This conference call transcript was computer generated and almost certianly contains errors. This transcript is provided for information purposes only.EarningsCall, LLC makes no representation about the accuracy of the aforementioned transcript, and you are cautioned not to place undue reliance on the information provided by the transcript.
spk00: Hi, everybody. My name is Chris Gibson, co-founder and CEO of Recursion. Delighted to be joining you on our learnings today. And I'm joined by our chief R&D and commercial officer, Najat Khan, and the interim CEO, and I hope very soon to be CSO of Recursion, interim CEO of Accenture, and soon to be CSO of Recursion, Dave Hallen. We are coming to you live from Oxford, UK. We are in the Accenture facility where behind me, they are using a closed loop automated synthesis platform for chemistry to advance new medicines towards patients. And we're just delighted to be sharing the news today that our two businesses have announced a combination. What I would like to do today is walk through first that combination together with Dave and Aja, and I'm going to start by talking about some of the complementary factors that we see. First, a pipeline of nearly 10 or approximately 10 readouts over the next 18 months in the clinic. I think this is a really important milestone for a company like Recursion, a company that is trying to prove this next generation of medicines, a new way to discover medicines. And being able to generate this quantity and quality of potential readouts in the coming quarters, I think is going to be really, really fantastic. Next, partnerships. Recursion has some incredible partnerships with large companies like Roche Genentech and Bayer. Our partners at Accenture have fantastic partnerships with companies like Sanofi and Merck, KGA. And we are just delighted not only for the opportunity to combine our businesses and work against all of these partnerships, but actually to deploy the tools and technologies, the teams that we will be assembling against the partnerships of our counterparties. And I think this deal in many ways will make Recursion perhaps a partner of choice for others in the industry. Finally, our platform, our platforms at Recursion really focused on exploration of biology, hit discovery, target discovery. We've been building that for over a decade and our colleagues at Accenture have really been building for about the same amount of time this incredible precision chemistry platform. The ability to go from a hit to a development candidate with active learning and automated synthesis is really, really exciting to us. And putting these two platforms together, we think, is going to put us on the cutting edge. And finally, when we combine these businesses, we believe that we will have not only the team, the tools and the technology to go to the distance, but we'll also have the resources to do that. At the end of Q2, the companies combined had roughly $850 million, which we believe with the right kind of operational synergies puts us on a runway into 2027. And finally, the most important piece is the people. Both of these businesses have been building and pioneering the technology biology interface for the last decade or more. And we think we have some of the best teams in the industry. And by combining our businesses, we're going to be able to take these two incredible teams, put them together. And this is definitely, as Dave likes to say, definitely a situation we believe where one plus one equals three. So I want to dive into the pipeline just a little bit, give you a little bit more depth there. What I think is most exciting and important about the pipeline beyond the 10 potential readouts in the next 18 months is the complementarity. These are pipelines that have really no therapeutic overlap, where the two companies are both focused to some degree on oncology, but recursion really focused in rare disease, infectious disease, and our colleagues at Accenture really focused in immunology beyond oncology. That's a fantastic opportunity in the combined business for us to expand the reach. Through our partnerships, we also were able to go after a number of additional areas as well. Of course, recursion, given our focus on biology, has really gone after first-in-disease, first-in-class type targets. And our colleagues at Accenture, I think, have done a tremendous job of going after some of the hardest chemistry on targets that the world is really, really excited about, really best-in-class tools and technology. And by putting these together, we believe that this organization is going to be in a position to bring first and best-in-class medicines to patients and drive down the cost of discovery. So I want to turn to Najat, who just joined us, boy, just a few weeks ago. It's been quite a whirlwind. Let's talk a little bit about the pipeline of the proposed entity with these 10 programs that may read out in the next 18 months. Najat?
