Hello, everyone, and welcome to this next session on the final day of the 28th Annual Needham Growth Conference. I'm Ryan MacDonald, and I lead Needham's healthcare technology research efforts. And in this session, I'm pleased to be joined by Recursion Pharmaceuticals CFO, Ben Taylor. Ben, thanks for joining me today. Pleasure to be here. Thanks, Ryan. Yeah, thanks for coming. All right, so for those who are joining us, first of all, thanks for joining us. And second, we are going to have about a 35-minute fireside chat conversation here. I've got a number of questions and topics that Ben and I are going to run through. But if any of you have questions for Ben that are listening in, please put it into the chat box, and we'll make sure to get that asked and answered if there's some time at the end. But with that, Ben, let's get started. So for folks in the audience who might be newer to the Recursion story, how about giving us an overview of the business? Sure, of course. Well, and I think one of the really important parts is actually taking a step back and thinking about why we're here at all. So there are well over 1,000 biotech companies out there, and we actually have quite a different mandate from most of them. So a lot of our foundational investors, so the Baillie Giffords, the SoftBanks, the Novartis of the world, came in and wanted to invest behind us because they were looking for something that broke away from the binary risk model of biotech and really got more towards a balanced business model. Now, all of those investors are big tech-focused investors as well, where they want to see transformational change in an industry coming out of technological advancement. And so if you think about our operations, everything we do tries to integrate in AI and automation. So we do both what we call dry lab and wet lab. So in silico work on the tech, we've really been leading the industry on a lot of the advancements in how do you image cellular systems and how do you think about biology in different ways, how do you create novel chemistries in different ways. All of that is using AI to power it as a new tool and modeling system. We also are world-leading a lot of our experimental capabilities. So we've been able to do things by using a lot of automation that has allowed us to create new data. We have over 45 PB of proprietary data that we have generated internally in our own labs, which is a huge differentiator and allows us to drive a lot on those modeling systems. Those two things have come together to allow us to have both an internal pipeline, so more like a traditional biotech where we just announced great data on our most advanced asset, but we have a number of them coming through, as well as a sizable partnership business. We've brought in over $500 million worth of cash inflows out of our partnerships. That's predominantly through our largest partnerships with Roche, Genentech, and Sanofi. Excellent. Excellent. And And before we dive sort of deeper into the business, I wanted to at least kick things off and just kind of talk about the recent succession plan that was announced with Chris stepping into the chairman of the board role out of the CEO role and him being succeeded by Najat Khan, obviously a familiar face with investors and someone who's certainly not new to Recursion. But as you think about sort of this transition and sort of what were some of the key initiatives that Najat's excited to work on? And does her vision differ materially from sort of Chris's leadership today? The mission, absolutely not. I mean, if you look at where we're going long term, I think that's exactly the same spot. If you think about that mandate I was talking about, I mean, one of the problems in the industry is we have a 96% failure rate, and beyond that, so I've been in the industry for over 25 years. All of the innovations over that period, we are still only drugging about 10% of the genome. I mean, that means there's about 90% of biology that's out there that we don't have a good way to understand. We don't have a good way to drug, and so it's really untapped potential, and so that mission and vision of where we want to go in creating new ways to achieve that and improving the probability of success has not changed at all. I think it was a really natural evolution between Chris and Najat. And Najat had come in and ran our R&D operations for 18 months before the transition. So they both worked well together and got to sort of build the business plan around it. So it's been a very smooth transition from all of those perspectives. And obviously, Chris is still on the board and helping to give us a lot of advice and guide strategy. So if you think about where we want to go, some of the changes that Najat really brings in, it's a lot of operational tone and discipline and looking at how we go about advancing into the next level of the company's trajectory. So Najat was at J&J for many years. She oversaw their portfolio review committee across the entire company, and so obviously was helping guide J&J's decision-making process on what to advance and not. During that time period, the value of their portfolio just massively. It was about a 3x on the growth in the portfolio. So that was a huge change. At the same time, she was also their chief data science officer. So she built from the ground up J&J's AI capabilities, how they think about data and data science. I think it's very telling that she did want to still come to Recursion. And that's really because we are an AI-native company. We just have so much more freedom to be nimble, to operate in a different way, and execute on it. And so during her first 18 months, we did a lot of work changing around the portfolio