00:11:17 · Adam Miller, Josh Meier, Sachin
[00:11:17] S2: Yep, and what other ones that you think pharma will then sort of pick and prioritise? [00:11:23] S0: I probably can't talk about all of those details. [00:11:25] S2: Yeah, okay. [00:11:27] S1: Sitting opposite you, you kind of ooze this kind of confidence, and it seems like everything's just going up into the right for Chai. If we were to kind of like look inside your internal monologue, what would surprise us with maybe some of the doubts you have or some of the things that don't feel like they're going up into the right?
00:11:44 · Josh Meier
[00:11:44] S0: That's a good question. Again, I sometimes wake up, it's like I have to pinch myself, are these results real? Actually, with Chai-2 was a very big thing because no one had shown results like this. People have been working on it for a very long time. People even told us when when we were getting started, oh, the antibody AI field is crowded. Like, you know, so many people have tried these models. It's like there's literally 100x improvement that we're able to build within a year. And, until we had all the data back from the lab, we didn't fully believe it. There were all these, you know, last experiments we needed to do to make sure that the results were real, and we published all that, of course. So I think it's always you see these big results and it's like, what's the catch, right? Did we do something wrong here? You know, and usually it ends up being being right, but that's why before we put anything out at Chai, we like to be really rigorous about this, ask all the right questions, you know, dot all our i's, cross all our t's. So, you know, there's always concerns. We're pretty skeptical and rigorous group here. It's one of the reasons why I love working with this team. You know, we're we're do... We're working in one of the most hyped up spaces, AI, right? But at the same time, everyone here is very realistic, asks all the hard questions. And and that means, you know, we're always thinking about these things, but we're very open about, you know, discussing those doubts per se, and then we can go do the work in order to, you know, in order to massage them.
00:12:59 · Adam Miller, Josh Meier
[00:12:59] S2: Focusing on the product for a moment, it's been described by some as CAD for molecules, so when I actually sit down and look at the software, like, what am I actually seeing and how does it work? [00:13:09] S0: Yeah, well, like to think of the protein folding models. If you're looking at the structures on the product, it's almost like an atomic level microscope. Protein folding has advanced to such a point now where, we can now predict how antibodies and antigens interact with a very high degree of accuracy. If you look back just a couple of years ago, you know, you look at the state of the art AlphaFold, right, a few years back, it could only predict these antibody antigen complexes correctly about like 3% of the time, and then now with our latest models, it's improved by an order of magnitude, right? So we're now in a space where we can really see what these molecules look like on the computer, and that really changes the way that we design things because when you see something on the computer, there's a very good chance that it looks like that in the real world. Like, one of the craziest things we saw here is if you take those structures that you're seeing on the product, and then we went into the lab, we used a technique called cryo electron microscopy. We literally used electron microscopes to see at the atomic level what's going on here. The error between what we saw in the lab and what we saw on the computer, was... The error was was less than the diameter of an atom. So it's it's crazy how accurate these AI systems have gotten in biology.
00:13:09 · Josh Meier, Adam Miller
[00:13:09] S0: Yeah, well, like to think of the protein folding models. If you're looking at the structures on the product, it's almost like an atomic level microscope. Protein folding has advanced to such a point now where, we can now predict how antibodies and antigens interact with a very high degree of accuracy. If you look back just a couple of years ago, you know, you look at the state of the art AlphaFold, right, a few years back, it could only predict these antibody antigen complexes correctly about like 3% of the time, and then now with our latest models, it's improved by an order of magnitude, right? So we're now in a space where we can really see what these molecules look like on the computer, and that really changes the way that we design things because when you see something on the computer, there's a very good chance that it looks like that in the real world. Like, one of the craziest things we saw here is if you take those structures that you're seeing on the product, and then we went into the lab, we used a technique called cryo electron microscopy. We literally used electron microscopes to see at the atomic level what's going on here. The error between what we saw in the lab and what we saw on the computer, was... The error was was less than the diameter of an atom. So it's it's crazy how accurate these AI systems have gotten in biology. [00:14:17] S2: And also from the product perspective, in terms of what the actual outcome is, would you describe it as it collapsed the amount of time it's taking to discover and design drugs? Is that the fundamental thing?
00:14:17 · Adam Miller, Josh Meier
[00:14:17] S2: And also from the product perspective, in terms of what the actual outcome is, would you describe it as it collapsed the amount of time it's taking to discover and design drugs? Is that the fundamental thing? [00:14:28] S0: I mean, that's for sure. I think it's, I can't think of anything else in AI that takes a process that might have taken months or years, you know, discovering an initial molecule, and can now do it with twenty four hours of compute and then, you know, a couple of weeks of testing in the lab, like that's just incredible just to think about like what that compression of time looks like. But actually time is not the thing I'm most excited about, it's actually the ability to make better medicines. A lot of the field right now, is, again, drug design is really hard. There's a lot of projects that are quite incremental that are out there, and I think that by making it easier to design these complex molecules, hopefully we can raise the bar for what people are working on, and then allow patients to really get the treatments that they need.
00:15:08 · Adam Miller
[00:15:08] S2: Something that stayed with me from this interview is just how much of Chai's story comes back to trust. Drug hunters won't use tools they don't trust. Pharma companies rely so much on models, and that's why security needs to be airtight. Chai's customers' projects are, in their own words, top secret, and that's why they need to invest so much in security. And that is a big reason why they've been able to partner with companies like Eli Lilly. And that trust equation isn't just unique to biotech it's something that every company needs to think about when they start to engage with serious customers. It's not a question about is your product actually good? It's a question of do they trust you? We've heard of deals that have stalled for months because things like SOC two and ISO 27,001 weren't installed. These problems are exactly what our sponsor Vanta was made for: continuous monitoring across every framework, dashboards set up for you, and what used to take months now takes weeks. This is what lets ambitious teams focus on their product while enterprise trust gets built in the background. If you're building something ambitious and want to unlock real enterprise relationships, head to Vanta and get $1,000 off from us. Head to vanta.com/sachaninandable.