Recursion Pharmaceuticals, Inc. (NASDAQ:RXRX) Q4 2023 Earnings Call Transcript

Recursion Pharmaceuticals, Inc. (NASDAQ:RXRX) Q4 2023 Earnings Call Transcript February 27, 2024

Recursion Pharmaceuticals, Inc. isn’t one of the 30 most popular stocks among hedge funds at the end of the third quarter (see the details here).

Chris Gibson: Hi, everybody. I’m Chris Gibson, Co-Founder and CEO of Recursion, and I am really excited to welcome you to our first ever Learnings Call here at Recursion. So what is a learnings call and why are we starting this practice now? A traditional learnings call has a lot of value, but over the years I think these have become extraordinarily scripted, frankly quite boring in many cases and hard to access for all of the stakeholders that we want to be able to speak to. Learnings is our interpretation of a traditional earnings call, which we feel is more authentic, so I will not be scripted today, I’ll just be working off of the slides in front of me, adaptive and we hope easy to access. And please, if you have suggestions on how we can make this better going forward, please send them our way.

What I would also say is that, we’ve chosen to initiate our first learnings call at this moment, at the start of 2024, because as we look ahead at the future of Recursion, the milestones and catalysts coming before us are going to be coming fast and furious, and we want to make sure that we have a robust mechanism to reach out to all of our stakeholders on a quarterly cadence, and to be able to share all the incredible work that we’re doing here at Recursion with you. So to frame where we are today, where we’ve been and where we’re going, I want to start by going back really a decade, going back to the origins of TechBio, one decade ago. And it was a really interesting time in the early 2010s. You saw technology companies coming into a wide variety of industries and leveraging a pretty straightforward playbook to bring fundamental new advances from how we get around cities, to how we think about our preferences for digital media, to how we even think about what products we want to order.

And what these companies did was quite straightforward. They used technology to capture high dimensional data to create a digital record of reality. And it’s important to note that the data that they collected was rich, very, very rich and high dimensional. They aggregated and digitized that data, and then leveraged algorithms to make predictions across all of these massive data sets. And most important of all, they went back into the real world to test those predictions. So whether that’s telling you to turn left instead of right, whether it’s telling you to buy product A instead of product B or to watch TV show X or Y, these algorithms could be tested in their ability to predict the right outcome in a real setting. But in biology, this has been extraordinarily challenging.

There are so many roadblocks to aggregating and generating the right data to be able to map and navigate this complex system of biology and chemistry. There are three primary drivers of that. First, this world is very analog standard. It was more so in the 2010s, but it still is in some ways today. There are still CROs who send you scanned PDFs or printouts with handwritten notes. And in the biopharma industry, there’s a tremendous amount of data, hundreds of petabytes of data, but that data was collected in a way that wasn’t built for the purpose of machine learning. And so it’s often siloed on legacy servers, it’s often built without the right kind of high dimensional nature or the right kind of metadata to make it easier to extract the connections across and between all of those different data.

And then of course, there’s the public datasets that we and others use. But as you all know, there’s a reproducibility crisis, and there are real challenges, because just like in the pharma data, there’s not enough metadata and not enough relatability of this data across all these different publications and data sources. And so it’s very, very challenging in the biopharma industry to aggregate and generate the right data. But what we and other companies who are today leading TechBio saw in the early 2010s was an opportunity. We saw exponential improvements across five main areas. The first was the cost of storage. So in the early 2010s, we were at the end of a 40-year cycle of precipitous decreases in the cost of storage. And this is important because a company like us at Recursion today with over 50 petabytes of proprietary data has to be able to pay to store all of that data.

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Q&A Session

We were seeing a radical increase in the availability of compute. We’ll talk more about our supercomputer a little bit later. We were seeing an increase in accessibility and flexibility of automation tools that allowed us to pioneer and industrialize a new kind of omics using robotics. We were seeing a renaissance in new biological tools like CRISPR. And then, of course, the field of AI was making extraordinary strides as we took 20 years of learnings and really invested in billions of dollars across the tech industry to move from expert systems into this neural net modern AI age. And now fast forward to today, where Recursion is right now, leading TechBio. We are taking that same formula that was so obvious across the technology companies of the early 2000s and 2010s, and deploying it now across the biopharma industry, where, as I said before, the data is so hard to generate and so hard to aggregate.

