Transcript
Hi everyone. I’m Chris Gibson, Co-Founder and CEO of Recursion, and I'm thrilled to welcome you to our inaugural Learnings Call at Recursion. You may wonder what a learnings call is and why we are implementing this practice now. Traditional earnings calls have value, but over the years, they've often become overly scripted, rather dull, and difficult for stakeholders to engage with. Our learnings call represents our take on a traditional earnings call, aiming for a more genuine approach. I won’t be following a script today; instead, I’ll refer to the slides in front of me and adapt as we go along. We welcome any suggestions for improving these calls in the future. We decided to start our first learnings call at the beginning of 2024 because we foresee a series of significant milestones and opportunities ahead for Recursion. With this in mind, we want a strong method for updating all our stakeholders quarterly about our remarkable endeavors here at Recursion. To set the stage for today’s discussion about our past, present, and future, I’d like to take you back about a decade to the origins of TechBio. The early 2010s was a fascinating period when technology companies began entering various sectors to apply a straightforward strategy that transformed everything from how we navigate cities to our preferences for digital media and product consumption. These companies harnessed technology to gather extensive data, creating a digital representation of reality. The collected data was rich and high-dimensional, allowing for the aggregation and digitization that led to predictions drawn from large data sets, which were subsequently tested in real-world scenarios. However, achieving similar progress in biology has been exceedingly difficult due to numerous barriers that complicate data collection and generation. There are three main obstacles here. First, the biological world is largely analog; while there has been some change since the 2010s, many contract research organizations still rely on outdated methods, like sending scanned PDFs or handwritten notes. The biopharma domain generates vast amounts of data—hundreds of petabytes—but much of it has not been structured for machine learning, often existing on legacy servers lacking adequate metadata to facilitate the connection of different data types. Additionally, public datasets, while valuable, face reproducibility issues, as they too often lack necessary metadata. In the early 2010s, we and other leading TechBio firms recognized this challenge as an opportunity. We observed exponential advances across five key areas: a significant decrease in storage costs, increasing computing power, the development of more accessible automation tools for robotics, a renaissance in biological tools like CRISPR, and monumental strides in AI. Fast forward to today, Recursion is now at the forefront of TechBio, applying a similar formula that those technology firms used in the early 2000s and 2010s to the biopharma industry. Unlike industry standards, we are creating new ways to generate and aggregate this complex biological data. At Recursion, we’ve established a substantial automated platform enabling us to analyze biology across various cell types and patient data. We can extract high-dimensional data, aggregate it, and train algorithms on our supercomputers to make predictions. Importantly, we believe we are uniquely positioned to take the predictions from our algorithms and test them in a laboratory setting, creating a continuous cycle of learning and improvement, which we refer to as the Recursion OS. Over the past decade, our combination of data and technology has set us on the path to lead in the TechBio sector in the coming decade. Our focus spans across drug discovery and development, recognizing that it involves numerous steps. Recursion is crafting integrated cycles in both wet labs and dry labs, optimizing how we connect patient data, refine chemical compounds, and identify patient cohorts for clinical programs. We strive to be the most comprehensive TechBio solution in 2024 through our internal pipeline, partnerships, and platform. We are making significant strides with our first-generation programs, with five Phase 2 trials that are either enrolling or soon to enroll patients, targeting specific niche biological areas efficiently. Moreover, we are excited about our second-generation programs that leverage the tools we’ve developed and added to our platform. We’re actively collaborating with partners in both biology and technology. Partnerships include Roche-Genentech in neuroscience and oncology, and Bayer in precision oncology. Distinctively, we also engage in partnerships for data with Tempus, computational resources with NVIDIA, and chemistry with Enamine. This diverse cross-credentialization of technology and biology partnerships positions us uniquely in the market. Our platform is built on over 50 petabytes of proprietary biological and chemical data, covering multiple cell types and patient data. Currently, we operate the fastest supercomputer in biopharma, enabling us to bring algorithm predictions