spk01: Yep. Thanks, Chris. Look, at the end of the day, we talk a lot about biology, chemistry, automation, tech, et cetera, but it all converges to the medicines we make for patients that are waiting. And that's really what this slide is about. So I'll talk about two portions. First is about our internal pipeline, and then also about the external pipeline that we're building and learning with our partners, which you've heard Chris mention. So in the first piece, you see that there's about 10, as Chris mentioned, clinical and near clinical readouts coming out of the next 18 months. There is breadth, and then there's depth. The depth really coming from both of us doubling down in precision oncology, going after both heme and solid tumors, both of which have significant unmet need. And then in terms of other areas where we have depth, rare diseases, different types of rare diseases, significant unmet need, and quite a few of them, quite frankly, with no standard of care that exists today. And then also in infectious diseases, going after areas that have huge unmet need for the aging population. So that's one piece. The second piece I do want to mention is the fact that we have multiple readouts coming out over the next 18 months. The 10 readouts across safety, tolerability, Preliminary views and efficacy. And then the third piece is the external pipeline. You know, as Chris mentioned, this is expanding us from oncology, rare disease, infectious diseases to immunology. Two sides of the coin. Many people would say oncology and immunology in terms of understanding the biology and being able to address it. So we'll speak more about that with some of our large partnerships that we have that are transformational. But I want to mention one other point. The milestones that we'll talk about also are diverse. And what do I mean by that? Some of them are focused on therapeutics, milestones, and others are focused on products, such as the biology maps that we recently optioned to Genentech.
spk00: Thanks, Nizha. I think it's important to also mention that beyond this internal pipeline, as Nizha mentioned, there's this external partner-based pipeline. And as we're sharing today publicly, there are 10 programs that have been optioned by one of our partners. So we really are building a pipeline of pipelines, both internal and external, within multiple therapeutic areas. For those tuning into our learning stall, they may be less familiar with the pipeline of Accenture. Certainly, we've become incredibly familiar with it. over diligence last few weeks and months, and we're really excited. I want you to tell folks a little bit about what you've been working on, and I know we'll have a chance to dive into your lead program in just a minute.
spk03: Thank you, Chris. Thank you. What I like about this proposed combination is the complementarity, as Chris mentioned, about the pipeline. Our lead assets have focused in large indication spaces, high unmet needs, and broadly in the oncology space, solving design problems that other people have failed to solve. And over the course of the coming next six to 12 months, you'll see significant updates on each of those. I'll focus on three at the moment and then talk about CDK7 in a second. So our PKC theta inhibitor that we licensed to BMS is navigating a phase one healthy volunteer study at the moment that was updated very recently. LSD1 and MALT1 are pretty much neck and neck. We are looking to file INDs of those two different compounds in the coming months, looking to actually initiate the dosing of patients as we straddle 24 to 25. But if I may, I'd just like to concentrate on CDK7. Some people may be aware that we recently acquired 100% of commercial rights, this asset, and that's because we believe in it so much. This is a truly potential kind of best-in-class compound that operates in a related but broader space to the well-known CDK4-6 inhibitors. This compound is currently recruiting deep into a monotherapy dose escalation study, and I'm hoping to provide an update on the progress of that towards the end of this year. The first portal call that we're going to take this into into a clinical setting is looking at a combination study. So this is in HER2 negative. hormone-resistant positive breast cancer patients that have progressed after CDK4-6 inhibits. Really, really impressed with the performance of this compound. Looking to also potentially explore other tumor types, and we'll update the capital markets later in the year. Thanks, Dave.
spk00: Very excited about the potential for bringing these two pipelines together. Beyond the pipelines, of course, as we mentioned before, the partnerships. At Recursion, we have a number of partnerships you can see here. Our colleagues at Accenture, a number of extraordinary partnerships. I think some of the most exciting partnerships in the space of technology-enabled drug discovery. And as we mentioned earlier, there are these 10 programs that have been optioned. We believe that between the two companies, if this deal is able to be closed, we have the potential to drive roughly $200 million worth of milestones over the next two years or so. And there's the potential between these partnerships, assuming no additional partnerships for more than $20 billion worth of milestones before royalties. And I think that is really, really unique. We have had interest from large pharma, not only in finding really exciting medicines, but finding medicines at scale. And I think that speaks to the platforms that both companies have built. Nizhan, I wonder if you want to talk a little bit about how you see the complementarity of the partnerships.
