Chai-2: The AI Model Accelerating Drug Discovery with Chai Discovery Co-Founders Jack Dent and Joshua Meier

**Sarah Guo** (0:05)
Hi, listeners. Welcome back to No Priors. Today, I'm excited to speak with Josh Meier and Jack Dent, two of the co-founders at Chai Discovery and former bio, AI and engineering leaders at Meta OpenAI at Science Stripe. This week, Chai released their industry-leading Chai-2 zero-shot antibody discovery platform, which at its core is a generative model that can design antibodies that bind to specified targets with 100-fold the hit rate of prior computational approaches. We'll talk about their product, the next frontier for Chai, why they're bullish on biotech, and why the most effective antibody engineers will soon be working as expert prompt engineers. Jack, Josh, congrats on the Chai-2 launch. Thanks for doing this. Welcome.

**Joshua Meier** (0:44)
Thanks for having us, Sarah. We're excited to be here.

**Jack Dent** (0:46)
Good to be here.

**Sarah Guo** (0:47)
Josh, I'll start by just asking, you and several of the scientists on the team have been working on AI drug discovery for about a decade now in different settings. I've also been looking at this area for over a decade. We haven't yet seen successes of drugs to market that were designed with these AI computational techniques. What made you believe? Why start the company when you guys did?

**Joshua Meier** (1:09)
That's a great question. Many of us have been working on this space for a while, and didn't start a company because it was really a research idea, I think, until very recently. There were signs of life that someday this was going to work, but it wasn't really on the timeline of a company. You can't really start a company thinking that 10 years from now things are going to work. You also don't want to start a company after it's already working and miss the boat. So the sweet spot is like, okay, we have like maybe one, two years that we have to really get this off the ground. And we made a bet when we started the company, that was going to work. There were really a couple of things that fueled that decision. The first one was we made a bet that structure prediction, protein folding was going to get a lot better. So obviously protein folding is considered solved in a couple of years ago, around like 2020 You had the breakthroughs of AlphaFold 2, and being able to predict protein structures with experimental accuracy. But it was just a single protein structure at a time. So we can take a single protein sequence and we can see what that protein looks like. That's very useful for basic biology, so we can understand what the proteins we're looking at look like. But if you think about drug discovery, which is where we're really focused on Chai Discovery, in drug discovery you need to understand how multiple molecules interact with one another. So you need to understand how a small molecule drug is going to modulate a protein, or how an antibody protein is going to modulate an antigen protein. So we started to see early signs of life that that was going to be possible. Again, we made a bet that we would be able to take this to the next level with the kinds of breakthroughs that we were seeing around diffusion models and around language models. The previous generation of structure prediction models would really just predict one confirmation protein at a time, kind of like one view on a protein. It's like the early image models. They didn't have diffusion models. You weren't really able to look at the diversity of generations that could come out. We thought the same thing would impact drug discovery and protein folding as well. So that's a bit of color on how we decided to start the company and we did. Maybe lastly, I should say, almost every AI biocompany before us has had some kind of very tight lab integration with what they are doing, and it almost too tight. I think the lab integration is great. We do a lot of lab experiments at Chai. But the thing that was missing was could you actually have some kind of portable AI platform, something that would actually be generalizable and could be applied to lots of different areas. If you could do that, it means that your impact could really be taken to the next level. We can take Chai-2, the model that we've just released, and we can deploy it to hundreds of different projects, thousands of different projects. Chai-1, which we open-source, is already being applied throughout the industry to tons of different projects. We don't even know everything is being applied to because it's open-sourced. But that was something that was also really important to us if we were going to kind of see this transformation of biology from a science into more of an engineering discipline, which is ultimately the goal of the company.