into the lab efficiently. We have automated various levels of omics data generation at Recursion, which allows us to conduct millions of experiments weekly. Reflecting on 2023, I believe it was an excellent year for us despite a challenging capital market landscape. We made notable advancements in our pipeline, partnerships, and platform. Highlights include the simultaneous acquisition of Cyclica and Valence in May, which has integrated into our operations to enhance our program efforts. Moreover, we launched our first clinical trial, SYCAMORE, focusing on a significant unmet medical need in cerebral cavernous malformation, fully enrolling patients and receiving positive indications on tolerability. In addition, our collaboration with NVIDIA led to a $50 million equity investment and crucial advancements in computation. We have generated a synthetic data layer, predicting protein-ligand interactions for over 36 billion compounds, which has allowed us to prioritize targets based on action mechanisms. The successful Phase 1 results for REC-3964 and significant enhancements to our foundation models have been pivotal developments. We also expanded our supercomputer capabilities with additional NVIDIA H100s, positioning us as a full-service biopharma technology company with the fastest computational power. As we look forward to 2024, we anticipate several major milestones, including topline results for our clinical trials and advancements in our pipeline. Our ongoing efforts to institute regular updates aim to propel Recursion forward. We’re excited about the potential for quarterly readouts and the opportunities presented by our new programs and partnerships. Overall, we're set to lead and innovate in the TechBio space in the upcoming year. And to do that, I have to go back a ways again, to the early days, back to the 2010s, when companies like Recursion were founded. And all of these companies really made their start with a point solution and we’re no different. We were scaling, industrializing, and pioneering a new kind of omics based on images of human cells to try and understand and explore biology. And since that time, we’ve actually seen that our work in this space has just continued to grow in complexity. Today, we can leverage our automated platform on Phenomics to generate more than 2.2 million experiments worth of data every week. We leverage extraordinary foundation models like Phenom-1 that I talked about earlier to make predictions about the relationships across more than 5 trillion biological and chemical contexts. This is an extraordinary, extraordinary feat, and it’s based on broad biology, over 50 human cell types that we’ve explored, roughly 2 million chemical compounds, whole genome CRISPR knockouts. This is really, really exciting work that we continue to push the limits of. But this is but one step in the Recursion OS today. While we started with Phenomics, it is now one of many steps spanning patient connectivity all the way to the clinic. And while I wish we had time to go through each one of these, I’m just going to focus on a few of these areas that I think are important to illustrate some of our focus on building these virtuous cycles. And the first of those is DMPK. Our DMPK platform is now up and running at Recursion. This is a highly automated platform that’s allowing us to execute three critical assays across both human and rat contexts. We can do nearly a thousand compounds a week on this automated platform and this is great because we can profile the molecules that are moving through our internal pipeline or our partnership pipeline. But what’s more, we’re using the majority of this platform’s bandwidth to actually profile many diverse compounds to build the data substrate on which we can train additional state-of-the-art predictive ADME and Tox models. And it’s this virtuous cycle of learning and iteration, of data generation and algorithm improvement that we think will differentiate us not only in target discovery with Phenomics, hit discovery with Phenomics, but even in how we advance our molecules towards the clinic. And it doesn’t just stop in human or rodent cells. We’re building these same kind of tools in model organisms. In our vivarium, we have over a thousand cages with cameras and other sensors that allow us to extract much richer, high-dimensional data from each one of these animals. And this means we can use fewer animals as we drive our programs forward and it means we can make decisions in real time. We can deprioritize and prioritize molecules based on digital tolerability studies in real time and this has already made a difference in both accelerating and leading to the faster termination of programs at Recursion. But it’s beyond model organisms. It also goes to the ultimate model organism and that is humans. With our Tempus data, we’re able to now aggregate patient data across oncology together with all of the wet lab data we’ve generated at Recursion. And in just about eight weeks since we’ve had access to this data, this has already led to our team combining our wet lab data and the patient data. So forward and