already and aligning the products that we wanted. We did a lot of work sort of building different ways that we collect data and analyze it. Now, going forward, I think what you'll see is a real transparency and discipline to how we make decisions, lots of go, no-go decisions, so that it's not that classic biotech model of, "I'm going to spend $50 million or $100 million or $150 million, and then I'll get an answer." It's like, "Can we spend $5 million or $10 million and get to that same point?" So you'll definitely see more of that. You'll see us be very disciplined also with the cash management. I mean, she and I have worked very closely together over the last year to take over $200 million out of the pro forma spend from 2024 to what we give guidance as for this coming year. So that's a 35% reduction in our costs. We haven't actually changed what we expect to do. So we announced our strategy last May, and we haven't backed down from any of those pieces while still being able to find that efficiency. And that's really a fundamental belief that we are a tech-based company. We should be finding more and more efficiencies. The gains that are going on with agents and with compute and with all of those different aspects benefit us directly. And so we're going to keep taking advantage of that and implementing it across the company. Yeah, it really has been great to see sort of the natural evolution and maturation of the company, even particularly as you and Najat have entered the business and sort of kind of helped to guide strategy as well. So very exciting point in the overall life cycle of the Recursion business. I want to take a step back a little bit and talk about sort of the market dynamics, macro dynamics. So you talked about, obviously, we're in the very early innings here within tech, AI, data, and how sort of pharma companies, biotech companies utilize this. And I'm curious to get your view on how, from the conversations you have with your partners, how that large pharma sort of view on data strategy is shifting overall. Obviously, Lilly's made some announcements around opening up and sharing access to its AI models. The news of the week for them was NVIDIA, who's obviously also a partner for Recursion. They're going to kind of market and sort of partnering there. What's the market dynamic? How's it changing? And do you feel like this is a tailwind to the Recursion business or a validation, if you will? Absolutely. I mean, I take a step back and say, "Okay, everything that the entire industry is working on now covers that 10-ish%, whatever the right percent is, that we actually have some understanding in biology and chemistry right now." I mean, that other 90%, we need new tools. We need new ways of looking at data to be able to penetrate into it, and it's vast. I mean, any of us can say we don't have our arms around disease right now. There are so many different illnesses, ailments, ways just to be more healthy that we currently can't do, and so what you need to do is start to come up with new ways to create and analyze data, and that's what we were very early pioneer in, and I think Lilly is definitely taking a great step. I love the fact that they're building the supercomputers in the labs. I would expect more people to do it because if you actually take the results that we're seeing, and it's really important to remember, we're working with AI, but we create things, right? This isn't a concept that we're doing. We create a chemistry or a biological target that you can go to a lab and test, and what we see is we are achieving things that traditional methods didn't, so it is not surprising to see other people say, "Hey, I want to start to look in that area." I'll say there's a learning curve to it, and I'm sure the industry will get their arms around that learning curve too. We're going to continue running fast, but even if people do also try and really run fast and do put a lot of investment in, the amount that we are not doing right now is really mind-blowing. Yeah, I was going to say, so there's plenty of opportunity because, like you said, there's only that 10% right now of the targets. But for those who look at this and say, "Well, okay, some of these large pharma are going to try to build it themselves instead of maybe partnering," you talked about the learning or alluded to it with the learning curve and how steep that is there. Can you just refresh investors on the defensibility and how hard it is to build what Recursion has built? Sure. And I mean, there's a couple of different components to it. I'll start with the data because I feel like that's something that a lot of people understand. So publicly available data is, and actually, this is true of almost all of the privately held data as well, so most of the data that pharma companies have and other groups, it's done on almost always an individual project-by-project basis. So imagine you, Ryan, decided to run an experiment. You're going to define the data that's collected, how it's collected, the format that it's kept. Even things like how you're going to refer to the target name could be different than if I ran that same experiment. And it often is. And so when you think about that publicly available data, one, the annotations are wildly different. The formats are wildly different. Two, there's been a long-held practice of basically publishing the positive and not the negative data, and the negative data almost teaches you more, and so those two things really work against a lot of the public data sets in being very strong. We have seen one success really come out of the public data sets, one strong success, which was AlphaFold, and AlphaFold, and we obviously worked with MIT and