Hey, Julian here, CEO & Cofounder of Adaptyv (https://adaptyvbio.com). We run an automated wet lab for testing AI-designed proteins.
Anthropic benchmarked their newest Claude models on protein engineering tasks, and we at Adaptyv ran the wet lab work behind it.
They picked 16 targets from our past protein design competitions on Proteinbase (https://proteinbase.com) and sent us an anonymised list of designs, so we had no idea which model produced which sequence. We then ran those protein designs through our automated wet lab workflow to characterize protein expression and binding affinities.
Here are the results:
- 95% of the designs expressed, which three years ago would have been the headline on its own. That matches the best expert expression rates from our competitions
- 354 of 1,320 designs bound their target on SPR, a 26.8% hit rate overall. Claude got binders on 14 of the 15 targets we could evaluate. Compared to our own data from public competitions, Claude beat de novo hit rates, for one target by more than three times the rate on Proteinbase
So can AI now solve all diseases? Well no, not yet.
This case study shows that Claude is at least expert-level at orchestrating protein design tools. That’s great news, since protein design tools are hard to use. Before AI, even setting up a protein structure prediction tool could take hours of debugging opaque conda errors.
Of course, those proteins that we tested here are not real therapeutics. They completed only the first step of the process: demonstrating that they can function as binders. Still, this study shows a path towards making actual therapeutics with AI.
Imagine making a drug is like climbing a mountain. We have clearly been able to climb some mountains, as humanity has made many drugs already. But the way to the top is a dangerous narrow path and climbing it takes many years and costs billions of dollars (and the lives of many biotechs).
The goal of AI for drug discovery is turning this narrow mountain path into a highway, making it easier and cheaper to get to the top so that we can develop 100x more therapeutics than we have right now. Similarly, writing code was a more of a high-expertise craft before LLMs, now it’s mostly automated and it has made generating software accessible for anyone.
Hey, Julian here, CEO & Cofounder of Adaptyv (https://adaptyvbio.com). We run an automated wet lab for testing AI-designed proteins.
Anthropic benchmarked their newest Claude models on protein engineering tasks, and we at Adaptyv ran the wet lab work behind it.
They picked 16 targets from our past protein design competitions on Proteinbase (https://proteinbase.com) and sent us an anonymised list of designs, so we had no idea which model produced which sequence. We then ran those protein designs through our automated wet lab workflow to characterize protein expression and binding affinities.
Here are the results:
- 95% of the designs expressed, which three years ago would have been the headline on its own. That matches the best expert expression rates from our competitions
- 354 of 1,320 designs bound their target on SPR, a 26.8% hit rate overall. Claude got binders on 14 of the 15 targets we could evaluate. Compared to our own data from public competitions, Claude beat de novo hit rates, for one target by more than three times the rate on Proteinbase
So can AI now solve all diseases? Well no, not yet.
This case study shows that Claude is at least expert-level at orchestrating protein design tools. That’s great news, since protein design tools are hard to use. Before AI, even setting up a protein structure prediction tool could take hours of debugging opaque conda errors.
Of course, those proteins that we tested here are not real therapeutics. They completed only the first step of the process: demonstrating that they can function as binders. Still, this study shows a path towards making actual therapeutics with AI.
Imagine making a drug is like climbing a mountain. We have clearly been able to climb some mountains, as humanity has made many drugs already. But the way to the top is a dangerous narrow path and climbing it takes many years and costs billions of dollars (and the lives of many biotechs).
The goal of AI for drug discovery is turning this narrow mountain path into a highway, making it easier and cheaper to get to the top so that we can develop 100x more therapeutics than we have right now. Similarly, writing code was a more of a high-expertise craft before LLMs, now it’s mostly automated and it has made generating software accessible for anyone.