This sounds awesome but I wish these would be published in a peer-reviewed journal or something of that nature. I can't believe these press releases they put out, even though I'm sure it's an achievement.
They're trying to catch up to OpenAI which has been doing these a lot, but I agree, can we actually get them peer reviewed as a hard requirement? I think OpenAI would have some looked over by experts before posting their blog posts.
How do you determine which one's ahead in what? OpenAI isn't just known for their models being used in mathematical discoveries, they've also been used in other areas as well? Anthropic on the other hand has not had anywhere near the amount of news coverage as OpenAI gets for their discoveries / efforts.
If I were at Anthropic, I'd be making sure the higher ups highlight discoveries made using the models they make, because OpenAI is definitely highlighting those.
I hope this produces some useful anti-venom that is cheaper and widely available. My kid was stung by a small bark scorpion on his thumb here in Arizona and I would not wish that experience on anyone. For hours afterwards he was in excruciating screaming pain, and he even lost his vision for while. That lasted about 24 hours and then it took weeks for him to recover feeling in his hand. Nothing seemed to help with the pain and there was very little we could do for him. I would love to have had a more available anti-venom, but apparently now it is so expensive they only keep it around for the very worst cases.
Unfortunately, available does not mean cheap these days. Parents (good ones anyway) would go into a decade of debt to prevent their child from having an experience like you just described. Drug giants see that as a business opportunity.
A single shot of rabies costs about ~$3 in Asian countries where street dogs are common. It’s crazy how expensive post exposure rabies treatment is in the US.
Can someone explain whether design of protein binders is an important rate-limiting step in drug creation (of which antivenom is a very small market)? Or AI-driven drug discovery in general? It feels like something that will definitely help the drug market but is nowhere near the rate-limiting step, but that's from my own researcher as someone without a background in it.
My understanding is that FDA trials (specifically phase 2 + 3) are the death valley for most drugs and finding a biomarker is something mostly done through a mix of gene bank scraping + looking at old biomarker databanks. The majority of the cost and time is in failed drugs that do not pass FDA trials (which average 6-7 years end-to-end).
Note: I am asking this in earnest. I am not trying to ask a leading question and saying it is not important at all.
The impression I have, which admittedly is purely as a layman reading articles, is that by far the rate-limiting factor in all drug creation is the cost of doing clinical trials in humans.
My understanding is it is not - this is something we have models for, and there was a flurry of machine learning based drug discovery companies about 8-10 years ago. But they ran into the testing bottleneck real fast because they would spit out thousands of candidates for binders and so on that all needed to be tested in vitro before they could even start the real drug dev circuit. Furthermore the drugs need to be manufacturable and that's not a trivial problem.
It's nice to (as the blog post suggests) give the opportunity to non medical experts, say in this case a snake/venom research center, to pursue a problem and produce some realistic candidates that they could pay to test. I do think it's good to democratize what parts of the process can be, but it's more of a resource prioritization problem than a molecular design one.
While Clinical Trials are the most expensive step and have a 90% failure rate, it is not the *source* of the failures, it's just where failures are revealed.
Much of whether or not a candidate drug will succeed was determined all the way back in the design/discovery stage. If we can design better drugs, then we would enjoy a higher probability of success in the later clinical trials.
The single biggest improvement to pharma drug development would be from increasing the probability of success in clinical trials.
Is it fair to say the source of the failures is we simply do not understand the complex relationship between the drug design (chemistry, cell therapy, etc.) and the biology (the complete system of the living thing). If we could understand that relationship, we could design better drugs? I know this is very hand-wavy. Probably so much so as to not be a useful thing to say, but I'm just trying to understand.
Correct. The on-target effects of antivenin can be well-modeled along with a known subset of off-target effects, but the latter is far too complex and unknown to model completely.
Trials are the acceptable middle ground that uses animal and human models to explore those unknowns.
Neat, but doesn't change anything about the fact that even with this protein design headstart nobody is going to pay for the human trials or the cost to produce and distribute the possible antivenom.
In practical terms this is like Claude designing you a new home. Cool, you now have blueprints for a new home. You can print it out and hang it on the wall of your studio apartment.
It orchestrated available open source tools to de novo generate and assess the designs. It is still really impressive that it has enough theory to use the tools correctly, but it isn't generating the residues from scratch within the model itself.
I got Opus5 to write me a enzyme design pipeline on AWS last month using available tools and databases. It works fine. It's not as handsoff as you might think when you start looking at more complex proteins with multiple binding pockets and sequential catalytic process within multimeric structures. The bottleneck is still actually testing the candidates out in the lab.