NVIDIA on Boltz-2, which was sort of what we would look at as the next generation of that in bringing and binding affinity as well, but the reason that that was possible for Google to go out and do originally was because for the last 30-some years, there has been a public database where people were collecting unified data on certain types of protein structures. And so there was more than 30 years of data that was annotated in a consistent way and available publicly. That's really the only part of biology where you'll find that. And so that's why there haven't been more breakthroughs in other areas. That leads us to say, "Well, we needed to develop our own data in-house." And so we always look at publicly available data. Oftentimes, our partners will share data with us as well, and we'll try and use that as sort of a starting point for some new projects. But in the end, what we've had to do is really create a lot of that data in-house. And that was going back to that wet and dry lab piece. So for over a decade, we've been creating our own data in-house. We have about 45 PB worth of our own internally generated data. All of that is done in a format where we can use it to inform models and drive exactly the sort of outputs that we want. I think, in addition, there's a level of understanding what data is important. When you first start collecting data, it takes a lot of time because you don't know what data is important. And now we don't need to continue generating the same amount of data to get the same result. First of all, we may already have data that gives us the answer. Second of all, if it doesn't, we probably need much less new data to get to the answer than we did 5 or 10 years ago. So I think that's a really, really important piece on the data side. On the modeling side, there are a lot of people out there that can write algorithms now. I mean, algorithms can write algorithms now, and so the important part is actually how do you validate it, and then how do you layer on top of it things? I'm going to talk about something that's really unexciting to most of the world, but so we generate all of these different chemistries and have this generative AI. We were using generative AI a decade ago before it was a big term, but you can do that all day long. If it doesn't create meaningful molecules, it doesn't matter, and so one of the things that we've designed is everything that our system generates has to be what is called synthetically aware. Now, again, sounds really boring, but that means it has to be able to be manufacturable in a reasonable way, at a reasonable cost, and something that is actually drug-like. These are things that you have to layer on top of it. That takes a lot of time and trial and error to get right. The final piece is we're the only company that has that end-to-end spectrum. We can create a new biological idea. We can create a drug to drug that new biological idea. We actually use a lot of real-world data, patient data, to be able to better understand that patient, better design a clinical trial. Bringing all of those components together, we actually use them interchangeably internally. It's not like that's a handoff on the progression. We use our development clinical data at the very beginning of a process as well. That integration of those systems and being able to think about all of the different types of problems that plague a drug is really important. Now, I want to be clear. We do not solve all of those. We are a very applied company. And this is another last really important differentiator that I'll talk about. We're much more focused on how do you create a system, an experiment that improves the probability of success of this drug or this program rather than trying to solve it for all of biology or all of chemistry or those sort of things. That is just, it's unlikely to happen given how little we know. And so what we want to do is really focus that in. So 75% of our spend is on our pipeline programs and our partnerships. And so that's all applied learning platform development and growth that we're doing. As you said, you have the great thing about the Recursion business is that in how it differentiates from your typical biotech is this platform is validated through your partnerships. You have great strategic partnerships with Roche, Genentech, Bayer, Sanofi, BMS. And so I guess how is the evolution of those partnerships as they've matured in those relationships trended? And then what's the pipeline for similar partnerships look like over time? What do prospective partners want to see out of Recursion? Yeah, really great question. So we've brought in over $500 million from our partnership inflows already. And actually, I've seen a lot of momentum in the most important element of that, which is the milestones. So not just getting in some great upfront payments. We love upfront payments, but the milestones are what tell you if the programs are actually working. And that's also where the partners also get invested. So for Sanofi, for example, we've hit four different program milestones for Sanofi just recently. And what that means is those are four different drug programs where Sanofi and the rest of the industry hadn't been able to solve the problem themselves. And so we come in, that initial milestone is really around, it looks like we have solved the problem. That was the design goal. Now we have to do a bunch of additional experiments to make sure it's a good drug to take into the clinic. But those are the sort of validation points that not only get us really excited and show that the platform is doing things that outside parties care about, also brings in great money, but it gets them very excited. And so Sanofi and