But we are doing it here at Recursion. We’ve built a massive automated platform where we can profile biology across human cells, rodent cells, in vivo systems and even patient data. We can extract that data in high dimensional space, aggregate it and then train algorithms on our supercomputer and cloud computing resources to make predictions. And this is the most important part. More than any other company in this space, I believe we are set up to take the predictions from our algorithm and test them back in the lab and creating that virtuous cycle of learning and iteration is the Recursion OS. It’s what we’ve been building for the last decade and it’s what we see positions us, the data, the technology together and this virtuous cycle to really define and lead the TechBio space in the decade going forward.

But we’re not just building at one point in the drug discovery and development process. It takes hundreds of steps to discover and develop a drug and Recursion today is building these virtuous cycles of wet lab and dry lab of learning and iteration at points from how we connect patient data into our targets to how we optimize chemical compounds, how we translate these programs and now early work in how we even identify the right patient cohorts to drive our programs into the clinic. I think more than any other company in this space, really building the full vertical TechBio solution. And that means that we are leading TechBio in 2024 across three primary areas. Our internal pipeline, our partnerships and our platform. Recursion is leading.

Our first-generation programs, five Phase 2s, either enrolling or soon to enroll patients that are really focused in capital efficient niche areas of biology. And we’re excited to have second-generation programs that are leveraging some of the tools that we have built or added to our platform in just the last few months moving to the clinic as well. If we build this platform right, every generation of programs will be better than the last. But it’s not just our internal pipeline. We’re also learning from and working with partners across both bio and tech. On the biology side, we’re partnered with Roche-Genentech in neuroscience and one oncology indication and then also partnered with our colleagues at Bayer in precision oncology. But unlike many other companies in this space, we not only have the therapeutic partnerships, we also have partnerships across data with companies like Tempus, across compute with companies like NVIDIA and across chemistry with companies like Enamine.

And it is this cross-credentialization of technology partnerships and biology partnerships that we believe sets us apart. And all of these partnerships and pipeline are based off of the Recursion platform. Today, over 50 petabytes of proprietary biological and chemical data spanning human cells to rodent cells to model organisms to human patients. And in order to make use of all of that data substrate, at Recursion today, we now own and operate the fastest supercomputer in the biopharma space. And in order to take the predictions from the algorithms that we generate on this computer and test them in the lab, we have industrialized and automated multiple levels of omics data generation at Recursion. On our Phenomics platform, for example, we’re able to do more than 2 million experiments in any given week.

And so before I talk about what we’re looking out to in terms of our near-term catalysts and milestones for Recursion, I want to take a moment to just look back at 2023. And I want to do this because I think it was one of our very best years. Amidst a challenging capital market environment, this team delivered on our pipeline, our partnerships and our platform. And so we’re going to go through just a few of the highlights. First, I’m going to start back in May, where we announced simultaneously on the same day the dual acquisitions of Cyclica, a digital chemistry company that’s based in Toronto, and Valence, a cutting-edge AI laboratory for drug discovery that’s based in Montreal. And we were able to fully integrate the Cyclica team in just 90 days.

And in a few minutes, I’ll share with you some of the output from that acquisition that led to us advancing and improving our programs within just a few months of signing that deal. On the Valence side, I’ll show you LOWE later, which is our Large Language Model workflow orchestration engine and this has really been driven by the Valence team. And I will set the stage for how we see a new direction for how biopharma is going to access all of these incredible new TechBio tools. In June, we announced that our first clinical trial, SYCAMORE, this is a trial for the first therapeutic candidate to be advanced by any industry sponsor into Phase 2 for cerebral cavernous malformation and I will remind you that is a massive area of unmet need. This is a disease that affects roughly 6 times the number of patients as cystic fibrosis and yet we are the first with an opportunity to be first in disease.

