Recursion Pharmaceuticals, Inc. (RXRX) Earnings Call Transcript & Summary

I'm Jill Hall, Head of U.S. small and mid-cap strategy here at the Global Research. So I just wanted to welcome everyone to our day 1 of our virtual SMID-cap event excited to hear from corporates across the small and mid-cap space, cost sectors, great breadth of coverage here by our analysts. They cover about 1,000 small and mid-cap in the U.S. So I'm happy to bring nearly 20 companies today and feel free to reach out to me if I can help with the schedule or giving you signed up for any additional sessions or if you're interested in broader smaller mid-cap research, some of our compilation e-mails we send out on the fundamental side. So I'd like to pass it over to Alex for the session to introduce the company.

Thanks, Jill. As Jill said, my name is Alec Stranahan. I'm senior analyst covering biotech here at BofA. I cover around 30 stocks, ranging from $40 billion all the way down to $400 million in market cap. And one of the more interesting names delays Recursion, and it's my pleasure to be joined by Ben Taylor, who is Chief Financial Officer and President of Recursion U.K. And I would say speak just about as well about the science and AI aspects of recursion as he does about the finance side. So Ben, really happy to have you with us.

Thanks, Alec. I always appreciate the intra and the conversation.

Yes. Great. So as Jill said, I'm going to run through some questions here with Ben in a fireside format, but hope to keep the conversation topical for those dialed in and -- if you do have a question, please utilize the raise hand feature Vazo or you can e-mail me separately, and we'll get your questions asked. So then maybe just to tee things up for investors, maybe newer to the recursion story. -- and maybe AI drug discovery as a whole. What is that? Why is AI needed in the drug discovery process and how is recursion maybe blazing the trail here?

Yes. And 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 used computers are 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. And 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 multiparameter way than you've ever been able to do it before. And so 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. And 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 published some of the statistics on that showing pretty dramatic reductions and the time and cost to be able to get to those differentiated outcomes. So there's a lot of different pieces at. 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 are running for multiple years. And 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. And so that's really what we're focused on.

Okay. And maybe along those lines, and this is a question I get asked a lot, which is like for those paying attention, ChatGPT was kind of -- you could see it coming. But if you were most of the population, it was just like 1 day we didn't have LLM and the next day we did and the world feels like it's changed. Is there a ChatGPT type moment in drug discovery? Like is there a moment where the entire industry do you think will just 1 day be AI in terms of the back end on the drug discovery side? Or is it maybe a little bit different of a situation?

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. I mean, if I go back to the days when we were a private company, literally no one, 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. And now you look at pharma there have been multiple large pharma who have announced $1 billion-ish investments towards building out an AI and in investing in that. And 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. I mean, 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. And so it was seeing us demonstrate you can actually get to that different outcome. And so 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 high thing to talk about in biotech. But the outputs that they're doing, if you look at Paravalos, I mean there's a lot of AI that was involved in how they achieved that outcome and it was a great outcome. So I think the industry side has already occurred I think on the investor side, there's a lot of speculation about when and how and who. And so we haven't quite got to that moment yet. But hopefully, soon, I mean, 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.

Yes. Yes, I agree. Are there maybe 1 or 2 examples that you think kind of proof points for how the AI-driven approach or incursion is doing specifically can produce better medicines or is it really in the clinic? Or is it maybe just coal wrapped up and into that?

Well, it's funny. Hopefully, you respect this coming from a data-driven company. We don't like anecdotal examples. And so we really look for an accumulation of evidence that something is changing. And so -- how do we know if our biology AI is working like being able to target new ideas that weren't in the literature, they weren't common known and so now we've seen multiple examples of that, like 2 clinical examples for us with 881, which we'll talk about later and 1 with K12. And then with RVM39, that's another target for or in other areas. Those were novel biological insights. And then 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, neuronal cells and microplate cells. But now we've started to take completely novel programs from those maps and transition them into the design phase. So that's actually saying -- this is something that wasn't in existence as a neuroscience target or no 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. So I think those are 3 points that all point in the same direction of finding new connections and biology that doesn't -- that didn't exist before. One of the things that I always blows my mind -- if you look at all of the drugs that the pharmaceutical and biotech industry have created over their entire life span all of the good people and all of the money that we put in, the approved drugs only cover about 3.5% of the genome. If you add on all of the drugs that are currently in development, we think of this massive pipeline of drugs that are coming through and all the innovation that's going on, you're still only covering about 13% of the genome. And so the reality is we keep digging in the same holes. And so what we want to do is create new data, look at it in new ways so that we can actually break outside of those holes that we've been taking over and over again and really find new path. So that's why the biology side is really exciting to us. I think we can also talk about the chemistry side. I mean, we've had many milestones with Sanofi and other partners advancing through as well as being able to demonstrate how our chemistry is actually achieving things that other chemistries have not. I think we'll hopefully be turning over cards on the clinical side on that soon, but it's been exciting to watch both of those come together.

