Welcome everybody to Recursion's Earnings Call. I'm Chris Gibson, Co-Founder and CEO, and I'm excited to take you through Recursions 2024, 2025 and the time ahead. So with that we'll jump into the slides and I want to just set the stage first of all to share a little bit about the moment in time that we're in right now. Recursion is leading this field of TechBio. We're the frontier of this exciting opportunity to decode biology to change the way that drugs are discovered and developed. And I think what we're seeing in 2024 at Recursion are leading indicators of what this inevitable future may look like. And what we're going to see moving into 2025 now is a cascade of proof points that are going to make it more and more obvious to everyone about what the future of the biopharma industry looks like. Now, to talk a little bit about how we've gotten to this place in 2024. I want to dive into some of the year-end review so to speak. And I want to kick that off by talking about our clinical data readouts. Last year, 2024 was the first time that we got to talk about efficacy in our first two clinical programs, REC-617 and REC-994. And I'll start with REC-617. This is a potential best-in-class CDK7 inhibitor for advanced solid tumors. And during this sort of dose escalation phase of the study before we would have expected to see efficacy and in fact in monotherapy which also is an exciting opportunity for us, we typically would have expected to see efficacy with combo therapy. We saw not only reasonable safety, but we saw early signals of efficacy. We had one patient who had a reduction in their tumor that was sustained for six months…

Yeah. Thanks Chris. So, already as two independent companies as Chris mentioned, we built two technology platforms that are already realizing great efficiencies in drug discovery and development from two complementary angles. At Recursion, we were founded around novel insights from complex biology mapping. So essentially building giant data sets, fit-for-purpose, for generating industry-leading foundation models of human health and disease. And then on the Exscientia side; technologies, models for compound optimization, so efficiently synthesizing molecules with the right multi-parameter properties for potential treatments and now we're bringing these two complementary sites together. And very specifically at the merger we created 90-day plans to very rapidly supercharge generating this real value. And we promise to tell you about the outcomes from that and here I am with some of the first results, and there will be more to come later. Specifically, apologies, -- specifically as you can see here on the right, we have brought together massive data sets from both companies into a unified platform from ADMET liabilities, Phenomics and Cellular Function, Protein-ligand binding put all of this into our Centaur model management platform, together with Recursion massive Compute that you heard about in order to generate a new generation of models that are more powerful more accurate and more generalizable. And then immediately deploy this using Centaur, against our pipeline in the Recursion OS and within the short three months, able to quantify some of the benefits to the pipeline. So for example, deploying new scientific agents that have resulted in a 60% reduction in the human time needed to get to hit to lead initiation or federation of models based on a new data set of over one million compounds for more accurate MOA deconvolution another set of models since Centaur, also new with 2.5-fold increase in efficiency of detecting new bioactive compounds, as whole groups of new potential useful drug starting points and a 40% reduction in likely, cytotoxic compounds. That's just one example there among 18 new admit applications. So really already now seeing massive progress of building a new joint platform where the new hole is much greater than the sum of the prior parts. And with that, I'm going to hand it off to Ben Taylor, our CFO, who's going to talk about how we are doing all this new exciting work while at the same time save money as a joint entity.

Thanks, Lina. Yeah, it's been a great year for us both coming together as companies as well as what we've seen looking forward. So for starters we had $83 million in revenue as a combined group that's a pro forma basis in 2024 and had an ending cash balance of over $600 million. Now, that gives us enough of a runway to be able to extend into 2027. So continuing on the business model that we have, we feel very comfortable that we've got the runway to be able to execute on a lot of those things that Chris and Lina were just talking about. An important element of that when we closed the deal we gave guidance that we expected up to $100 million in synergies, we actually believe we will achieve a majority of those synergies this year and be able to get to a run rate that is beyond that $100 million over time. Now that is coming both from the more traditional synergies that you would expect from two companies coming together, but also on a lot of the operational synergies. So all of those benefits that Lina was just talking about actually translate to economic benefits for us too as we push forward. In addition really exciting important note we have carved out the Vienna operations into a new company. That will continue on and that both gives the new company a great stand-alone mandate, as well as helps to organize some of our operations and really provide us as much focus as we can possibly get. And we're on track to also clean up a lot of the excess office and legacy sites that we had previously when we came together as a combined company. So we will give you a much more in-depth…