spk01: Absolutely. And I want to pick up on something you just said around scale. And as you're talking, Chris, we're talking about 10 programs in the clinic, in our internal pipeline, over a dozen in discovery, and 10 in our external pipeline. I mean, that is a pretty significant portfolio that we've built with our partners and also internally. How do we make it happen? Well, partnerships are a big part of it. Roche, Genentech, Bayer, those are extremely important partnerships where we are working on new hard targets that can be first in class with the potential to be first in class medicines. But there's two other partnerships I want to note, which I think has a lot of complementarity with Excientia as well. One is with NVIDIA. Need I say more? The need for compute to do everything that we do, whether it's in biology, chemistry, it's complex problems, lots of data. I think there is so much value in terms of what can be done with the work that Exantia is doing in generative AI, 2D, 3D design, quantum mechanics and so forth. So that's going to play really into our favor to accelerate what we both are doing. And then the other type of partnerships are data. You know, as I previously mentioned, Tempus, Helix, why is this important? As we have more of these programs going into the clinic, 10 in the internal pipeline. 12 in discovery, 10 in external. It becomes really important to design programs well. So having clinical multi-omic data with real-world data is extraordinarily important for us to stratify our patients, drive true precision medicine in our trials, and accelerate our programs in trial recruitment so that we can fulfill our promise of developing novel medicines better, faster, and more cost-efficient.
spk00: Thanks, Najat. Dave, can you comment a little bit on how we can use our complementary sort of superpowers at both of these companies in service of the complementary partnerships as well?
spk02: Sure.
spk03: I think one of the many things that really excites me about the journey ahead post-close is how on the existing partnerships that Chris and Najat have just mentioned. So from looking through the lens of Accent, you say our Sanofi collaboration is how can we kind of leverage the combined capabilities to really accelerate and add further kind of depth to existing collaborations? But looking to the future is just the end-to-end capabilities that the two organizations bring together. And I'm looking forward to how we can leverage additional relationships through this combination. Thanks, Dave.
spk00: So finally, I want to talk a little bit about the platform. We're here in the Milton Park facility where the automated synthesis platform of Exientia is running behind us. And I've just been so impressed over the last few months and even the last few years as we've gotten to know this team by what they've been building. True, true, extraordinary depth. in leveraging technology for precision chemistry. I don't think there are many organizations on earth that are better at taking a program from hit to dev candidate, especially in the context of challenging chemistry, where there are perhaps multiple different parameters that have to be optimized against at the same time. And with this new automated synthesis platform that you see behind me, they're able to now integrate automation, robotics into that entire process. And we think drive down the time, the cost, and increase the probability of success. And what's most important, I think, is this philosophy of design, make, test, and learn that they built here at Accenture, where not only will we be generating data that can improve The potential medicine in each program, but we'll be generating data that can be used to build algorithms that can understand difficult challenges in chemistry and admin and talks and even data that can be used together with the data that recursion is generating across biology to really start to build these foundation models that have. generalizable understanding of biology, of chemistry, of the interaction of those two. And when we put those platforms together, we really have built what we believe is the end to end solution. There's more to build, but we don't know of any other company in the space that has focused more on trying to build the full stack solution with a philosophy of technology enablement at every step, a philosophy of data generation, evaluation, learning, and creating these virtuous cycles of iteration at every step. And we believe that this is the recipe for success in the biopharma field. We believe this is the experiment that we exist to run at a recursion. And I think in many ways at Accenture too. And that's why there's so much complementarity between the two organizations. I do want to just talk a little bit about the transaction details for those who are joining. As you saw today, Accenture shareholders will receive 0.7729 shares of Recursion Class A common stock for each share of Accenture, assuming that the deal closes. If there's no new share issuance, that means post-close. Current recursion stockholders would own approximately 76% of the organization, and current Accenture shareholders would own approximately 24% of the