No Priors Ep. 103 | With Vevo Therapeutics and the Arc Institute

Hi, listeners. Welcome back to No Priors. Today, we're here with the CEO, CTO, and core investigator of the Arc Institute, as well as the co-founders of Vyvo to talk about their release of the Tahoe 100, the largest single cell drug-perturbed dataset ever created, as well as where we are in AI for biology, why we need a virtual cell model and not just protein structure prediction models, and when we should finally expect to see treatments from this growth of use of machine learning in bio.

Hi, I'm Johnny, and I work on single-cell RNA sequencing at Vyvo.

I'm Nima. Uh, I'm one of the founders together with Johnny. Um, I'm a quantum chemist by background, but I've converted to be, being a computational chemist that loves playing with biological data. And we are building Vyvo to, to really do that, to predict how chemicals interact with cells in different biological contexts. Some people call it the virtual cell. That's, that's basically what we're working on.

I'm Patrick Hsu, one of the founders at the Arc Institute, which is working at the interface of biology and machine learning to try to understand and, uh, one day treat, uh, complex human diseases, which are most of the major killers.

I'm Dave, uh, CTO at Arc Institute, focused on computational biology and building novel AI models, uh, for biology.

I'm Hani. I'm a core investigator at Arc. I work very closely with Dave and Patrick to push our, you know, virtual cell initiative.

Congratulations, everyone. It's a big day. Uh, let's jump right into it. What is the Tahoe 100, and what is the significance of it?

So, Tahoe 100 is the world's biggest single-cell RNA sequencing dataset, and it enables basically a ton of machine learning applications, including things like the virtual cell, but it also enables a lot of drug discovery applications. And broadly, in the context of where I think we are as a field, it's kinda the beginning of a different way of doing drug discovery, of basically understanding how to build medicines, and basically bringing AI and machine learning people into the mix.

And maybe something I would add, uh, there as well, um, over the last 20 years or so, people have accumulated massive amounts of, you know, data points when it comes to protein structures, uh, protein function, how drug molecules interact with proteins. But one thing that we haven't had as much is how, uh, how different cells behave in different contexts and how different genes whi- within each of those cells actually functions in the presence of the other genes, so, you know, in, in, in these different biological contexts. Um, this, we believe this is the era for that right now, and you have seen the emergence of protein language models built on the datasets that have been accumulated over the last two decades. But now is the era for actually having data on cells, how they function, how they interact with drug molecules. And exactly what Johnny's saying, Tahoe is really a landmark dataset there that allows us to really measure how drugs interact with different cells from different patient models, um, and that gives us the ability to build similar models that we built in protein language models, but in the, um, in the cellular kinda context.