"Our prompt specified the targets, the design sets and their sizes, the rules for each design, the protein design and prediction tools, the scores and ranking formulas, and the novelty and diversity rules."
I think most scientists would consider LLM to be an orchestration tool in cases like this. LLMs do work great for automating this type of drug discovery workflow! You definitely don't need the latest and greatest model, either, as long as you're careful in your specification. But attributing the designs to the tool (Claude), not the scientists who came up with the workflow, is distasteful and misleading.
This is somewhat funny given that not that long ago they were doing an article instigating fears of what Claude could do, and one of them was a researchers screening venom peptides along other things
Perhaps he could have worked on a similar subject? Who knows.
I don't see scientists reacting to this with the same affront as Terrance Tao does for frontier LLM mathematical proof dumping. Just imagine the Salk Institute declaring "we are on a war footing" after learning of such LLM aided discoveries.
MattGrommes | 2 hours ago
giancarlostoro | 2 hours ago
enraged_camel | an hour ago
Sorry, what? OpenAI is "ahead" in math. Anthropic is "ahead" in life sciences, and even have their own wet lab.
giancarlostoro | an hour ago
If I were at Anthropic, I'd be making sure the higher ups highlight discoveries made using the models they make, because OpenAI is definitely highlighting those.
enraged_camel | an hour ago
We are talking about discoveries the labs have publicly showcased.
conorh | 2 hours ago
LoganDark | an hour ago
kevin_thibedeau | an hour ago
They saw Shkreli hike prices 100x and decided that was the business model for them. Same thing has happened with rabies.
darth_avocado | an hour ago
dreamcompiler | 31 minutes ago
amelius | 6 minutes ago
vhiremath4 | 2 hours ago
My understanding is that FDA trials (specifically phase 2 + 3) are the death valley for most drugs and finding a biomarker is something mostly done through a mix of gene bank scraping + looking at old biomarker databanks. The majority of the cost and time is in failed drugs that do not pass FDA trials (which average 6-7 years end-to-end).
Note: I am asking this in earnest. I am not trying to ask a leading question and saying it is not important at all.
dooglius | 2 hours ago
estearum | 2 hours ago
devindotcom | 2 hours ago
It's nice to (as the blog post suggests) give the opportunity to non medical experts, say in this case a snake/venom research center, to pursue a problem and produce some realistic candidates that they could pay to test. I do think it's good to democratize what parts of the process can be, but it's more of a resource prioritization problem than a molecular design one.
eig | an hour ago
Much of whether or not a candidate drug will succeed was determined all the way back in the design/discovery stage. If we can design better drugs, then we would enjoy a higher probability of success in the later clinical trials.
The single biggest improvement to pharma drug development would be from increasing the probability of success in clinical trials.
vhiremath4 | an hour ago
EA-3167 | 41 minutes ago
Trials are the acceptable middle ground that uses animal and human models to explore those unknowns.
slashdave | 18 minutes ago
slashdave | 19 minutes ago
throwaway_20161 | 2 hours ago
Let alone the fact this doesn’t even appear to be peer reviewed either from Anthropic.
runj__ | an hour ago
I wouldn't trust you with a gun of mine... I don't know you.
throwaway_20161 | an hour ago
red75prime | 59 minutes ago
yunnpp | 18 minutes ago
bayarearefugee | 2 hours ago
In practical terms this is like Claude designing you a new home. Cool, you now have blueprints for a new home. You can print it out and hang it on the wall of your studio apartment.
Catloafdev | an hour ago
https://i.imgur.com/fRVxjzq.png
daemonk | an hour ago
I got Opus5 to write me a enzyme design pipeline on AWS last month using available tools and databases. It works fine. It's not as handsoff as you might think when you start looking at more complex proteins with multiple binding pockets and sequential catalytic process within multimeric structures. The bottleneck is still actually testing the candidates out in the lab.
AnodicElegy | 41 minutes ago
"Our prompt specified the targets, the design sets and their sizes, the rules for each design, the protein design and prediction tools, the scores and ranking formulas, and the novelty and diversity rules."
I think most scientists would consider LLM to be an orchestration tool in cases like this. LLMs do work great for automating this type of drug discovery workflow! You definitely don't need the latest and greatest model, either, as long as you're careful in your specification. But attributing the designs to the tool (Claude), not the scientists who came up with the workflow, is distasteful and misleading.
dev_hugepages | 31 minutes ago
waffletower | 11 minutes ago