organization. As we shared before, the combined cash of the two companies at the end of Q2 was roughly $850 million. And given some of the operational synergies, the incredible discipline, frankly, that your team executes your work with, that we know we can benefit from, the deployment of our tools to make each of our other processes more efficient, we believe we can extend Runway into 2027 in the combined entity, saving over $100 million annually. It's important to also note that Recursion will be the go-forward entity post-close. I will remain the CEO of the post-close entity. And Dave is going to join us as chief scientific officer of the post-close entity. We also have two members of the Accenture board who will be joining the recursion board. So we really are combining these businesses in a way that we think is going to really enable these two upstarts to take on a massive industry to try and bring medicines to patients more quickly and to drive down the price of medicines for patients in the coming decades. And that is, we believe, something that's great for patients, for consumers, and certainly something that we're all excited about as a hard challenge with incredible impact. We expect that this transaction will close by early 2025. And of course, this is subject to the approval of the shareholders of both of the organizations and closing conditions. And with that, I want to transition just a little bit to a few of the other updates from the last quarter. Certainly the big news today was this business combination. You may see some tired eyes here. It's been quite a couple of weeks to get this together, but we're so excited to be doing it. So excited to have this team joining us. I do want to just mention some of the work that we've done. A couple of days ago, we were able to share that our partners at Roshan Genentech have optioned the first Neuromap. This is an extraordinary, extraordinary achievement. And just huge thank you to the team back at Recursion who's been toiling away for the last two and a half years. We've become what we believe is the world's largest producer of neural iPSC cells simply to service this particular Neuromap. We have knocked out every gene in the genome. We've explored hundreds or thousands of other perturbations in the context of these neural iPSC cells generating omics data on top of it. And we believe that from these data and our computational approaches, we will be able to potentially identify some exclusions extraordinarily exciting new targets in neuroscience, an area that has just tremendous unmet need. And I think this really validates the approach that Recursion has been building. Next up, we'll be augmenting this with chemical perturbations. That has the opportunity to generate milestones that are even more significant if we're able to succeed in doing that. And our colleagues at Roche Genentech accept that map. This is the first of many potential maps to come as part of this collaboration. And I'm just so proud of the team and having had a sneak peek at the map, both ourselves and our friends at Russian EdTech. I know that everybody is tremendously excited by some of the new biology that we're already seeing. And now with this proposed business combination, I know that we will be able to not only go after that novel biology, but we'll be able to bring best in class and first in class chemistry to bear. So really, really exciting day. I want to mention just a little bit about our partnership with Bayer. As you know, we updated this partnership just a few months ago. We started working oncology in the first quarter of this year. In Q2, we announced that the first joint project was rapidly heading towards lead series domination. And we are now sharing that we are on track to complete 25 unique multimodal data packages by the end of Q3. And again, with this partner, I think we're going to be able to deploy the tools of this collaboration, this business combination very, very well. Finally, our colleagues at Bayer are the first users, beta users of Lowe, which is our large language model orchestration work engine. Another potential opportunity for us to bring together some of our software tools. Tremendously excited about that. And finally, next month in September, we will be reading out the top line data for REC 994 for CCM or cerebral cavernous malformation. We've talked about this in a lot of depth in the past, but we are very excited about this potential medicine. Really a massive area of unmet need. Jean, I don't know if there's anything you want to add about this program, but...
spk01: No, I mean, look, rare diseases in terms of having any standard of care, these patients, huge unmet need. The standard of care is not what it needs to be. And on top of that, you know, the diagnosis, we've seen this across industry, a lot of rare diseases, once there is a therapy viable, the diagnostic rate actually goes up and you start to see a whole shift in that area in terms of other therapeutics that are coming to address the unmet needs. So yes, very, very excited for next month to see the results, primary safety and tolerability, and then also looking at some of the early efficacy data as well.