Yes. That's a great way to sort of sum things up by bank for the state of the industry. And we hear more and more the actual generative AI models are not necessarily the big differentiator. There's still maybe an edge to be gained on compute and you guys have your supercomputer in-house that you've already built out. But it's really more and more, it feels like the data side, that's -- and kind of how you pump that through the funnel internally and then spin the flywheel based on that data and generate new data from quality foundational data sets, that seems how you build a good platform. Maybe you can just talk about that piece sort of how recurrence built that kind of from day 1 and sort of where you see that in dataset being leveraged either internally or through your partnerships?

Yes. Well, it's a really great point because if you think about why haven't we gone into more of that 87%, where there's literally not even a drug candidate out there, much less something approved. I mean a big part of that is because you can only go after what you have some sort of diagnostic or assay system or some way of understanding what good looks like. And so this is where coming in and being able to create and look at data in new ways, makes such a difference because if you're just focused on the algorithm and you're just trying to look at the data that's already in existence, you're going to get a lot of the same answers. You're just -- you're certainly not going to have differentiation from other people who are doing the same work. And so you need to really be creating novel data to be able to look at it in different ways and analyze it. Most of the data that's created across the biopharma industry is not good for machine learning because it's been created in a format that's usually very local. It's usually done for a single project. The annotations are quite different from project to project. And you can use it as fluidly. And so that's where starting from the beginning, I mean, more than a decade ago, have been creating novel data in sort of a machine learning ganitated format that we can then consolidate down. And so that's where we get to over 50 petabytes of having that data in existence that's very differentiated from what is in existence in other places. And if Najat was on, so a Najat, our CEO, who used to head up AI at looked at it and said, all of the data inside of J&J, probably only about 30% of it was really something that could be used for machine learning and even that had a lot of data wrangling. And so this is where trying to build up unique ways of looking at the data can get you down into really new spaces. And I think that's where a lot of the excitement on our side and as well as across the industry business, how can I analyze this biology signal in a different way, whether it's phenotypically or applying overlaying cellular imaging with transcriptomics and proteomics and other sort of assets, you get this much richer set of data. I think the last point, this is also a place where we've really felt the differentiation value of just having a lot of the breadth that we have because we started in the phenotypic cellular imaging, like that was the original base. And it's sort of a new language for being able to look at biology, which is incredibly powerful, but then being able to overlay that with orthogonal data sets like the transcriptomics I was talking about of proteomics or looking at real-world patient data and figuring out genomic signatures. What that allows you to do is actually synthetically or virtually compare the data output and the analysis. And so you can actually dramatically improve your ability to find real signals because no matter what source you're using, there's always going to be a lot of item. And so we published a paper in Nature Biotech just recently that showed we were able to actually outperform models that were even up to 100 times the data set size that we were using. But it was because our data was better annotated and we were able to use multi-mill sources to basically enhance the signal that we were seeing and make far better predictions on it and that's sort of how you get to more composite biology and systems biology and where we hope to go in the future.

Great. Yes. I mean the same garbage in, garbage out that still holds true, maybe even more so today. But maybe the ground spend some of the construction -- the conversation that we've been having so far, -- you mentioned your Roche Genentech partnership. You recently selected the first neuroscience target here from your collaboration for further drug development. So I guess, what did that target need to demonstrate for you and Roche to be convinced that this is something worthwhile taking forward and why should investors maybe view this decision as important validation for the platform.

Yes, absolutely. So just to take the step back on the partnership as a whole. So Russian regionally given us $150 million to go out and basically build maps, most of that was to build maps in 2 different neurology cell lines, so neuronal cells and micro clear cells. And those maps were basically hold genome knockouts and other pervasions of those cell lines and then looking at how did it morphologically change? And then we overlay on some of the transcriptomic signals to understand what might be happening within those cells. So then they auctioned in both of those maps. They don't get any of the data, but they are able to query them out basically and that was $2 million to $30 million payments to be able to do that. So that's already all happened. Now basically, what the milestone that we just got is from those queries, we get a list of potential targets. And from those targets, we selected a few that we need to go through and do a validation on -- some of that validation is still AI-based and really how we selected the targets that we wanted to do. But most of it, we went to experimental systems. And we said, let's test this in a disease model, we know, let's look for how this is disease modifying to different cells of interest or diseases of interest. And so in a very classical way demonstrate that this novel virtual finding is having real experimental impact. And so we went through a process with Roche. They're obviously a world leader in neuroscience and -- that was what triggered the validation. And so now we're taking that target and recursion is designing the molecule to be able to drug it.

Okay. Okay. Got it. And I think 1 point that we shouldn't sort of gloss over is that this was a target in neuroscience that is novel, right? There's been decades and decades of research in CNS diseases and your platform and your neuro maps were able to uncover something new. So talking about being holes in fresh oil.

