Conference Transcript - RXRX 2026-09-15

Sean Larman, Analyst — Morgan Stanley

Good morning, everyone. I'm Sean Larman, Head of US MidGap Biotech Equity Research at Morgan Stanley, and welcome to our global healthcare conference. Before we commence, I'll make you aware of some important disclosures. For those disclosures, please go to the Morgan Stanley Research Disclosure website at www.morganstanley.com forward slash research disclosures, and if you have any questions, please reach out to your Morgan Stanley sales representative. post that we now welcome Recursion Pharmaceuticals with CEO and President Najat Khan and CFO Ben Taylor welcome to the both of you maybe do me a favour here and answer sort of three macro type questions so just think about the rise of China originated innovation does it change your competitive positioning and your R&D and BD playbook yeah thank you so much Sean

Najat Khan, CEO

for the question and it's great to be here Look, I would say it, in fact, reinforces what we do. I mean, recursion started with the whole concept of better understanding and decoding biology. So a huge amount of focus for what we do is around novel biology. I was, in fact, just recently in Shanghai two weeks ago, and just the speed and the execution and the prowess in that in optimizing known biology was really impressive to see. And it's also reflected by all of the deals that have been done to date. But that next frontier has to be new and novel biology, really first-in-class targets. And that's evidenced by what we have in our clinical program. I mean, REC 4881, REC 1245, we'll talk a little bit more about that, I'm sure, which are both first-in-class targets that came from our engine, novel biology, or novel biology linked to the right indication, which is the case for REC 4881. The other thing I'll also say, we recently announced an unexplored new target that was optioned in neuroscience by Genentech that comes from our proprietary data maps that we've generated. So taken together, that's the premise of what recursion was really founded on around novel biology. And I think it becomes even more important and some could say prescient in terms of what's happening with the optimization and the speed in China as well.

Sean Larman, Analyst — Morgan Stanley

Sure, thank you. I'll skip the next question because it's AI related, but you are an AI-derived company. So, you know, which policy variable is it? Is it FDA, Medicare negotiation, MFN, tariffs, or global pricing, you know, do you think about the most and what matters most to your business on the regulatory front?

Najat Khan, CEO

Yeah, it's a great question, Sean. I think all of the above are, of course, important to take a drug and actually commercialize and ensure this access to patients. For the stage of the company that recursion is, FDA, regulatory, you know, guidance, et cetera, is critically important. and we have a very productive relationship with regulators. I would say in addition to that, as we think about the commercial potential of our assets, of course, the other policies that you mentioned, MFN and others, are also critical.

Sean Larman, Analyst — Morgan Stanley

Sure, thank you. Some questions here on strategy. So you've been elevated to CEO over the last 12 months. You've cut operating expenses materially. A bit of level setting, how should investors view recursion now?

Najat Khan, CEO

Yeah, I know. Nine months, Sean, nine months. Can't take it. I'm just kidding. Here's what I would say. Recursion is a fundamentally different company than 12 to 18 months ago. You know, Recursion has transitioned from a company with a promise in the novel biology platform to a company where the engine, I like to call it, has been expanded to all parts that you need to make a drug. Biology, chemistry, clinical. And to do that, what we've done is we are building the body of evidence in the clinic and beyond in terms of differentiated outputs that are being generated by that engine. I think that is critical. And you're starting to see repeatability across the progress we're making both in our internal pipeline and also in our partnership. And I'll go through that just in a second. And then the third piece that's materially changed, Sean, as you mentioned, is just our level of productivity. We have really leaned into AI agents, automation, as well as concentrating capital where we have the highest conviction. We do that every single day. And therefore, we've reduced our cost base about 40% pro forma post the integration with Excientia. Just to give an example, you know, it's good to say those three pillars, but just to give an example in terms of where we're seeing that body of evidence translating from the engine. Number one is the first-in-class program in our clinic. So REC 4881, first-in-class, or FAP, total addressable market. Just to remind everyone, nothing approved. This is an oral drug. It's over $10 billion. Then we also have multiple programs with one, for instance, REC 1245, RBM 39, first-in-class target identified in our platform, the degrader made on our platform, and should have more data later this year. That's just one. Then we've also made a lot of progress with our partners. We've just crossed over 525 million in upfront and milestones where we're actually advancing first-in-class or intractable biology programs with our partners. These are not isolated events. I think that's really important. understand you're seeing this repeatability across from novel biology novel programs to the clinic and that's all produced by one recursive learning platform that we've been building over a decade and the fact that we're doing that with better productivity as i mentioned 40 less of our cost base i think it's a testament to if you get better at learning you should be able to predict more going forward and be able to validate that in the clinic. So in a nutshell, I will say the company is really transformed to a company with differentiated assets powered by an end-to-end AI engine with repeatability that's coming through both our internal pipeline and our partnership.