reverse genetics coming together and allowing us in the context of non-small cell lung cancer to already identify multiple potential drivers of disease that we are predicting are causal, which in many cases have not yet been robustly explored in this space. So Recursion now has a program that has advanced just in the first eight weeks based on this kind of data and we’re really just getting started. But what’s happening is that as we continue to build this full stack of technology tools and as each of these tools runs through its virtuous cycle of learning and iteration and is improved rapidly, it’s becoming increasingly complicated for anyone to keep up with the latest on each tool, the right way to use each of these tools and we actually think this is going to be a problem across the industry, as we and many others are building lots of models and lots of different tools. And so we wanted to address that together with our colleagues at Valence Labs. And we were able at the JPMorgan Healthcare Conference, both in the conference, we think for the first time doing a live software demo and also at the event we co-hosted with NVIDIA to show off our LOWE system. This is a Large Language Model-Orchestrated Workflow Engine. And what this is allowing you to do, what our scientists and our partner scientists may be able to do with this technology, this tool, is to use natural language, to not have to be an expert programmer, to be able to access all of the tools, to be able to design experiments the right way, to order experiments and execute them on our platform, to analyze data and visualize data using the latest tools at Recursion. And really, this kind of technology is putting the power of the Recursion OS at the fingertips of all of our scientists and partners. And we see this trajectory as very similar to the early days, the late ‘70s and early ‘80s, in the personal computer space. You had products like the Apple One on the left, where you really had to be an expert user. You had to be comfortable with this microprocessor board. You had to be comfortable working at the command line in order to make use of this burgeoning new technology. And with subsequent Apple models, including Lisa on the right, we moved to a graphical user interface. And this created really a renaissance in the ability of more people to be able to harness the power of compute. And what we’re building with LOWE, with the Recursion OS, we believe is akin to this, but it’s really a discovery user interface. And we believe it’s going to allow each scientist at Recursion and beyond to make more progress faster. It’s going to mean that our teams are doing less of the toil and more of the thinking around our projects and it also means that these tools are going to be accessible, not just to scientists in biology and chemistry, but to software engineers and data scientists, to business development and to finance. And we think ultimately that’s going to be fantastic for the field and we believe Recursion is really leading out on this new trajectory for our industry. So before I move to questions, I want to just end with our near-term milestones, the things that we believe we’re going to hit over the next 12 months to 18 months or sooner. And I’ll start with additional INDs. We’ve got both our RBM39 program and our Target Epsilon program that we in-licensed from Bayer, moving towards the clinic. We’ve got more Phase 2 trial starts, AXIN1 or APC and C. diff that we believe will be starting this year. We have multiple Phase 2 readouts that I alluded to earlier. And all of this on top of a healthy balance sheet with nearly $400 million in cash at year-end 2023. And what’s more, we see the potential for significant runway extending options for our map building initiatives with partners and for additional partnership programs being optioned. And beyond that, we see the strong potential for additional partnerships in large intractable areas of biology, like cardiovascular metabolism and immunology, where we expect robust upfront payments that will further extend our runway. And what’s more, we have an at-the-market offering open, which we’re using in a very, very surgical way with the right investors at the right time in order to make sure that the company maintains a robust runway moving forward across all of these exciting catalysts. And finally, we’ve got the potential both on the BioNeMo platform and through our LOWE tool to make some of our data and some of our tools available to biopharma and commercial users. And there’s the potential for some of that work to generate additional revenue as well. So I hope you’re as excited about the future of TechBio as I am. I hope this has been helpful for you to see the trajectory of the company through 2023 into 2024 and how we see the future of our industry. And with that, I’m going to stop here and head over to answer some questions. And these are being updated by our team live. If you haven’t had a chance to ask a question yet, please log into the Slido tool and do so now. Thanks, Morgan. That’s a fantastic question. I would say that the response has been