This program was fully enrolled in June across 62 patients in three arms. And one thing that gives us a lot of confidence about the tolerability of this molecule is that today, as patients finish their 12 months on therapy, the vast majority continue to opt into our long-term extension study. And so we’ll be reading out the topline phase two data in Q3 of this year. This will be our first real POC readout and we’re really excited about the opportunity, not only to potentially drive forward an exciting medicine for an area of significant unmet need, but also regardless of the outcome of that study, to learn and put that data back into our platform so that the next generation of molecules can be even better. Then in July, a month later, we announced our collaboration with NVIDIA.

This included a $50 million equity investment. And with our partners at NVIDIA, we’re working on advanced computation, so foundation model development; we’ve got priority access to compute hardware, which I’ll talk about later; and the DGXCloud Resources and we talked with them about the potential for us to put some of our tools into their BioNeMo marketplace. And in fact, just last month in January at the JPMorgan Healthcare Conference, we released the first third-party tool to exist on NVIDIA’s BioNeMo platform. That was our Phenom-Beta foundation model in January of 2024. So very excited about this ongoing collaboration. One month later in August, we were able to deliver a demonstration of how we leveraged the May acquisition of Cyclica and our brand new partnership with NVIDIA to drive a real value into our platform.

We were able to predict the protein ligand interactions for more than 36 billion compounds from the N Enamine REAL Space across about 80,000 predicted binding pockets spanning the human proteome. And what this did was generate a large in silico data layer for us, a synthetic data layer. So when we find a new target or an initial hit, we can immediately prioritize that target based on a potential mechanism of action and we have already advanced multiple programs, terminated multiple programs or changed the course of multiple programs using this exciting new technology. So we really see it as fantastic to have the complementarity of this functional machine learning algorithm alongside our or this physical machine learning algorithm alongside of our functional biology based platform here at Recursion.

Then in September, we announced the Phase 1 study results for REC-3964 in C. diff colitis. The molecule was safe and well tolerated at multiple doses up to 900 milligrams. There were no SAEs and no discontinuations that were related to treatment. And along with the favorable PK profile, this gave us the confidence to advance this new chemical entity towards a Phase 2 trial, which we will initiate later in 2024. Then back to our platform in September, we announced our first foundation model we call Phenom-1. It’s the world’s largest Phenomic foundation model that we’re aware of. And I want to take a moment just to talk a little bit about this, because I think it’s really exciting, especially given all of the talk around Large Language Models in the background.

In a Large Language Model, one trains a neural network to predict the next word in a sentence or in a paragraph. And we’ve done something similar here, but instead of using written language, we’re using the language of images of human cells. And what you can see on the left is an image where we’ve masked 75% of the cellular image and we’ve trained a neural network to predict what the rest of that image would have looked like. That’s the middle row here and you can — or the middle column. And what you can see is that our neural nets got really good at doing this. You can almost not even tell the difference between the Phenom-1 reconstructions and the original image. But we’re not in the business of reconstructing masked images at Recursion.

That’s just a training task. And like in a Large Language Model, where the ability to predict the next word in a sentence led to these emerging features that almost gave us a sense of rational thought in ChatGPT and other sorts of settings, we’re seeing emergent features from these foundation models. So against a wide variety of benchmark tasks in drug discovery, these sorts of models are giving us state-of-the-art performance to rediscover known biology, to make predictions about admin talks and beyond. One of the things that was most interesting about this work, though, was that we were able to demonstrate that the scaling hypothesis holds in the world of biology. We were able to demonstrate that is that the bitter lesson holds true and that one must have more data and more compute, all else being equal, in order to build a better model.

And so based on that, just two months later, we announced with our partners at NVIDIA that we were expanding our supercomputer, which was already the fastest supercomputer wholly-owned and operated by any biopharma company, with another 504 NVIDIA H100s. And this is a picture of the team just a week or two ago where these H100s have arrived on site and we believe when this system is up and running, it will not only be the fastest supercomputer in the biopharma space, but it has the potential to be one of the fastest supercomputers privately run in any industry. So we’re really, really excited about the potential to get this thing up to speed and humming. But going back to our partnerships, in October, we also announced that Roche had exercised the first program under our collaboration, this program in the context of oncology.

