Chai Discovery

“We're lucky to work with the folks at Chai Discovery who just released Chai One, which is open source model that outperforms Alpha Fold Three.”

“A lot of these deals you know, and this is all public, right? We are working with them to, you know train or fine tune a version of our model for them.”

“For example, in CHI-II, we showed like GPCR agonist activity, right? Where you can really hit the switch on a, you know, on a cell doorbell protein, so to speak, right? In a very precise way. Very, very, very hard to do that with antibodies if you can't be tha…”

“One of our first hires on that realm was Nathan Rollins, who I think he started working in the Baker lab at 14. Graduated from Harvard at like 18 and got his PhD by like 21 or something like this in the Marx lab.”

“Just be bitter, less impaled in that sense, and just really bet on the models getting better. And we definitely took the latter approach. Like we bet on the models getting better and we just pushed as hard as we could on that front.”

“Like a lot of the money that we're raising now will let us train bigger models that can maybe be even more general and start to account for even more things at the same time, right?”

“We'd been working with you know or talking to Eli Lilly and, you know, they were, you know one of the first partners to really work with us closely on that kind of you know, it made that V one of that that design suite, right. That you can use to engineer some…”

“The ultimate goal here is to design medicines, right, and design new molecules, and I think CHI-2 really crossed the threshold of performance for doing that with antibodies a year ago.”

“And, you know, I think this class of models is going to be like just as big, just as impactful as LLMs, but it's almost like the compute market, like kind of doesn't realize that yet, both in the capacity sense, but also in like the software stack sense.”

“Actually OpenAI co-led our seed round.”

“There's a lot of bio companies, AI for bio companies that are like making their own drugs. We really don't see ourselves that way, right? We see ourselves as almost a neutral software factory. For making medicines.”

“We actually chose these targets specifically like to have no known antibody binder. So like if we did get a hit, like it was definitely the first antibody hit to this target.”

“We're kind of at the inflection point now. We're really seeing this internally at CHI, where the models are getting pretty close to, like, producing Molecules that could eventually, or like are very close to drugs.”

“We're only 30 people. And that's because everyone we hire onto the research team or the engineering team, you know, especially now that they're in some ways like very empowered with AI, A lot of it is just, like, allocating, you know, their attention into the …”

“So we designed antibodies to 50 targets for that paper, got binders to about half of them with being on average around a 20% hit rate for binding.”

“We don't care about going and developing those drugs. Like we just do that in service of validating and making our models better.”

“And in this case, it was a 0.33 angstrom error, which is one third the width of an atom.”

“You start to get to the point where now you can declaratively precision engineer what you want, rather than betting on, you know, nature or trial and error to get you there. And I think that Look, we had the same thing happen in software where you can write co…”