really, really robust. We had many R&D heads of large pharma companies at our JPMorgan presentation, which we co-hosted with NVIDIA. We had CEOs of large companies there, both tech and bio. And what we heard from people is, how do I get access to something like this? And we are doing the work now to increase the robustness of the LOWE platform. We’re having conversations with potential partners around how we could put these tools in their capable hands in a way that would be helpful to Recursion and to the industry writ large. As far as guidance around revenue, I don’t think we’re going to give guidance around revenue in the near-term. What I will say is that, we see the bigger opportunity in driving these companies towards really significant collaborations like the ones we’ve done with Bayer and Roche-Genentech, as they see the power of a tool like LOWE, probably that’s the bigger opportunity for us in the near-term compared to sort of recurring software revenue. But we certainly will take all the revenue we can get if we’re able to identify those questions. All right. Thank you. Next up, we have a question from Alec Stranahan of Bank of America who asks, how do you plan to utilize LOWE either internally or as an external offering? How does this fit into your existing full stack capabilities? This is actually a fantastic question because I think it highlights something that’s really important. LOWE internally at Recursion is being used by certain teams on the business development side and elsewhere. It certainly is something we think pharma could use. But I’ll actually go to a slide from our other deck here to say that internally, we actually believe there’s a step beyond LOWE, where autonomous agents use a tool like LOWE to drive discovery as opposed to individual scientists and I think this is a great example of this. This is a plot of thousands of targets in human biology. And what I’m showing you here on the Y-axis is how we’ve used a Large Language Model that is based on public data sets, like the cancer dependency map, open targets, TCGA, et cetera. And we have profiled all of these different targets to assess their relevance in oncology. Whereas on the X-axis, we’ve used a Large Language Model that’s looking only at proprietary data internal to Recursion. And so what you see on the top right are important targets like PIK3CA, BRAF, mTOR, EGFR, et cetera, where we see approved medicines for these targets in oncology. We see that these targets score robustly for oncology relevance based on both the public data and Recursion ‘s proprietary data. But we see hundreds of targets in the bottom right, in this blue box, that are now being automatically initiated as new pre-programs at Recursion without almost any human intervention based on our Large Language Model scores. And we see these as targets that have the potential to be totally novel. And so at Recursion, our scientists aren’t just using LOWE, they’re really using robust workflows that are highly automated. And LOWE is more of a tool that we see to collaborate with partners, that we see to drive partnership progress through our pipeline. Next question is from Jesse Brodkin, who asks why Tempol or REC-994 was chosen for your CCM indication when the vitamin D data appeared better in our preclinical screens. Thank you for the question, Jesse. There is a paper in Circulation discussing this work. We observed that both vitamin D and REC-994 showed a strong response in these preclinical models. However, REC-994 provided an additive effect on top of vitamin D. In the study, vitamin D was included in the chow of the mice, and the REC-994 treatment enhanced the observed effects. Since vitamin D is a very safe and commonly used supplement, which many people get naturally from sunlight, we did not find significant added value in advancing that program. In contrast, REC-994 was not accessible to patients and was not approved or available, so we believed it had the potential for additional benefits. That’s why we are focusing on advancing that program and are eager to share the data in Q3. It looks like we have a number of questions regarding our collaboration with NVIDIA. The first question is about our future involvement with them. We are currently focused on three main areas in our collaboration with NVIDIA. The first area is advanced computation, where we've had a longstanding partnership and have been working closely with their team. They have exceptional expertise in training multi-billion parameter models, which greatly benefits our algorithm development. We are collaborating on several of our larger models and have already utilized our priority access to NVIDIA in expanding our BioHive supercomputer. Additionally, we have the possibility of accessing DGX Cloud Resources with priority as well. We also see the opportunity to place more tools on their BioNeMo marketplace as we continue our development efforts. Our teams are consistently generating new ideas, and we look forward to exploring these with our colleagues at NVIDIA in the near future. The next question is from Mark Simmons who asks about our