Sean Larman, Analyst — Morgan Stanley

Thank you. And how should investors think about the key points of differentiation of your platform? And what do you think investors misunderstand or miss the most?

Najat Khan, CEO

I think what investors haven't fully captured, I would say, is two things. And it's because of this transformation that's happened in the last 12 to 18 months, Sean, as I mentioned. First is actually the inherent value of the assets itself. So I mentioned we have multiple programs, but two that are first-in-class programs. The first one with really high-quality proof-of-concept data already delivered. I think that is underappreciated today because often people are associating recursion with the pipeline from a couple of years ago. So I think that's one. The second, when you look at the progress across the pipeline and the partnerships, like I said, these are not disconnected. There's that body of evidence collectively that's being generated, and I think people are underestimating how much the platform has changed. It's not a phenomics platform. You know, when I first joined Recursion, people would say, oh, yeah, you do some phenomics. Absolutely not. It has expanded from that to multimodal data where we can find novel targets validated by what we just did with Genentec and what we have in the clinic. We can make novel molecules, and we have evidence in the clinic. And I think that's a very different engine than what was around 12 to 18 months ago. And I would always look for that repeatability, because if you have an engine, I want to know that you can do that over and over again. So I think those two pieces are underappreciated, and we have a lot of exciting catalysts coming up to dig into that more.

Sean Larman, Analyst — Morgan Stanley

Awesome. I'll move on to REC 4881 and FAP, but maybe to give some investors tying the platform and the discovery piece together. So how was the platform used to discover 4881, and what are some of the properties of the pipeline candidate that make you most excited?

Najat Khan, CEO

Yeah, so thank you, Sean. This is REC481. This is our first-in-class oral asset in FAP. So just to answer the first question, this came, as I like to call our platform, there's three components, the biology, design, and clinical AI. This came from the biology part of the platform. It has been known for 50 years that FAP is driven by a loss of function in this gene, APC. But nobody had ever studied and nobody had ever made the unobvious insight that a MEK1-2 inhibitor could actually reverse the challenges caused by APC loss of function. That arc was not known. Nobody had tested it clinically. So what we did is we looked at cells, and we knocked out APC. That's the root cause of the disease. And then using our platform, we screened thousands of compounds to say which compound can actually phenotypically reverse. This is a cell morphology. Reverse the cells from disease state to healthy state. This is where we use NML models. And the top of the list of predictions from our foundation models was this asset, which happened to be a MEK1-2 inhibitor. We did not go in with the hypothesis. So think about how you do discovery today. You have a hypothesis and a mechanism, and you go test it out. Here's an unbiased, going from disease to healthy, and that's the mechanism that came up at the top of the list. But this is where it's really important, the last question you asked me. We generated our own data. We have our own data factory in Salt Lake City. Super important. As you think about LLMs, Frontier Labs, everyone's talking about models being commoditized. What's the next frontier? is having your own high-quality data. But the next step that's really important is actually testing that in the labs. So we did our preclinical model. It looked good with that asset. Then we did our healthy volunteer. It looked good. And as Sean just mentioned, we did our proof-of-concept study where we saw rapid and durable polyp burden reduction. Rapid in three months, almost half the polyps are gone. And what was even more remarkable, especially for care wells, is when they're off drug for three months, the effect not only persists but deepens in some patients. Now, remember, FAP is a chronic disease. It starts when you're 10, 11, even younger, and you're living with this disease for the rest of your life. It is the most penetrant GI cancer compared to BRCA1-2 in breast cancer. 50,000 patients, U.S. and EU5. Standard of care today is surgery, surveillance and surgery. Patients have more than 50% to 60% of their GI anatomy removed. Imagine losing that. Your colon is removed, parts of your upper GI is removed, your pancreas, your gallbladder. It's incredibly invasive. So if you can get a disease-modifying drug that can slow that progression of the disease so that you can actually reduce the surgeries, reduce the polypectomies, all of the invasive procedures, that would be incredibly meaningful for patients. And last thing is there is zero approved drugs. So ours, just to summarize, rapid reduction in polyps, which is, And by the way, every polyp is precancerous. That's the root cause of the disease. Durable, even when the patients are off drugs for three months. Oral, which we all know when you get a drug that's oral for patients, it's chronic. And the last thing I didn't mention, which has been a really exciting piece that we're learning more about, is we see polyp burden reduction in the upper GI and in the lower GI. Let me just take a second to answer why that's important. Even after your colon is removed, the polyps keep growing, upper GI and lower GI. Upper GI is where you have your other organs, pancreas, gallbladder, and so forth. It's really challenging because that's where you end up getting the invasive surgeries. And another thing about the anatomy of the upper GI is thin, which means when you remove polyps, the risk of perforation and bleeding is higher. We're the only asset, investigational asset to date, that's shown such rapid, durable polyp reduction and also in the upper GI. So that's really meaningful from an unmet need perspective.