And this was fantastic, less than two years after signing that collaboration to already have a program advancing forward with our partners and we hope and expect that this is the first of many options to come across this partnership and others. In November, we then announced another partnership. This time, instead of just generating data at Recursion, partnering with Tempus to aggregate what we believe is extraordinarily high quality patient data into our platform, access to the DNA and RNA sequencing data sets and clinical records for over 100,000 patients that we can now train causal AI models on using the Recursion OS. That gives us now access to over 50 petabytes of proprietary biological and chemical data that we’ve either generated in-house or partnered with companies like Tempus to bring in place.

And I’ll talk more in a minute about how we’re already leveraging this partnership to drive value in our platform. Also in November, we announced an update to our partnership with Bayer, focusing on precision oncology. And I think it’s important to note that with this update, we were able to more than double our per program milestones, which I think is a strong signal about Bayer’s excitement around what we’re building. And I know the teams are already hard at work together at Bayer and at Recursion to drive forward some of these initial new oncology programs together. Coming out of that same partnership, before it moved to precision oncology, it was focused in fibrosis and there was a program that was part of that that we thought was just too good to let go to waste.

And so we were able to negotiate with our colleagues at Bayer to in-license this program, which we call Target Epsilon, which we believe is a novel target in the context of fibrosis and we are driving this program forward very quickly. In fact, we’re announcing with today’s earnings that this is now in IND-enabling studies at Recursion. So we’ve already advanced it inside of our own internal pipeline. And finally, in December, we also crossed the threshold of having generated over 1 trillion neuronal iPSC cells since 2022 and based on the publicly available data, we believe that this makes us the world’s largest producer of high-quality neuronal iPSC cells. And this is but one example of the way our team is working with complex biology, co-culture systems, a wide variety of biology to drive our platform forward into new, exciting areas like neuroscience.

All of this underlying our pipeline, which, as I shared earlier, we believe is the most robust, deepest and broadest in the TechBio space. And we are now looking forward at 2024 with this learnings call setting the table for a number of important catalysts that are coming up. First, our Phase 2 topline readout for CCM in Q3, then a preliminary safety and efficacy readout for NF2 in Q4, and then in the first half of 2025, a preliminary safety and efficacy readout for FAP, the initiation of our Phase 2 program for C. diff colitis later in 2024, and then another Phase 2 safety and preliminary efficacy readout in the first half of 2025. So Recursion really beginning with this third quarter in 2024, setting the table for what we hope can be roughly quarterly readouts that we hope will help propel the company and the platform forward.

Beyond these early first-generation programs, we’ve got our Epsilon project and our RBM39 project, which are the first of our second-generation of programs, making use of some of our newest tools and we’ve got more than a dozen discovery and research programs in oncology or with our partners coming behind those. Now, before I talk about where we are today and what we see as catalysts in the near-term beyond just our pipeline, I want to orient you to the broader trajectory of the space of TechBio, at least as we see it. 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 AppleOne 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 BD 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 ATM 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.

A – Chris Gibson: 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 BD 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 is 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. All right. Next question is from Jesse Brodkin [ph], who asks, why did you choose Tempol or REC-994 for your CCM indication when the vitamin D data looked better in our preclinical screens? Thanks, Jesse, for that question. There’s a circulation paper that you all can read about this work. And what we noticed was that both vitamin D and REC-994 had a robust response in the context of these preclinical models. However, REC-994’S response was additive on top of vitamin D. So there was vitamin D in the chow of the mice and the REC-994 treatment added to the effect that we saw. And given vitamin D is a very safe, widely available molecule that many people take in their everyday, you get when you stand out in the sun as well, we didn’t see a lot of added value in us bringing that program forward.