relationship and investment with NVIDIA regarding AI and their products. I believe we’ve covered this topic already, so I will move on. The next question is anonymous and it's a good one. Why have insiders been selling shares each month? Do they not have confidence in the company? That’s an important question, and I appreciate the opportunity to address it. I'll speak for myself since many look to the CEO for insights on insider trades. In 2023, I sold a very small number of shares, approximately 4% of my holdings. All of these transactions were conducted using pre-planned sales and purchases under rule 10b5-1. If you consider the broader context, my total trades in 2023 make up about 6 or 7% of the total volume Recursion has traded in the market today. Although there are many sales and purchases among insiders, the actual size of these transactions is relatively minor, and my intention has been to ensure proper diversification. This is my first job after graduate school, and I still hold the vast majority of my shares from the beginning, specifically from the IPO. I plan to retain most of my shares moving forward because I have strong faith in what we are building here. I've committed my life and career to this endeavor. Next up, we’ve got questions in our fibrosis project. So Alec Stranahan asks, fibrosis has been a historically challenging area for development. This is true. How is the asset you unlicensed differentiated and what are the first disease areas of focus? Well, Alec, I really appreciate that question. I’m not going to share the first disease area of focus yet because the novel target we’re working on we think has the potential to be useful in multiple different areas. And so we’re going to probably hold that information back from a competitive standpoint for a while. What I will say is the differentiation here is that we used a very complex assay. We essentially looked for small molecules that were mimicking the effect of Pentraxin-2 in a complex fibrocyte assay. And what we saw was a number of molecules. Since then, we’ve really optimized one of those molecules, 1169575, and additional molecules that we’re advancing as backups. And we think this novel mechanism, and if you knew the mechanism, I could tell you more, but we’re not going to share it yet, has a lot of potential to modulate the immune response that could be broadly useful across this space. So we’re aware of the challenging development space. We certainly could imagine partnering this program as we get into sort of the Phase 2 portion of the clinical trials. But we think this one is important and worth advancing because we’re unaware of anybody else taking this target or this target class forward in the context of modulating the immune system to drive a reduction in fibrosis. All right. The next question comes from Jesse Brodkin, who asked, did Recursion pay Bayer any money to obtain the fibrotic disease lead candidate from the collaboration? So Jesse, this program was advanced under our original fibrosis collaboration and specific disclosures around the financial terms can be found in the 10-K. And we’ll be filing that 10-K here in the next 48 hours or so. So you can look there. But what I will say is we didn’t have to pay anything upfront. There are some modest milestones that we think are very attractive as we drive this program forward. And I think both we and the scientific team at Bayer are pretty excited to see what we can do with Target Epsilon. All right. The next question, back to Morgan Brennan from CNBC. And Morgan asks, what proof points can you share on AI, ML and medicine? And are AI applications in drug discovery happening as quickly and effectively as you anticipated? Morgan, it’s a great question. So I will share that I’m a founder and I don’t think any founder is ever satisfied with the pace that anything is advancing. So I can say no, things aren’t going as fast as I would have liked. But I think if you look back at where Recursion started in 2013, where other companies like us started, and where we are today, we now have developed at Recursion multiple tools that are state-of-the-art in terms of target identification, in terms of making ADME and Tox predictions. We have a pipeline of five programs in Phase 2, or nearing Phase 2. I think we can be really proud of the platform we’ve built, the pipeline we’ve built, the partnerships we’ve built. Some of our partnerships are not only, our Roche-Genentech partnership is not only the largest partnership in TechBio today, it’s one of the largest partnerships ever disclosed in biopharma in terms of total kind of bio box potential. And so I think that while the next 12 months to 24 months is going to feel to all of us like we’ve kind of under-delivered, we’re on this sort of exponential curve where if we look back in five years to 10 years, we’re going to be amazed at how far things go. But the reality is, like with any new technology, it takes time. And if we run these virtuous cycles and we get 1% to 2% better each time, but we can compound those efficiencies through many, many cycles, I think over time we’re going to see