Sean Larman, Analyst — Morgan Stanley

Thank you. You are in active FDA engagement to define a registrational pathway with updated data at CGA IGC and regulatory clarity expected this half. Can you provide more colour on what we should look for in the second half for those updates?

Najat Khan, CEO

Yes, great question. So Sean mentioned there's two important aspects. One is we're sharing additional information at the CGA meeting in early November. This is the Congress, the main Congress for Inherited GI Diseases. So this is essentially our target audience for FAP. So we're doing a presidential plenary to share additional information on our Phase 2 data, just some of the data I mentioned, but more information. So that's number one. Number two, we're also in active conversations with the FDA. Just to remind everyone, we have orphan drug designation, and we have fast-track designation as well. So it's been a very productive set of conversations with regulators. Our base case is essentially what you see ERAPA-like, progression-free survival. What does that entail? These are the meaningful clinical events that you would want to slow down. Surgeries, you know, dysplasia, and so forth. That's what we're focused in our conversations with the FDA, and more to come.

Sean Larman, Analyst — Morgan Stanley

Sure, thank you. There has been a history of toxicity with MEK1-2 inhibitors, So maybe talk us through what you're seeing on the safety profile. Also, can we touch on the durability of treatment raises the possibility of intermittent dosing. So can you talk us through how you think about dosing? Is that potentially part of the registrational package?

Najat Khan, CEO

So two questions. Let's talk about the safety profile. It's a MEK1-2 inhibitor. Over 85% of the AEs, treatment-related AEs we've seen so far, are grade 1-2. The main ones that we see is dermatitis. which is very much in line with what you see with MEK1-2 inhibitors. We've seen some transient CPK changes and so forth. Just to the question Sean asked, there are actually MEK1-2 inhibitors already approved in oncology, but there's also precedence in rare diseases. It's approved in NF1, which is a rare disease for pediatrics and for adults. And from that perspective, I would say what we have seen, this is very much in line, The dermatitis is something that we are also managing proactively with prophylactic solutions, which has really helped. And I would say, even in the real world, most of the CPK has been quite transient-driven LVEF. So it's very well-known in terms of the profile, given both in ONG and also in rare diseases. That's something we take very seriously. The other thing I just forgot to note is ours is a QD, and we will also be looking at intermittent dosing, just given what we have seen with the durability of this drug. Some of the other MEF1-2 inhibitors that exist are primarily BID. So again, if you think about it from a patient perspective, a QD, especially for chronic dosing, I do take something on a QD basis and I can tell you even that, my adherence isn't always the best. So that's a little bit around the safety profile. Your next question was focused on... on the durability of response. Great, yes, on the durability. You know, we don't know exactly why, but we have some hypotheses. You know, in terms of we're doing some preclinical work, you know, when you look at polyps, you know, is there a potential that a MET1-2 is actually evolving both the composition of the polyp as well as the stroma, so the microenvironment of the polyp as well? So there's some translational work we're doing in-house, but I will say what's really interesting think from an MOA perspective for this MEK1-2 inhibitor, it acts via a dual mechanism. One is, of course, the MAP kinase pathway it suppresses. We call this the evolution pathway for polyps because as polyps mature, you don't just get the APC mutation. You get KRAF, BRAF, and additional ones. And the dual part is the second part is it also has crosstalk with beta-catenin. So we think that's the other reason that it's also effective. It has a dual hit in terms of on the polyps. And then some of the work that we're doing in terms of is there potentially you're changing the composition of the cells in the polyp itself and the stromal area. More to come. But look, what we're very excited about in many KOLs, I think this is one of the aspects they found really compelling, is that durability when the patients, not just durability, off drugs. Nobody's tested it off drugs. And in some cases, even deepening.

Sean Larman, Analyst — Morgan Stanley

Sure. Thank you. Moving on to REC 1245 and the broader pipeline. So REC 1245 targets IBM 39, which came out of your maps. How was the molecule discovered?