Absolutely. Well, and by definition, everything that we do out of that partnership, this isn't going to be something that you can find in the literature or that someone is doing an alternative program for. This is wholly new work in neuroscience. So really, really exciting to see that progress and hopefully a lot more to come.

Okay. And maybe you could just remind us of the structure of the partnership with Roche. How many candidates could you bring forward? What are sort of the economics around bringing those forward? And then maybe you can talk about how that structure is maybe similar and different from your Sanofi partnership as well.

Yes, absolutely. So the Roche partnership originally a 10-year term, which obviously we can extend, and we're about halfway through it right now. I almost feel foolish stating the number, but it's up to 40 design programs that we can advance and actually part of the rationale for the recursion Acentia merger, which is now almost 2 years ago, was to bring the design capabilities in-house with recursion target ID. And so it's actually really exciting that Roche wants recursion to do all of the chemistry work and direct design work because prior to the merger, I don't know if that would have been true because we really have built out the capabilities. And so it's great to see that coming together. Now what we hope to do is basically create a pipeline of additional targets coming out in neuroscience. We also have work ongoing in some GI oncology as well and to be advancing that as a long-term partnership with Roche. The Sanofi is a little bit different in that there was not a target ID portion to it. It was -- this was a legacy Acentia deal. So it was really focused on hey, there's a target that we mutually are interested in. No one's ever been able to drug it before can we advance a candidate in it? And so now we've already seen 5 programs hit their first discovery milestone there. And so the next milestone for all of those would be basically opting in for Sanofi to take it forward into clinical panels. And that is really exciting because that not only marks that we'll be advancing again, really exciting new potential blockbuster programs. But also that it ends our operational obligations. So all of the payments from development can work candidate onwards are basically a profit for us. And so if you look at the Sanofi collaboration, each program has the potential for up to $343 million in milestones, $193 million of that is pre-commercial so there's not some massively back-end loaded deal. And then our royalties on it are averaging the low double digits. So we actually capture a pretty substantial part of MPV both of those programs and the Roche design elements are actually similar to Sanofi, not quite as high on the economics, but close -- and for both of those, they're really designed to be more of collaborations, but ones where -- we are always at breakeven or profit on a direct cost basis. So Sanofi and Roche and our other partners pay us ahead of time. for our expenses. So it's a really capital efficient way for us to grow value.

Yes. That was an important point that I think you also mentioned on your 1Q call about the cost to service these partnerships, and that's a question I've gotten. So it's good that they're designed to not be a burden on that -- and we've seen in the space, a lot of different approaches to monetizing these AI drug discovery platforms. You've got like the Schrodinger in that world that are more like a SaaS type revenue model. And then you've got like in silicones, which are maybe kind of a mix. And then you guys are more of like a hands-on, let's do interesting science together and leverage the platform to push those forward. But it's a little bit more hands-on, but you also get larger, chunkier deals out of that as well. So maybe you could just talk about kind of the philosophy around the partnership model and how you balance that with in-house development.

Yes. Well, and it's interesting. So if you go back to our original mandate as a company, there's really 2 parts to it. One was how do you change the probability of success using technology, right? Like -- we're in a 95% failure environment. No one is making data-informed decisions not because they don't want to, but because they can't. The data is not good enough, the models aren't good enough. You can't make good predictions. And so that's how you get to a 95% failure environment. So please improve on that. The other part of it was how do you make this into an actual business model rather than just a binary risk bet. And so that's where our ability to do things at scale more efficiently really comes into play. And so you can almost think of the partnership business as an outgrowth of that. So from early days, we decided having our own therapeutics was really important because -- it allowed us to demonstrate that the platform is working. And also, we were creating a massive amount of value. So being able to capture it as we get into those points. And that's where all of the upcoming clinical data are exciting because those are obviously amounts of potential transitional points for us. The partnership business, though, is a beautiful part of being able to fund the company, build the platform and grow the long-term NPV really well. And it makes sense because we do things at -- or we can do things at scale. And so we would actually be leaving some of our capability dormant if we didn't have the partnership build. So the fact that we can get paid early on, use that to actually do a lot of applied development. So one thing that most people don't know, about 65% plus of our budget is actually applied. So even when I'm talking about platform and technology development, like we're doing it on real programs. And so that's part of our edge, like we know if our models work because if they don't, the drug doesn't get made, right? Or the biology is and it doesn't work out. And so like the partnerships is a great applied platform for us, where we build out our platform, test our models and be able to add that in as a part of the overall product and on. And so we've always loved it. We don't want to become a service company that's a different set of economics. It's a different business model. There's a lot of infrastructure -- in fact, how we run our partnerships is basically exactly how we run our internal programs. We just have a partner that we're strategically working with and talking about what good looks like. And so that's where we differentiate in our model from the more service-oriented side.