a fundamental transformation of the biopharma space that over a decade is going to feel much more profound than most people believe today. Next, we have a question from Curtis Maxwell who inquires about the backlog of projects for AI analysis, including the typical cost and duration per project. Referring to our initiatives at Recursion, we can provide some insights. At Recursion, we aim to transform the traditional pharmaceutical development model into a more efficient one, where we can utilize our previous data and algorithmic strategies to predict the best molecule for each patient and advance it to the market without setbacks. While reaching this ideal state may not be fully attainable, we strive to progress in that direction. Compared to industry standards, Recursion is already altering our internal processes to resemble this model more closely. Our current performance shows that our costs to reach the Investigational New Drug stage and our timelines for validated leads are significantly better than the industry averages. Looking ahead, we aim to demonstrate that we can at least match the industry's probability of success, but with faster timelines and on a larger scale for our company, and we anticipate that each generation of our programs will enhance this. Ultimately, we aspire to show the industry that we can improve the success rates of our programs, particularly addressing unmet needs in rare diseases and oncology, where we could potentially lead in treatment options. We also plan to utilize this platform for rapid follow-up projects based on outstanding scientific advancements occurring elsewhere in the industry. There is a lot of promising work ahead. We have another question from one of our analysts regarding the increasing complexity and layering of data on our platform. How do we define a proof-of-concept in a constantly evolving environment? That's an excellent question, Gil, and it highlights a difference in mindset between the tech and bio industries. We believe in continuous cycles of learning and iteration. This makes it challenging to keep up with the latest tools and their updates. However, we ensure that every program at Recursion utilizes the most current generation of the tools we develop. This is why we refer to the generations of our clinical pipeline, starting with first-generation programs that primarily target rare genetic diseases, crafted before we established a chemistry team. Most of these initial programs involve molecules we identified as new opportunities for existing chemical entities using our machine learning and AI platform. In our second generation, we will incorporate new chemistry and digital chemistry tools into these programs as we advance them. As we move towards a third and fourth generation, we anticipate that this platform will learn and improve, resulting in each generation of programs having a higher probability of success and a greater impact. Let’s move on to some questions from investors regarding revenue. We have a question from Eric Joseph at JPMorgan about how investors should view the company’s business model at this stage. That’s an excellent question, Eric. Ultimately, in our industry, the measure of impact and success is having assets in the clinic. This is why Recursion has focused not only on developing software-as-a-service and building partnerships but also on advancing a strong internal pipeline in niche areas of biology that have high unmet needs, as well as in partnerships targeting significant, challenging areas of biology. We continuously conduct business experiments at Recursion. For example, LOWE and Phenom-1 were both business experiments. While we don’t yet know how these will influence our business model directly, I am confident that Recursion will stay committed to introducing new compounds into high unmet need areas of biology or finding ways to reduce the costs of existing molecules that have been brought to market. Therefore, you can rely on this being central to what we’re developing at Recursion, and we will pursue all of this with a more technology-centric approach compared to many other companies in this sector. All right. Back to Gil, one of our analysts. Do you anticipate that over time, more value will be created from the company’s internal pipeline or through its partnerships? Well, Gil, if we’re talking about long-term, I believe Recursion is going to generate much more value from our internal pipeline than our partnerships. We expect to generate significant value in our partnerships today. We signed these partnerships with Roche-Genentech and with Bayer because we saw them as having transformational potential for patients and the potential for extraordinary impact in areas of high unmet need. But as each of those partnerships finishes, we expect to have learned what we need to as a company to be able to build our own internal pipeline into those more complex intractable therapeutic areas. And until every disease has a treatment, we won’t rest and so I think you can count on Recursion’s internal pipeline being a robust primary driver of our growth if we’re to look out