spk00: Thanks, Anjana. And finally, on our broader pipeline, we've already hinted at this before with the 10 potential programs that are going to be reading out. But, you know, we've got detailed information here and on our website around the specific timing of the seven clinical trial readouts that we've already given guidance around here at Recursion. So this is really an exciting day for us. We continue to, I believe, really boldly chase this vision of trying to leverage technology to discover and develop medicines. We've got incredible partnerships, an incredible platform. We've got a fantastic pipeline. And now I think a fantastic business combination in the works. And we are very, very excited for the coming quarters. I think with that, we're going to go ahead and transition over to questions, which I know are coming in. All right, we've got Scott who's asking, even though there is no competitive overlap, is there anything to be learned from each other's internal pipelines that can allow you to accelerate the advancements of your programs regarding the Accenture business combination? You know, Dave, we've talked a lot about this, deploying our tools for each other's programs. Do you want to give your insights here?
spk02: Sure, I guess...
spk03: I think post-close, I think one of the things that motivates me is that the combination of the data. So we're a learning organization saying that recursion is. And so every program that we execute on, every piece of data that we bring in-house allows us, our operating system to actually kind of to learn and to get better. Imagine the kind of the excitement about actually kind of bringing the data, the huge data sets and the competence that Recursion have been generating, particularly the last few years, with the kind of not only the pipeline that's visible today, but the kind of the pipeline that sits within our partnerships. That's a huge amount of information and a huge amount of data that we can kind of leverage to both benefit current partners, future partners and also our kind of pipeline as it goes forward. Yeah.
spk01: And maybe if I can just add very specifically, you know, we look at CDK7, there are CDK therapeutics on the market, right? And let's face it, the response is not the same for all patients. There's resistance mechanisms. So many things that we need to understand from a patient stratification, patient selection perspective. That's where Recursion with the Tempest partnership that we've done already in the last six months, identify novel targets in non-small cell lung cancer and other areas using causal AI techniques. and many other algorithms that we've developed. Now, think of the merger of the two to say, how do we design these really important programs for CDK7 and other programs in a much more effective way? Precision medicine at the core is being able to predict the right patient, the right therapy for the right patient at the right time. And I think there's a lot we can do to shape the industry, leveraging real programs for patients in the near and long term.
spk00: Thanks, Nizhan. Thank you, Scott, for the question. Let's go to Aurore, who asks, following Recursion's acquisition of Ciclica and Valence in 2023, what is the vision to integrate Accenture GenAI capabilities in the new company? Nizhan, I'm going to turn to you because I know you've spent a lot of time not only in diligence the last few months or a few weeks, but also working with, it's been a swirl, working with our internal teams on the GenAI vision that we have. Do you want to talk a bit about this?
spk01: Yeah, absolutely. So in terms of generative AI, especially, let's talk about it in the molecular design space. You know, there's hit to lead, there's lead optimization. Most of the times we end up getting really challenging the industry for small molecules around lead optimization, the potency, you know, this trade-off for some other parameter. So what Excientia has from a using active learning end-to-end to improve the multi-parameter optimization, which is the problem that we're trying to solve we want to be able to integrate that into what we do at recursion today not only that we want to go earlier because if you can actually solve the problem and hit to lead with some of the solutions that um that Dave and his team have developed, that will improve our probabilities of success and hit rates even better. The last point I want to mention, because I get this question all the time, whenever you integrate two platforms, isn't there a lot of integration challenges? This is the beauty of it, where we spend a lot of time and diligence. It's been built in a modular way. which means we can be agnostic to the best models, whether it's inside our two homes or outside, to make sure we have the best green molecules to our portfolio and our pipeline. So that's some of the ways we're thinking about integrating it. And last thing, you know, some of the phenomics and multiomics data that we have will also benefit Excientia. And the team has already started looking at ways to integrate that. So we're very, very excited. There's so much work to do and can't wait to get started.
