Recursion Pharmaceuticals, Inc. Class A Common Stock Bank of America SMID Cap Virtual Conference

Hi, everyone. I'm Jill Hall, Head of U.S. Small & Mid-Cap Strategy here at BofA Global Research. Just wanted to welcome everyone to day one of our virtual mid-cap event. Excited to hear from corporates across the small and mid-cap space, across sectors. Great breadth of coverage here by our analysts. They cover about 1,000 small and mid-caps in the U.S., so I'm happy to bring nearly 20 companies today. Feel free to reach out to me if I can help with the schedule or getting you signed up for any additional sessions or if you're interested in broader small or mid-cap research or some of our compilation emails we send out on the fundamental side. I'd like to pass it over to Alex for this session to introduce the company.

Thanks, Alex. I always appreciate the intro and the conversation.

Yeah. I think it's really good to level set because AI, especially, currently is waved around like it's a magic wand, which it absolutely isn't. The way that we really think about it is, it's a better analytical system. So it's more similar to the evolution of starting to use computers or starting to use spreadsheets, how those things have changed the way that we do business and look at data analysis. I think AI is another step up in that. So where we have been able to apply it to drug discovery and what makes us different, is it allows you to both produce and analyze data in a different way than you ever could before, and also do it in a more multi-parameter way than you've ever been able to do it before.

By putting together the data with modeling systems, with the ability to compute, we can really get to a different outcome than was historically possible. So the foundation of the company is actually around changing the probability of success and really trying to unlock new parts of biology and chemistry rather than efficiency. But we have also been able to do it much more efficiently and we've published some of the statistics on that showing pretty dramatic reductions in the time and cost to be able to get to those differentiated outcomes. So there's a lot of different pieces at play. I think the nice thing about being in our shoes is we're actually at the point where we have multiple clinical programs. We have partnerships that have been running for multiple years.

So you don't actually have to understand all of the AI, just like people didn't understand drug discovery and biotech for many years. You just have to understand the output of it. So that's really what we're focused on.

No, I think it's absolutely the same, but rather than it being the entire world sort of figuring it out at once, I think what you've seen is more of layers. If I go back to the days when we were a private company, literally no one, the large pharmas, the investor base, no one was really using AI to evaluate drug discovery or try and build some of the models that we're doing now. Now you look at pharma, there have been multiple large pharma who have announced billion-ish dollar investments towards building out AI and investing in that. That's because we're actually getting better results over and over again. It's repeatable. It's not just one-off. We're doing things in a better way. We're more efficient and achieving things. All of our partner milestones, we've achieved well over a dozen partner milestones.

All of those milestones where they paid us millions of dollars were things that they couldn't do internally. It was seeing us demonstrate you can actually get to that different outcome. I think that there's still another level of having it be more generally accepted. So inside of the industry, people are absolutely using it. It's funny, some of the biotech companies that are coming up now, they don't talk as much about the AI because it's such a hype-y thing to talk about in biotech. But the outputs that they're doing, if you look at Gravitas, there's a lot of AI that was involved in how they achieved that outcome, and it was a great outcome. So, I think on the industry side, it's already occurred. I think on the investor side, there's a lot of speculation about when and how and who.

We haven't quite got to that moment yet, but hopefully soon. Really, on the investor side, I think it's a matter of demonstrating that the products really make a difference, and we're very cuspy on that, it feels like.

Well, it's funny. Hopefully, you'll respect this. Coming from a data-driven company, we don't like anecdotal examples. We really look for an accumulation of evidence that something is changing. How do we know if our biology AI is working? Being able to target new ideas that weren't in the literature, they weren't commonly known. We've seen multiple examples of that, like two clinical examples for us with REC-4881, which we'll talk about later, and REC-1245, so in FAP, with MEK1/2, and then with RBM39 as another target for DDR in other areas. Those were novel biological insights.

What we just saw with the Roche collaboration milestone is not only had Roche opted in on the biology maps that we created around neuroscience, so neuronal cells and microglial cells, but now we've started to take completely novel programs from those maps and transition them into the design phase. That's actually saying, "This is something that wasn't in existence as a neuroscience target or known biology, and now we're transitioning it into something that can be a drug because we've done the target validation work on it and experimentally validated." I think those are three points that all point in the same direction of finding new connections in biology that didn't exist before.