over the intermediate and long-term. All right. Back to Eric Joseph at JPM. What’s envisioned as its earliest and most significant lines of product revenue? I assume that Eric’s talking here about some of our software tools like LOWE. Eric, we’re having lots of discussions with biopharma companies today about how we might integrate a tool like LOWE and our teams at Recursion with them. I think it’s too early to talk about the significance of these lines of product revenue. I don’t think it’s too early to talk about how Recursion leading the field with tools like LOWE is helping pull the industry forward, partnering with extraordinary companies like Roche-Genentech and Bayer to help move the entire industry forward. And I think over time, whether it’s through the software offerings themselves or whether it’s through new chemical entities that we discover with our partners or in our own pipeline, I think we’re going to drive a tremendous amount of product revenue leveraging these tools. We have a question from Kareem Harrison regarding when the company will be profitable. That's a great question. We see the opportunity in front of us as a multi-trillion-dollar opportunity with significant potential impact for patients. There are not many industries today where, despite the efforts of hundreds of thousands of talented scientists, our industry still experiences a 90% failure rate in the clinic on average. Additionally, there are about 20 to 25 biotech and biopharma companies with market caps over $100 billion. This kind of lack of concentration is quite unique to biopharma. Therefore, we believe that in the next 10 to 20 years, there will be significantly fewer biopharma companies, and those that remain will resemble Recursion more than traditional biopharma companies. We aspire to be one of them. This means we will focus on growth in the coming years. We will manage our capital responsibly while prioritizing growth. As we recognize the scale of this opportunity, we aim to reduce losses quarter by quarter in the short term with upcoming milestones and revenue. In the long run, we don't plan to focus on maximizing profitability because we believe there is a multi-trillion-dollar opportunity that could benefit hundreds of millions or even billions of patients over the coming decades. Next up, we have a question from Juan Fernandez who asks about the company's vision and the daily actions being taken to achieve it. This is a great question. We believe that biology and chemistry are deterministic, and that with the right data and technology tools, we will eventually be able to predict how any biological and chemical interaction operates, not only in human cells but also in any living organism. Our vision is to digitize this space and transition from wet lab to dry lab, where experiments are conducted solely to validate our predictions at scale. If we can accomplish this vision, we could become one of the most impactful companies in the world. So, how do we manifest this every day? We promote a Recursion mindset among our team. We host events like Decoding Recursion, where new and experienced employees come together to discuss how to focus on our experimental goals. Instead of following the conventional path, which we know the probable outcome of, we aim to explore new ways to discover and develop medicines. This philosophy is ingrained in every team member at Recursion and extends to our partnerships as we encourage our partners to adopt new tools and workflows. We emphasize this vision daily with weekly all-hands meetings, where I frequently present. We gather to reinforce our commitment to this vision, and we are unapologetic about it. We believe someone needs to strive to not just improve this space slightly, but to revolutionize it. We are grateful that Recursion is at the forefront of this effort, along with many other TechBio and biopharma companies, who are making significant strides toward a markedly different future. Next question from Steve Deckert. Do you have a rough timeline for when you might submit an IND for Target Epsilon? Thanks, Steve. We are currently beginning IND-enabling studies. We just moved that program forward in the last week or so. I believe we will be able to provide a clearer timeline in the upcoming quarters. However, I know that the team is aware that I am never satisfied, and our goal is to prioritize speed and quality for that program as well as for all other initiatives at Recursion. All right. Now we have a question from Steven Greenwood who asks, would you consider looking at multiple sclerosis and the issue of remyelinization? Steven, that’s a great question. And certainly I can’t talk about the specific areas of neuroscience that we may collaborate on with Roche-Genentech. But what I will say is that an important limitation of the platform that we built today at Recursion is that it is not yet built, I think, to build models of complex multi-organ systems or tissue systems. It’s really built today to understand in a very deep way cell-type autonomous biological mechanisms and we’re working on that. We’ve got spheroid models and