spk00: Thanks, John. Let's go to Alec, who asks, how do you plan to leverage Accenture's automated laboratories? Well, it's a great time to answer that question since we're sitting in them right now. So I think this is really, really important. Dave and I had a very long talk about this during during the time that we spend in diligence. And I think really this team has done an incredible job of building a state-of-the-art automated synthesis platform. And really the only one that I'm aware of that has integrated this vision of using active learning, machine learning to be able to drive very flexible decision-making throughout the process of synthesis and then through the other side of the physical U-shaped platform to be able to drive the molecules that come out of the automated synthesis platform into a variety of biochemical assays. This is technology that Recursion has not built. We have built incredible technology that can take a potential small molecule and explore its biological functions across these large-scale multiomics datasets. Combining these datasets, we think, just like combining the Tempest dataset with patients, just gives us this extraordinary opportunity to build models that have the potential to learn not across one layer of biology or chemistry, but across many. What I think is important probably to note is that this facility is now up and running. We believe it should stay up and running. We should build it out from here. And we're going to continue building the biology organization in Salt Lake City. We do believe that the learnings from this platform could be used in the future to help us build a next generation microsynthesis facility that we can tack on to the platform we built in Salt Lake City, where smaller quantities of a larger number of more flexible molecules is going to be better for kind of exploring chemical space with our phenomics and transcriptomics platforms. But I think what they've built here and the team, frankly, that they've assembled around this platform here, truly, truly extraordinary, and I think is going to give us the ability to drive specific programs in a much more differentiated way. I mean, I was struck through diligence by how few compounds this team synthesizes while achieving best in class status very often from sort of hit to death candidate. It's dramatically lower than the industry average. And this is a great example of how we're going to find ways to kind of bring down the cost of the organization. We are great at finding hits for novel biology. And when we get into chemistry, we have an incredible team. But because we built less tooling, we operate at an efficiency that's closer to the industry average. At the same time, we know that our colleagues at Accenture are fantastic and incredibly efficient at driving chemistry from hit to dev candidate through lead optimization. But they spend a lot on kind of the outsourcing of various CROs and others around the early stage biology and hit discovery. By combining our platforms, we believe we're going to be able to bring the most efficient technology enabled approach across the entire process of discovering and translating these medicines. And that, frankly, is going to make us not only a powerful organization, but I think one that is going to be extraordinarily efficient. If I could just maybe start just a little bit, can I think?
spk03: This closed loop design, make, test, learn platform that's literally sat behind us is designing molecules in the cloud, but not only making the molecules, but in a target centric way, actually generating data against them. So here's an interesting idea that Chris and I talked about in Diligence is that obviously post-close, If you look at kind of what Cyclic have done and basically with their matchmaker tool in terms of predicting kind of ligand protein interactions on scale is actually to help kind of better underpin those models by actually kind of actually making some of those and generating some of those and actually kind of actually generating data, experimental data to actually underpin those predictions and further improve those generalizable models. I think there's hundreds of ways that basically this particular design-make-test loop kind of lab that sits behind us would benefit the future combination. I agree.
spk00: All right, next up, we're going to go to Mani, who asks, what do you see as the bar for efficacy in the upcoming Sycamore readout in CCM? For that, I'm going to turn back to you, Najat.
spk01: Sure. Now, in terms of our Sycamore study, I mean, primary endpoint is safety and efficiency. tolerability. So that's something we're going to be watching very, very closely. In terms of the efficacy, we have two different types of categories of endpoints that we're looking into. One is very objective MRI-based endpoints. So for instance, looking at lesion volume and so forth. And then the other is PROs. These PROs are extremely important to patients. So for instance, CCM is health index, which is something that was recently developed, and many other effects. So we're going to be looking at both of those. And then the third piece I will mention, because again, this is a signal-finding, signal-seeking study as we're looking at various different doses, is looking at some of the biomarkers. It's going to be important to understand what's happening to the vasculature of what's happening to the inflammation and so forth. So those are the three areas that we're going to be focusing.