Thanks Ben. Yeah, I think we're so excited about 2025 as Ben just shared so many different potential catalysts upcoming across our pipeline our partnerships and our platform. But as we look out a little bit beyond that sort of to the intermediate or even longer term, we believe that there is just extraordinary value to be unlocked. And we think Recursion is better positioned than almost anyone to be able to do this work. And so I want to share a little bit of that vision for you. I want to talk a little bit about how we think about the intermediate term. And, of course, Recursion today is leading the field and bringing together the real world and building these world models. This is the idea that we can have a laboratory full of scientists who are generating data. That data can be leveraged in a computational environment to turn it into models. We can learn and hypothesize using those models and go back into the real world. And this has been what we've been building at Recursion for quite some time. This idea of the real world and the world model this loop of learning and then hypothesizing, but what we think is coming, what we believe is coming in the near to intermediate future is actually a transposition of the -- the transposition where the world models become so good that they actually start to look more like a virtual cell. They start to actually look like -- they start to look like a virtual cell and that virtual cell is well-positioned to help us make predictions about the -- make predictions about biology. And so if we are taking this virtual cell and making predictions about biology instead of taking real-world data to inform algorithms…

It looks like the first question is coming from Alec Stranahan from BofA. Recursion has spoken about it supercomputer and data scale, especially, on the phenotypic side in the past but new advances including DeepSeek or bringing the need for scale into question. I would question that. Do you think this is a risk for TechBio as well? Or is biology just so complex that scale will continue to be essential. Well look Alec, I think it's a great question. I mean biology is extraordinarily complex. And the interaction of biology and chemistry is extraordinarily complex. So I think scale is going to continue to matter. And while DeepSeek did demonstrate that there are ways that you can train and deploy models in a more efficient way it does not hurt to be able to bring scale to these -- bring scale to the latest generations of neural nets and architectures. I think we're going to be able to do that. And we're going to be able to bring data across all these different data layers together. And so, we don't think that there's a dramatic opportunity for somebody to essentially bootstrap biology and chemistry. We just think that it is fundamentally too complex. Next we'll go to Vikram, one of our analysts from Morgan Stanley. How is your partnership with NVIDIA progressing? And what are your key focus areas for your foundation model work? Great question, Vikram. So we've been working with the NVIDIA team for many years. We're working on a number of different projects and they've been helping us to deploy lots of our different models across these very complex supercomputers BioHive-2 being the one that we have in-house at Recursion. It is not trivial to be able to train on a supercomputer of that scale not…

Yes. This is a great question. And it causes a lot of confusion out there, because we are in many ways a tech company. However, our revenue and earnings don't show up like a traditional tech company. In the sense of we are often in our partnerships paid an upfront payment that then is recognized as revenue over a longer period of time. So you can't think of this like a subscription or a payment that's coming in quarterly. And so important fact we've brought in $450 million from our partnerships, a significant amount of that still has not been recognized as revenue. So as we continue to go on and have milestones or enter into new partnerships, those will continue to be cash inflows. They may or may not show up immediately as revenues. So it's really, really hard to track our quarter-to-quarter performance based on our revenue and we never suggest that people guide to it.

A wonderful world of GAAP accounting. Thank you, Ben. All right. Let's go to Dennis Ding from Jefferies, who asks talk about your expected cash burn for this year, and what we should expect at the May 2025 update?

Sure. We've got another complicated accounting question. So because of when we closed the transaction most of the financials in our 10-K actually reflect the Recursion -- the legacy Recursion stand-alone financials with a stub period for the legacy Exscientia piece. So what we tried to do was actually put in you'll see in the press release and the 10-K, the cash burn amount of $184 million. That was the starting year and end of year cash balance for Exscientia, the difference. As well as if you look at the cash flow from operations and the CapEx from Recursion, you're going to get to a number that's a little over $550 million for the combined entities. That is not a perfect number by any means to be clear, but to give people a general sense. I think what we're comfortable with is we are going to continue to grow, but we will be able to manage our cash burn and be underneath those numbers this year and we'll give you more detail in May. I don't want to front run ourselves. We want to run a proper budgeting process, but we are very, very focused on cash burn. We are very, very focused on runway. And we'll come back to you with more guidance on that later as well as what makes up it and why.