Roche, which are our two largest partnerships, regularly talk about us at their investor meetings and their different presentations. And they're invested at the C-suite level to those partnerships. So that's really the sort of thing that I look for is, are we hitting milestones, which means the technology is working, and do we have strategic engagement from the top of the company? And we're getting both of those to come through. Now, one of the nice things, we have fantastic economics on both of those programs. So Sanofi, for example, $343 million in potential milestones per program. $193 million of that is pre-commercial. So this isn't some big back-end loaded biobucks thing. And then average double-digit royalties for each program. So really, really nice economics. Roche is structured similarly. Once we start doing design, there's just this big end of it. So we've brought in $60 million in what are called Map milestones, which are us doing something different with cells and cellular imaging that can highlight new ideas in neuroscience. So they've paid us $60 million in milestones just for achievements on that. And so you'll see that start to translate into drug programs as well. We've started to do that in oncology with Roche already, and doing it in neuroscience should be very exciting. As far as new partners and what the other groups are looking for, it's an interesting question for us because obviously those successes draw a lot of interest. People are looking for a partner who can solve problems they can't do internally. One of the things that we're always weighing, though, is the economics for those two relationships are very good. They're both very large. So for Sanofi, we can go up to 15 design programs. For Roche, it's up to 40. And so we have a lot that we can still go deeper with them just on the existing contracts. And to some extent, part of the value proposition that we get is semi-exclusive value, right? I think they like the fact that a lot of other large pharma don't have access to our technology. And that's why we get such great economics. Certainly, I worked at a SaaS company for a while. We are getting far better economics than we ever got as a SaaS company. And we want to keep that up. So we look, we are in discussions. We'll always be and certainly could do additional partnerships. We could also do expansions of our existing partnerships. Really a lot of room to grow. Yeah, excellent. I want to touch on some of the recent data readouts you've had. So forgive a tech analyst if I'm mispronouncing some of these things. But maybe starting with the ongoing study for REC-4881 for patients with FAP, what's the therapy aim to achieve, and just kind of give us the table stakes for their investors? And then can you give an overview on the latest results from the phase 1b/2 TUPELO study? And what's the next key milestones for development that we should be keeping an eye out for? Yeah. Well, FAP is an orphan indication. All that means is it's a smaller patient population. It gives you regulatory advantages and some different commercial differences. It is not the normal type of orphan indication. It's about 50,000 patients in the U.S. and EU5. It's a relatively sizable patient population in comparison, for example, to most cancer markets. Most cancer markets sort of tend to 30,000 or 40,000 patients. It's larger than most of them. It's a really tough indication because it's chronic, lifelong disease that if left untreated, will with almost 100% certainty progress to colorectal cancer. Most of the patients in it are familial. They're getting it from one of their parents or some other family member. They know who they are. That's also another big difference. Oftentimes in orphan drugs, you have to go out and find patients. These patients are seeing their physicians three or four times a year. Most of the time, they might be getting colonoscopies three or four times a year to have polyps removed. They generally will have colectomies somewhere in their 20s, and that can progress on to more and more resections over time. So it's a really, really difficult disease, high patient and economic burden. And what we saw in the results that we just announced in December was we were able to reduce polyp burden by which the polyps are what leads to the cancer, all of them malignant. Polyp burden by 43% median within three months. And just to put that in perspective, there is no approved therapy for this besides surgical intervention. The most compelling other clinical results were a 20% reduction over the course of a year. So this was very strong in the reduction and very fast onset. Plus, we actually took patients off for three months because we wanted to see if it was a sustainable result. What we saw is it was. The median reduction stayed about the same. It actually grew a little bit over that three-month period, which is really exciting because you think about in an ideal world, you don't have to take a drug every day for the rest of your life. You can sort of take it intermittently for the rest of your life on and off periods. That would be part of the goal. We do want to look at things. We only drugged for three months. Does that deepen if you continue drugging for longer periods? And also, we're currently going back to the FDA to talk about the pivotal trial design. There is a pivotal trial design that we know that we could use. What we want to do is talk to the FDA and see if there may be a more basically a faster, more effective clinical trial design that we might be able to do. No one's ever had data like we did. We were also able to use our tech in another way. So we have a clinical development technology business. We created an LLM over the