spk00: Thanks, Nisha. No, I think that's great. I will say just from a safety and tolerability perspective, the vast majority of patients in this trial have already rolled into the long-term extension, which gives us a lot of confidence on that side of things. Next, I want to go to a question, I believe, from Cole. A question from Cole who asked, who asked around the biggest bottleneck in drug development is clinical trial process. And as much as I want to answer this question, Najat has spent the last six years doing exactly this at her former employer. Najat, please take us through your vision for this part of the platform.
spk01: So Cole, I'm so glad you asked this question. I love this question. Backdrop for everybody watching, like 70 to 80% of time cost is actually spent in development. And where does that get spent? Two areas. One is the design of the trial. You have to design it right. This is where precision medicine comes in. And then the other is the trial execution, which is what you're alluding to, I think, in terms of the trial process, site selection, getting the trials executed. So one of the things that we're working on in addition to all of this is building out the AI capabilities and tech capabilities on the clinical development side. So number one for clinical trial design is really using multimodal data, real-world data, such as Tempus Helix, but much more, in order to be much more effective in terms of how we design our programs, knowing which patients to treat, simulating inclusion-exclusion criteria, so that we don't do what a lot of the industry suffers from, which is many protocol amendments, which leads to time, cost, and yet patients are waiting. The second part is clinical trial operations. I'll give you an early example for our 488.1 program, Maxin 1 and APC. We actually used real-world data, machine learning, and just-in-time sites. What does that all mean? Basically, it means instead of using the traditional processes today, which is you go to a site and say, how many patients do you think you have that fit this criteria? You actually use all of the claims and real-world data to be able to understand where the eligible patient population is. It's anonymized, but you engage with the PIs early, a much more proactive approach. And we were able to recruit that cohort from what would take four to six months to four to six weeks. That is just one small example. But watch for the next few months of using much more of these innovative approaches where we can pull in our recruitment timeline so we can get medicines to patients faster.
spk00: Great. And we're going to finish up here with a final question from Marcel, who asks, could you share more on potential or ongoing efforts to use a platform for preventative health care? Specifically, are there plans to develop drugs or form strategic partnerships aimed at reducing the risk of diseases like cancer or neurodegenerative conditions such as dementia and Alzheimer's? And I think, Marcel, this is a fantastic question and really is part of the vision of what we're building at Recursion. We already have programs that are targeting genetic diseases that are essentially genetic diseases that are predispositions to cancer. That's already in our pipeline. We very deeply believe that there is a huge opportunity to go after areas in neuroscience like neurodegeneration. And while we cannot speak to specific diseases that we could be tackling alongside our fantastic partners at Roche Genentech, we certainly do agree that that is a really, really important part of the future. And I think what's so compelling about what we're building at Recursion and what we believe we'll be able to build together is that these maps of biology are not just giving us insights into one pathway or a couple of proteins that are interacting. We are building maps that are showing us the causal model of how biology itself works. is operating inside many different kinds of cells and we can start to understand this extraordinarily complex interplay of different pathways the way that different pathways are regulating each other and my belief my fundamental belief the founding belief of recursion was that this biology is fundamentally too complex for any human to understand and that we would have to deploy technology enable enablement across our entire process to really start to understand the way biology is interacting in truth, not the way we can put it on a whiteboard or put it into a nature paper. And I think the same philosophy holds with our colleagues here at Accenture. Chemists are incredible. They can do incredible things, but it is very difficult for humans to hold in their head a 40 parameter multi-optimization problem. It's a very difficult thing to do. Technologies like machine learning give you the capability to actually start to simultaneously optimize against dozens or maybe hundreds of parameters. And that's just something humans are not able to do. And I think this similar philosophy of biology on chemistry coming together gives this company true, true potential to deliver on the kinds of preventative medicine that you allude to. So with that, I want to thank everybody for joining our learnings call. So thankful for your team for hosting us here. So excited for the combination that we're putting together. And we like I always say, you know, we're 10, 11 years into this and it still feels like the beginning every day. Thanks, everybody.
spk01: Thank you so much.
Disclaimer