. I think it's my terminology issue. I just meant pocket as in informal word for "subspace", that is, generators can learn to generate good candidates within some subspace without needing to be good classifiers across the whole space.
But yeah as a whole that makes sense, thanks.
Unfortunately the HN UX doesn’t make it easy to surface these situations, not that I have any idea how one could make it easier.
Thank you for entertaining my curiosity! I have been meaning to educate myself on this but don't know anybody in my circle who is knowledgeable in this field.
We can try to simulate the interaction between a drug and a protein, but most of the time the data to say what the downstream effects (if any) are simply does not exist. This is further complicated by the fact that these downstream effects can be extremely varied depending on both the tissue/disease you are targeting and the person you are treating.
Of course we are always generating more data from more diverse populations and more conditions, but there is a very very long way to being able to simulate drug trials like this.
I'll bite: why not?
Discouraging-but-part-of-life issues: nobody is in a rush because everything is a marathon. It typically takes decades to get anything into the market; though regulators over the past few decades have been changing things to speed things up (basically doing science to make the process more adaptive and dynamic) it's still hard. You're continually looking for needles in haystacks: wispy signals that may be noise, extracted from huge datasets. As a startup, expensive slow, e.g. an overnight session on an HPLC can be a $10,000 experiment (or more because you're probably running a lot of those instruments in parallel at once) after having a bunch of people working all day to prep the samples.
The terrible: the industry is riven top to bottom with fear. Fear that the science will not work out. Fear that someone else is smarter than you (as a result: pervasive arrogance and hostility). Fear in the FDA that some company trying to game the system* will get something past you, or that the inspector general will launch an investigation, or some congressperson will launch a missile at you for no legitimate reason.
Also, you're launching artillery shells that can't be altered in flight. You write a study and then you have to run it with the protocol you wrote and got approved. You can't alter your marketing message unless it refers to scientific results explicitly demonstrated in a trial. And since the trials typically take years, there's a lot of sitting around.
* Back when I was in the biz we absolutely did not try to game the system and it made it a lot easier to work with the Agency (FDA). But the rules kept being made tighter and tighter to lock our bad action by companies. And of course the recent destruction of the FDA means all the rules have been thrown out the window, but by then I was already long gone. But my friends in the industry are still shocked and appalled.
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 sid...
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.
Yikes
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.