Whereas bringing REC-994, which was otherwise inaccessible to people, it was not approved, not available forward, we believe there was the potential for additive benefit. And that’s why we’ve driven that program forward and we’re so excited to read out the data in Q3. All right. It looks like next we’ve got a number of questions around our NVIDIA collaboration. The first from Harry Schoenberg [ph] at JPMorgan, who asks, what involvement will you have with NVIDIA in the near future and going forward? That’s a great question. I’ll go back to the slide on our NVIDIA collaboration here. And just to reiterate that with NVIDIA, we are really focused in three areas currently. The first is advanced computation. We’ve been working with the team there for many years.

We think they’re incredible and they’re helping us take the algorithms that we’re building and help scale them, help tune them. And there’s not many people in the world who have a lot of experience training multi-billion parameter models, but there’s a great team at NVIDIA that’s done just that. And so we are collaborating really closely on some of our larger models. What’s more, we’ve already demonstrated the use of our priority access from NVIDIA in our expansion of our BioHive supercomputer. And of course, there’s the potential for us to access the DGXCloud Resources in a priority way as well. And then, finally, we see the potential for us to put potentially additional tools on their BioNeMo marketplace as we continue to develop these tools.

And who knows? The collaboration with NVIDIA is very, very close and we know that our teams are constantly coming up with new ideas and we’ll be excited to try some of those out with our colleagues there in the near future. Next question is from Mark Simmons [ph] who asks, describe the relationship and investment with NVIDIA regarding AI and their products. I think we’ve really hit on this one already, so I will move on. Okay, the next question is anonymous. This is a good one. Why have insiders been selling shares each month? Do they not have confidence in the company? That’s a great question and I’m glad we’re addressing it. So I’ll speak for myself, because I think most people look to the CEO when it comes to insider buys and sells.

And in 2023, I traded a very small, relatively small number of shares. In fact, it was roughly about 4% of my holdings that were traded. And so all of these trades were done using 10b5-1 pre-planned sales and purchases. And again, I traded roughly 4% of my holdings. If you were to look at that at the grand scale, just on our volume today, all of the trades I did in 2023 represent roughly 6 or 7% of just the volume Recursion traded today in the market. And so I think while you see many of these sales, many of these purchases across insiders, the reality is that the magnitude of these is relatively small and we’re using these for making sure that we’ve got the right diversification in place. This is my first job out of grad school and so I have the vast majority of my shares that I’ve had from the beginning, the vast majority of my shares that I had at the IPO and I intend to keep the vast majority of my shares moving forward because I definitely believe in what we’re building here.

I’ve dedicated my life and my career to it. 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 bear 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’s 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.

All right. Next up we have a question from Curtis Maxwell [ph] who asks, what is the backlog of projects that are in the pipeline for AI analysis and what is the cost per project and duration typically? So here we can actually look at, if you’re referring Curtis to our programs at Recursion, we can share some of these statistics. We believe at Recursion that we’re trying to shape this traditional V of the biopharma industry into more of a T, where one day we will be able to take all of our prior data and our algorithmic approach and predict the right molecule for each patient and drive it all the way to the market without any attrition. Now, that T is going to be impossible to actually completely achieve, but we want to move in that direction.

And you can see compared to the industry average, Recursion already starting to shape our internal funnel to look more like that T and less like that V. And what we’re able to demonstrate so far across our programs is that our cost to IND and our time to validated lead significantly outperform the industry averages. What’s next up is that we hope that we’re going to be in a position to demonstrate at least meeting the probability of success of the industry averages with a faster time and higher scale for the size of our company and every generation of future programs we hope will build on that. And one day we hope to be able to demonstrate to the industry that we can increase the probability of success of our programs. And we can drive them forward not only in areas of unmet need in rare disease and oncology, where we can be first in disease potentially, but also one day to leverage this platform to fast follow at scale, to be able to take programs at Recursion that we can drive extraordinarily quickly based on the incredible science that’s being done elsewhere in the industry.