course of a week to search through about 300,000 patient records and create the first of its kind look at what is actual standard of care for these patients because there's never been a study of what does the patient journey look like? What are clinicians actually doing? We were able to use our technology to really get a great understanding of that and also look at what normal progressions would look like. We saw on average polyp count increases 60% a year. That reversal puts more context on. We want to do is take that data to the FDA, have a really data-driven discussion with them about it, and see what we can do. Yeah. And just what's your feedback or what's your sense of the FDA under the current administration? It seems like they've been a lot more forward-thinking in the use of data and AI or trying to find ways to move faster. Yeah. I mean, are you getting that sense that that's sort of not just press releases and actual and real discussions, if you know what I mean? Yeah. I mean, we continue to have positive discussions with the FDA, and obviously, we love the initiatives that they're taking in AI. I'll never get in front of the FDA until we've got agreement on whatever they want to see. We'll continue talking to them about it. Excellent. Excellent. The other program I wanted to at least touch on and talk a little bit about is the CDK7 inhibitor and the REC-617 program. Could you give an overview of the program itself and then highlight any recent milestones and timelines for the future that investors should keep an eye on? Yeah. And it's funny because one of the important things that we want to do in getting people to think about us as a business model is I'm personally really excited about CDK7. It was part of the Exscientia side. I come over from the Exscientia side of the merger. And that was a program we really grew up over the years. But we want to take all of the personal out of it. We've got four drugs that have really important data points over the next 12-18 months: CDK7, MALT1, LSD1, RBM39. And all of them have had important changes or important impacts from how our pipeline looks at the biology and the chemistry to, we hope, improve the probability of success and solve really important problems. And so what we want to do is all of them are early stage. All of them could be blockbuster drugs. Let's let the data lead us. And so as those readouts come out, we will inform the investors as quickly as we possibly can. If it's good, we'll be off to the races. If it's not, we'll kill it immediately. And so the nice thing about the way that we are able to do business is we can take a portfolio management approach to it. And that's really different from this is my lead and this is my second compound. Yeah, makes sense. Okay. Well, you've done a really good job on the strategic and some of the science questions. We're going to give you some CFO questions now, right down the middle of the lane here. Let's talk about the balance sheet, obviously. You reported or announced earlier this week sort of with JPM Healthcare that you're expecting to end 2025 with $755 million in cash and with an expected runway out to 2027. How should we think about the rate of cash use for program developments? And just talk about the level of confidence you have and maybe just what are the biggest swing factors from an OpEx perspective that could either shorten or maybe even extend that runway? Yeah. Really, really good question, and it's funny because talk about another boring thing that no one wants to hear about. We've spent the last year rebuilding all of our backend systems. So I'm here in Salt Lake City right now, and my whole team's here because we went live on all of the instances January 1st, and it is actually transformational how you can manage a business. This is why it becomes I am answering your question. What we've done is transform the entire company into what I'd call an outcomes-based model, so we can actually measure every dollar, every time commitment of people, everything else to an actual outcome that the business is trying to produce, and we did that across everything. It's not just our pipeline programs. It's like, okay, what technology development are we doing? Or what R&D activity are you doing, right? There is an actual outcome that we're trying to achieve, and so what we can actually do is look at this and say, okay, this is exactly what we're spending on XYZ program. How can we be more efficient on that? We can, in real time, go from our financial projections to our financial reporting, and that's a simultaneous system, so if you think about that question on what happens to the business model over time, you can be very, very nimble on saying, okay, CDK7 worked. You already have that scenario planned out, and you know exactly what additional resources you need and what's going to turn on. CDK7 didn't work. I know exactly what's got to turn off. I know what that is: direct variable costs versus fixed costs that we need to figure out if that fixed cost makes sense anymore and how we're going to do it, right? And so that sort of flexibility allows us to be incredibly data-driven. So if you take that step back and look at it from a business model perspective, now you start to think about, okay, we have our 100% wholly owned pipeline, and then we have our partnerships. Our partnerships are basically pre-funded. So that business is made so that it's meant to be break-even or slightly profitable from day one, where we get upfront payments that cover our direct costs. And then the milestones, if we have operating obligations, will continue to cover that. But like with the Sanofi programs I was talking about, those four programs, the next milestone