Awesome. Thanks, Ben. Next, it looks like we've got a couple of questions here on our CCM program. This is REC-994 and CCM. So Joe Philips asks, any updates on timing on REC-994? I was wondering if there's more clarification on whether this is going to advance to commercialization, our commercialization. And then Jeff at BioVantage, the primary endpoint of safety for REC-994 was met, but it was negative with regard to efficacy. Can you comment on the status of that program and plans moving forward beyond the recent presentation? What is the rationale for the longer-term treatment that will lead to statistically significant improvements? So great questions here. So I want to take Jeff's question first which is, we did see really, really exciting robust safety across this chronic treatment of a year. But I would challenge this idea that we did not see efficacy in the study. We did a signal-finding study. We're the first company to ever go to any regulatory agency with a CCM clinical trial to look at efficacy. And so we had to look at a wide variety of different measures a wide variety of secondary endpoints that could give us an idea of where to go in a subsequent trial. And so in a signal-finding study, you don't necessarily power all of those different end points, you go looking for maybe nearly significant or somewhat significant, but not p-value less than 0.05 findings that you can then parlay into a subsequent study where you narrow down the number of endpoints that you go after. What we saw was I think nearly significant data with a poorly powered study across this objective measure of MRI. If you look in the brainstem lesions for example, we see really robust reduction in these particular lesions. And…

Sure. Well, and it's really interesting. We've certainly done the work internally to look at different revenue streams to come in. What I would say is the economics that we get out of our pharma partnerships are excellent. For example, we're looking at over $300 million in milestone payments per program, high single low double-digit royalties. And effectively, we have all of our direct cost paid for upfront. And so that's a very economically attractive deal. For us to diversify into other areas, we actually have to exceed that sort of an ROI threshold. So we do continue to look at it. We do engage with whether it's partners on the tech side or on the pharma side different ideas. But I'd say there's a very high bar for us to expand that.

Thanks Ben. The second question from S.J. is how confident is the leadership in our ability to discover and successfully clear clinical trials and get viable drugs on the shelves potentially tapping into some of the delayed longer-term royalty-based revenue. And I would just say Look I think we're very confident here. Discovering and developing medicines is hard, but we've been building a learning system. And my strong belief is that the system we have built has a high probability of being able to generate better molecules and better medicines over time. So, whatever level we're at today on average I would expect each generation of new molecules to get better. Now, it's important to note that for any individual program there are hundreds of ways that it can fail. And some of those can be really, really surprising. And so we can't say with any confidence whether molecule A, B, or C is more or less likely to advance. There are certainly ones where we're investing more to go faster but that it's very hard on an individual asset level to be extraordinarily confident when you're in sort of the preclinical stage the Phase 1 stage the Phase 2 stage. But what I can say is both we with our own pipeline and our partners with a number of programs that have been moving forward in those partnerships a lot of potential. And what I think is really important about Recursion is that we're not a typical biopharma company that has a handful of assets that create almost a bimodal outcome for the company where if you're successful in that leading program in that Phase 2 trial, the company has bought for some really, really exciting number and that molecule advances in someone else's hands. We're building a platform…

Sure. The short answer is no. We're not giving any guidance on it right now. But I think what you've seen come out of our partnerships in the past is milestones around either drug programs advancing or delivery of phenomaps. I would say we are doing both of those things still in our partnerships. And so we look to do more of the same and more of it. The other thing that I would note is and this is alluded to and what Chris was saying another thing that we are always looking at is when is the right time to either advance a program on our own or to potentially look at partnerships around them as they advance through. And so I think that's something that we continue to look into for our pipeline and that could generate revenues and milestones as well.