So a lot of good work to come there. All right. Looks like we’ve got another question from one of our analysts here. Given the complexity and layering of data keeps growing on your platform, how would you define a proof-of-concept in a constantly moving platform? That’s a great question, Gil [ph], and I think this speaks to a difference in mentality across the tech and the bio industries. We believe that these virtuous cycles of learning and iteration must always be running. And that increases the challenge of keeping up with the latest tool — the latest version of that tool. But we want to make sure that every program at Recursion uses the latest generation of every tool that we’re building. And that’s why we talk about the generations of our clinical pipeline, the first-generation programs, which are by and large focused in rare genetic diseases before we had a chemistry team.

So most of those first-generation programs are actually molecules where we used our ML and AI platform to identify a new opportunity for a known chemical entity. And you’ll see in our second-generation, you’ll start to see the layering in of new chemistry and digital chemistry tools to these programs as we advance them forward. And as we run a third-generation and a fourth-generation in the future, I think, you’ll see we hope that this platform learns, that this platform improves and that every generation of programs will have, on average, an increasing probability of success and we hope increasing impact. All right. Let’s go now to some investor and revenue questions. We’ve got a question here from Eric Joseph at JPMorgan. How should investors generally be thinking about the company’s business model at this stage?

Eric, that’s a fantastic question. At the end of the day, in our industry, the currency of impact, the currency of success is assets in the clinic. And I think that’s why Recursion has not just focused on building software-as-a-service, not just focused on our partnerships, but has a robust internal pipeline that we’re advancing in a small — in areas of small niche corners of biology with high unmet need and partnerships where we can go after large, intractable areas of biology. We are always doing business experiments at Recursion. LOWE is a business experiment. Phenom-1 was a business experiment. And we don’t yet know how those will drive our business model per se, but what I’m confident in is that Recursion will always be focused in bringing new composition of matter into areas of biology with high unmet need or where we can drive down the costs of expensive molecules that have been advanced into the market.

So I think you can count on that being at the core of what we’re building at Recursion, but we’re going to do all of that with a much more tech-focused mindset than I think many other companies in this space. 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. All right. Next up, we have a question from Kareem Harrison [ph] who asks, when will the company be profitable? Well, that’s a great question.

I think we see the opportunity before us as a multi-trillion-dollar opportunity with profound potential impact for patients. There are few industries today where despite hundreds of thousands of really incredible scientists working really, really hard, on average, our industry still fails 90% of the time in the clinic. And what’s more, I think, there are roughly 20 or 25 biotech and biopharma companies today with market caps above $100 billion. That kind of lack of condensation of these companies, I think, is pretty unique to biopharma. And so we believe that if you look out 10 years to 20 years, there will be a much smaller number of biopharma companies and those companies will look much more like Recursion does today than they will look like a traditional biopharma company, and we hope and expect to be one of those.

And what that means is that we’re going to lean into growth in the coming years. We’re going to be good stewards of our capital, but we’re going to lean into growth. And so because we see the magnitude of that opportunity, while we hope to decrease with the upcoming milestones and revenue, we hope to decrease the losses on a quarter-by-quarter basis in the intermediate term. In the long-term, I don’t think we’re going to lean into maximizing profitability because we think there’s a multi-trillion-dollar prize and impact for hundreds of millions or billions of patients on the line over the coming decades. All right. Next up, we’ve got a question from Juan Fernandez [ph], who asks, what is the company vision? What daily actions are being taken to achieve it?

Juan, this is a great question. We believe that biology and chemistry are deterministic, that with the right data and the right technology tools, we will be able one day to predict how any biological and chemical interaction operate, not only in human cells, but in the human organism and beyond the human organism in any living organism. And our vision is to be the company that digitizes this space, that moves from wet lab one day entirely to dry lab, where our experiments are done only to validate the predictions we make at scale. And if we can achieve that vision, I think we have the potential to be one of the most impactful companies in the world. And so how do we manifest this every day? Well, we have a Recursion mindset that we teach our team.

We have events like Decoding Recursion. I just got back from one last week where we bring new and tenured employees together for a couple of days to talk about how we can focus on the experiment we are here to run. We don’t want to play the game everybody else is playing because we know what the probable outcome is. We want to play a different game. We want to test this idea that there could be a different way to discover and develop medicines and so we push that into every person at Recursion. We even push that into our partnerships, pushing our partners to adopt new tools, to adopt our workflows. And so it’s very front and center at Recursion and we lean into that vision every day. We still have all hands every week at Recursion. I’m still a presenter at all hands as often as I can be.