is what's called development candidate. That's when they in-license it, and then they'd start to take it into the clinic. One, those milestones are larger, but two, they end our operational obligations. So all of the payments after that and forever more would be profit to us. And so that's where we try and create these partnerships where we're getting money upfront so it's not capital off of our balance sheet to do the development. We actually get a lot of platform benefit off of doing those programs. And then once they're hopefully in-licensed, they become a profit stream going forward. On the internal pipeline view, we have a lot of different scenarios based on how many compounds are moving forward. So if FAP is the only program that moves forward and the other four, let's say, die, then we would maintain the best economics on FAP and do the investment behind it. We'd obviously also be shifting that investment to bring new candidates up, to bring that balance. If FAP continues on, which it's expected to into the pivotal, and then we have those other four programs all be successful, we are unlikely, very unlikely, to take all four of those programs ahead ourselves because you're talking about a cost burden. So then you can think about out-licensing. You can think about co-development. There's a lot of different optionality that comes into it. But we don't have to make that decision today, and we won't. We're going to constantly be looking at what's the value of a program, any program. There's none that are sacred. What are our capital needs and expectations, and what's the market value that we could get for it? Excellent. And as you think about the cash collections, the milestone payments that you get from those partners, what assumptions are you making for the inclusion of achieving some of those milestones within your current cash runway projections? And I guess what level of visibility do you have in terms of the timing of that? Yeah, sure. So what we found is the design programs themselves and the map builds are pretty consistent in timing. Within a few million dollars and within, I don't know, six months, we can probably tell you what the different stages will be and when the go/no goes will go. There's some variation, but it's pretty clear. We've done about a dozen development candidates so far. We've seen the data play out enough times. So we use that visibility to predict new programs as well. So we've got enough data to be able to start to use that. And then what we try and do, I'm almost ashamed to say this, we actually use industry standard probability weighting rates for our internal models. Now, obviously, our entire business model is on changing those probability rates to be better, but I would rather take the conservative viewpoint and say, you know what, our probability is whatever the industry standard is, and then we probability weight the milestones over time of that, and we offset our operational burden, but we only do it for our existing programs. I don't believe in baking in new business development or things that you just don't have visibility on. Makes sense. Makes sense. Music to conservatism on the probability wave. Music to investors here. So that's great. And maybe just lastly, as we finish up here, how big of a priority do you think about capital allocation is M&A? I mean, obviously, a very great platform you have today, lots of great data that you're generating every minute, every hour. But how are you thinking about M&A, or what areas would you kind of look at? Yeah. It's something we do think about a lot. We definitely don't need to do any M&A, but there's sort of two buckets where it might make sense. One is if there is a compound. So FAP, it's the only drug in our portfolio that we didn't design in-house, but it was basically sitting on the shelf at a pharma company. And they had no idea it could be used for FAP. No one in the world knew that a MEK inhibitor could be used for FAP until we showed them. And so we went and in-licensed it a number of years ago. This was earlier on in the company's history. And it's obviously doing great, right? And so there is a possibility of finding new uses for drugs that people don't understand because they don't understand the biology enough. And we'll keep our eyes open for that. We have had absolutely a number of pharma and other partners come by and say, "Hey, you want to take a look at what's in our closet and see if there's any value there?" So we'll keep our eyes on that. The other aspect is really thinking about something where it builds our capabilities or utilizes our platform. I mean, we won't do anything that doesn't directly involve our platform. That's just not our business. There's a thousand other pharma companies out there or biotech companies that could do that. What we want to do is there has to be novel insight coming out of us. We only work in proprietary compounds for our own development. We only want to be doing something that's differentiated. So if there were an aspect of technology or some other component of the platform that was really differentiated and we thought that we could drive a lot more value than whatever it is getting right now, that might make sense. Awesome. Well, Ben, we've come up on the time. By my account, you were closing a fiscal year. You completely redesigned your internal processes and have spent a week doing conference presentations, investor meetings. I think you've earned yourself a nap, so. Thank you. I don't think I will get one, but I appreciate the sentiment at least, so. Thanks again for joining me today. Thanks, everyone, for listening in. We'll leave it there. Have a great weekend, everyone. Terrific. Thanks a lot. Bye-bye.