organoid models, both internal at Recursion and potentially through partnerships that we could be working on in the future that think will move us in that direction. But if I’m very honest today, I don’t think Recursion would be best suited to go after MS or remyelination, though certainly we’ll be working with our partners at Roche & Genentech to take this platform in whatever direction they’re most excited to drive it. We certainly know that there’s a high degree of unmet need in that space. Next, we have a question from Steven Ma who asks if causal AI modeling with Tempus data will be used for internal drug discovery efforts, partners, or both. He also inquires about any changes in business development discussions post-Tempus and the associated economics. So, Steven, the economics are included in our presentations and filings for you to review. To save time, I’ll let you look at those. What I want to emphasize is that we will definitely be utilizing our causal AI models for both our internal programs and closely partnered initiatives at Recursion. For instance, in our oncology collaborations with Bayer and Roche & Genentech, we can train models using the Tempus data for specific projects with those partners. However, we are not simply reselling or memorizing the Tempus data without a genuine partnership. With strong partnerships like those with Roche & Genentech or Bayer, we are indeed permitted to apply those insights and move them forward. Additionally, this year, we acquired two companies, developed new foundation models, finalized the Tempus deal, and have been transparent with our partners about our goal to share updates quickly in our collaborations, as we are motivated to deliver medicines to patients alongside our partners. All right, last couple of questions here. Here we are looking at CCM. Gil’s asking, what can you guide, if any, on the upcoming CCM readout? Gil, all we’re guiding at this time is that we’re going to have preliminary or I should say topline safety, tolerability and exploratory efficacy coming out in Q3. And we’re excited. We hope, of course, that those data are positive, that they lead us to be able to advance that program forward for this important area of unmet need. But we know that regardless of what those data are, they’re going to help us improve our platform and to learn and grow as a company. Next up from NK, how is Recursion thinking about commercializing its CCM program if the data is very positive? Great question, NK. We have a broad commercialization strategy at Recursion. We believe that if some of our early programs are successful, they could present solid opportunities for outlicensing, selling, or partnering to generate revenue for the company and create a self-sustaining platform. Unlike many other biopharma companies that focus on one or two exciting programs, we believe every program at Recursion should improve upon the last. If we genuinely hold this belief, we should be open to selling or licensing our successful early programs to fund the next five, ten, or twenty programs that we advance. CCM might be a strong candidate for that. Over the intermediate or long term, we’ll need to monitor industry trends. We’ve been generally disappointed with the adoption of certain technology tools until very recently, in the past 12 to 18 months, where it seems the industry is finally getting excited about the potential of machine learning and AI. Depending on the pace of industry progression, we may eventually choose to advance and commercialize our programs. However, if we do, it's unlikely to follow the traditional commercialization methods. We see numerous opportunities, and larger companies like Lilly are exploring direct-to-payer and direct-to-consumer models. I can envision Recursion adopting a membership approach to incentivize physicians and benefit patients, but we are really discussing intermediate- to long-term strategies here. It seems the team has sent over a final question due to time. If our company were an animal, what animal would it be? This question comes from Johnny Gray. We’ll end on a humorous note. Clearly, our company would be an octopus. You can see here when we got our first Phase 2 program and dosed our first patient in that Phase 2, I promised the company that I would get a tattoo to commemorate that milestone, which we hope will be the first of many. The octopus plays a very significant role at Recursion, and I believe it’s the perfect representation for us. So, thank you, Johnny, for that amusing final question. Well, I hope everybody enjoyed this first earnings learnings call at Recursion. We intend to do this over the coming quarters. And we got a lot of potential milestones in 2024 and beyond. So I think these are going to be really exciting. I’m going to have other executives join me on future learning calls. And if you have suggestions, ways we can make this better, we want this to be adaptive. We want this to be accessible and so please reach out with that feedback on our social media platforms. Thanks everybody for tuning in and I look forward to seeing you again really, really soon.