And we bring together people to really lean into that vision. And we’re not apologetic about it. We believe that somebody has to be trying to make this space not just a little bit better, but a lot better. And we’re thankful not only that Recursion is doing that, but that there are many other TechBio companies and many other companies in the biopharma industry who are making big bets on how the future could look extraordinarily different from how it looks today. All right. Next question from Steve Deckert [ph]. Do you have a rough timeline of when you might submit an IND for Target Epsilon? Thanks, Steve. We’re entering IND-enabling studies at this time. We just advanced that program here in the last week or so. So I think we’ll be able to give you a better timeline for that in the coming quarters.

But I know the team knows that I’m never satisfied and that speed with quality is what we’re aiming for with that program and every other one 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. All right. Next up, we have a question from Steven Ma [ph] who asks, on causal AI modeling with Tempus data, will it be used for internal drug discovery efforts or partners or both? Any change in BD discussions post-Tempus and any economics?

Great. So Steven, the economics are both in the deck here and in our filings. I’ll let you take a look at those just for the sake of time because we’re almost out of time here. What I do want to say, though, is absolutely we will be driving our causal AI models for our own internal programs, as well as for closely partnered programs at Recursion. So for example, in the context of our oncology collaboration with Bayer or our oncology collaboration with Roche & Genentech, we are able to train models on the Tempus data that we deploy for specific programs with those partners. What we’re not is sort of resell the data or memorize the data from Tempus and act as a conduit without a real skin in the game partnership. But when we’ve got a deep, robust partnership like we do with Roche & Genentech or with Bayer, we are absolutely allowed to take those learnings and advance them forward.

And one thing I’ll say, this year we acquired two companies, we built exciting new foundation models, we signed the Tempus deal and we’ve been very upfront with our partners that we intend, whenever possible, to bring all of those updates into our collaborations as fast as possible because we’re incentivized to drive medicines to patients with our partners. All right, last couple 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 [ph], how is Recursion thinking about commercializing its CCM program if the data is very positive? Great question, NK. We’ve got a broad commercialization strategy here at Recursion. We do believe that some of our early programs, if they are successful, could be robust opportunities for us to outlicense, sell, or otherwise partner those programs so that we can bring money back into the company to create a self-sustaining platform. Because unlike many other biopharma companies who are focused on one or two exciting programs, we believe that on average, every program at Recursion should be better than the one before it.

And if we truly believe that, we should be willing to sell or license our early programs if they’re successful in order to subsidize and pay for the next five, the next 10, the next 20 programs that we advance at Recursion and so CCM could be a good candidate for that. Now, over the intermediate or longer term, we’ll have to see how the industry moves. We’ve been generally disappointed with the adoption of some of these technology tools until very, very recently, really until the last 12 months to 18 months, where it feels like the industry is finally starting to get really excited about the potential for ML and AI. And so depending on how fast the industry goes, we may decide one day to actually take our programs forward and commercialize them.

But I can tell you, if we do that, it is very unlikely we will commercialize those programs the way it’s done today. I think we see lots of opportunities and you’re starting to see even larger companies like Lilly doing a direct-to-payer, direct-to-consumer kind of play and I could imagine Recursion focusing on something like a membership model to drive the incentives to physician in favor of the patients and in favor of using all of Recursion ‘s molecules, but we’re really talking about the intermediate- to long-term there. All right. Looks like the team has sent over a final question due to time. If your company was an animal, what animal would it be? This question comes from Johnny Gray [ph]. We’ll end on a funny note. Obviously, our company would be an octopus.

And you can see here when we got our first Phase 2 program, our first patient dosed in our first Phase 2, I promised the company that I would get a tattoo to mark that milestone, which we hope will be the first of many. And the octopus plays a very important internal role of Recursion and I think it’s the perfect animal for us. So thank you, Journey, for that funny last question.

Chris Gibson: 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.