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.

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.

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.

From the technical report:

"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.

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.

So it’s ok for Anthropic to make new biologics … but it’s users cannot because it might end humanity.

Let alone the fact this doesn’t even appear to be peer reviewed either from Anthropic.

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.

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