Thanks Ben. Just a couple of things that I would add real quick right there is we've been doing the work of these partnerships for many years and essentially priming the pump on the milestones. And so while we're not giving any formal guidance for this year, I think there's a lot of confidence in where we're going. And as Ben was just alluding to, we're also starting to I think get a lot of interest in some of our individual assets that are in our clinical or preclinical pipeline. And so hopefully, we'll start to be able to demonstrate again different ways that Recursion is generating revenue generating credentialization of the platform and subsidization of the future pipeline that we're going to build. Next, so this one from Marcel. I'm actually going to turn over this first question to Lina. So, a while ago and by a while ago, Marcel, this is a few weeks ago at the JPMorgan Conference, time flies, we mentioned the goal of creating virtual cells to enable us to discover and develop medicines at scale including potentially to be able to simulate clinical trials. Given the known issue of hallucinations in LLM, are there concerns that virtual trials could also be prone to inaccuracies, particularly regarding cell drug interactions? And in the rare disease space where data might be limited, how would you validate the results generated from these virtual trials to ensure their reliability and accuracy?

Yes, sure. Great question. We rely of course, on LLMs in addition to lots of other different model architectures, transformers deep flow nets, MolE is an architecture unique to Recursion that you might have read about, if you are more deeply in the Machine Learning field. And these come both with great power, different and -- powers and be able to get accurate predictions. And also sometimes liabilities, and limitations, and hallucinations, and false positives and so on. So we build big branch marking data sets, to ensure that our models are performing to the highest capability that we are able to do in our hands, with those benchmarking models. In addition, I think a really great important component here, is that we have large laboratory setups, in vivo setups, et cetera, where we can validate insights that come out of these models. So at the play that we're doing harking back to Chris' point, it's not that we're never validating anything in real assays and really experiment, is that we're able to focus down our experimental assays to the most promising compounds, the most promising insights, so we can be incredibly comprehensive exactly, where it matters most and not spend time and money on compounds that never were going to go anywhere, in insights that were going to be the right ones. And so, we're still doing this incredible important validation along the way, before a drug gets put into patients. And then specifically around rare diseases, this is definitely of course, a challenge not just for Recursion, but for the industry as a whole. And one component to this is building models that are not just focused at predicting that specific disease, but models that can understand broad biology so that we can validate and have model performance that are not just about one specific gene for example, but about that gene in context with everything else going on in the cell and in that patient. And that is one way, to bring confidence in local areas, that are "rare" but in context of all the complexity of human health indices, including through data at the atomistic and protein level through our massive data sets and Phenomics, linking all the way into the Tempus Helix and other patient data that Chris mentioned.

So, adding on to that, a little bit -- thank you, Lina. Gill from Needham asks, as it relates to creating a virtual cell environment, what would convince you that or us that we've reached something productive? And so this is a great question. And the reality is, I don't think this is going to be us flipping a switch and all of a sudden going from not productive to productive, it's going to be a gradient that we achieve over the next handful of years. And Gill, we're really focused on benchmarking our team at Valence is helping to lead Polaris, which is a benchmarking initiative across the industry. We've done a lot of our models against the therapeutic data commons, predictive ad benchmarks. And what I think we'll be looking for is, moving from models that give us insights into predictive ADMET or mechanism of action deconvolution or clinical trial simulation, towards broader models that have these emergent features that help us ask and answer questions, across many different layers of biology. If I were to just sort of imagine a place in the future that would give me the sense that we were really on the cusp and starting to feel like we have a virtual cell. It would be when we start to report to all of you, a reduction in the scale of data that we're generating. And we're moving from data into really just validating predictions. And that will be the point at which that transposition has happened. So, we're still doing up to 2.2 million Phenomics experiments, a week. We've done 1.6 million Transcriptomics experiments over the last 1.5 years, we're building a number of other kind of data modalities. When you start to see those numbers go down purposefully, because we're just…

So, great with that. I think we're probably going to decide to move on here. Thank you everybody for joining us for Earnings Call. We're really excited to be building towards the future here, decoding biology to radically improve lives and hope you all have an amazing day. Thank you so much.