Najat Khan, CEO

Yeah, the discovery of the molecule came from the same maps that I just mentioned for REC 4881. This is what I mean, two non-obvious insights from the same engine starts to get interesting. And just recently, we had a new target optioned by Genentech from the same type of engine. So this is what I mean, that you start to see the repeatability. So let me just explain for REC 1245. So at Recursion, in Salt Lake City, we have a large data factory where we can manufacture millions to billions to trillions of cells. And we knock out every single gene in those cells, so 20,000. And you can measure not just phenomics, transcriptomics, and then for validation we add proteomics and other assays to the list. Very different from the recursion 12 to 18 months ago. So we're REC 1245. We were looking at CDK12. I'm sure many of you know that CDK12 has been a target of interest for some time for DDR modulation and so forth. But it's been hard to drug because of the similarity for CDK13. So we looked at what are other potential entry points by looking at all of the other genes that we knocked out that have potential shared biology to CDK12. That's when RBM39 came out. That was a non-obvious insight. And the next thing that we did was we rapidly, within 18 months, designed a novel degrader, and then we went into the clinic. And what we have, and by the way, there's two big pathways. I like to call this asset, potentially, multiple doors that you can open. One is the genomically unstable tumors. Think about heavily pretreated patients with solid tumors that are genomically unstable. The other is pediatric, you know, transcriptionally driven tumors, like Ewing, for instance. So the beauty of it is I think there's a potential, and, you know, we'll let the data drive it for us, Monotherapy, if we see increasingly high dependency in RBM39, you can actually put those cells over the edge monotherapy or potentially combo, you know, think about the PARVs, PRMT5. So there's multiple options. That's why the addressable patient population, about 100,000 patients is quite high. But this is a novel target, same engine that I talked about, 4881, same engine that's creating the novel targets for Genentech. And the design platform that made this molecule, same engine as what we're doing with Sanofi where we've gotten five milestones to date, another important one coming up in INI. So that's how we had discovered the target and at that point there was not this connection between RBM39 and CDK12 and the pathway and mechanistic understanding. So it's not just a novel target. Where recursion is going is once you understand the target, you actually get a better understanding of the patient population you'd want to enrich. That's powerful before you dose a single patient. We've been talking about it for the long time, but we're making some good headway there. But we have more data coming on that later this year.

Sean Larman, Analyst — Morgan Stanley

Sure, and you've got other programs, multiple programs advancing. How does the data set look or the news flow look at, say, next 6, 12, and 24 months?

Najat Khan, CEO

Yeah, I mean, we have multiple other programs. For instance, we just initiated our PI3K 1047 program, Days 1. We have IND-enabling programs in ENPP-1, we have LSD. I think the way, if I step back, Sean, I would think about it is we are constantly looking at our programs. I mean, because we have a repeatable engine, we have multiple shots on goal. And like we did last year, we continue to look at the programs and say, where do we have the highest conviction and double down. And we already have some great examples to pick from from the multiple programs that are coming up. All of these programs and some sort of catalyst coming up in the next year.

Sean Larman, Analyst — Morgan Stanley

Sure, thank you. Moving on to the partnership, so Genentech has advanced the first neuroscience target from the collaboration, previously unexplored target, into joint small molecule discovery. What does that option mean economically, and what triggers the next one?

Najat Khan, CEO

Yeah, I mean, I'll start, you know, Ben, if you want to add to this as well. I think for us, as Sean mentioned, our partnership with Genentech is focused in neuroscience and in GI oncology, where it starts by generating these novel maps of biology, which Genentec is option two already for about 30 million each. And then the next step is how can we take this rich, novel, proprietary data, predict which targets might be causal, and then validate them back in the lab. The first of those was actually just accepted, as Sean mentioned, by Genentec. One thing to note for those of you who are watching whether the scaling laws of biology and AI will make a difference, this is one of the first examples of that, that you can actually find non-obvious biology that is causal from data that nobody else has in the world. But Ben, I don't know if you want to talk about totality of what we've brought in and also to Sean's point what this is. Absolutely.

Ben Taylor, CFO

So, as you mentioned earlier, we've brought in 525 million in upruns and milestones from our partnerships. We've hit over a dozen milestones. This was another key one because it was also advancing the biology side of the platform. What we're doing now is moving beyond the maps and actually translating it into a pipeline of neuroscience programs that are all based on completely novel targets. So what we've looked at now is the first milestone coming in for that target validation. We'll move through small molecule validation. It's important to remember we run all of our partnership business to be at least break-even or profitable from the beginning. And so what we're trying to do is really use it not only to build our platform, but also build a lot of MPV value where we continue to expand the pipeline using the scale and the technologies that we have and then have a really nice, profitable back end to all of those programs.

Najat Khan, CEO

So maybe just two things to note. One is for each program, the milestones are about $300 million with single-digit royalties for Roche. And not only has the target been optioned, but we're actually working on developing the small molecule for the target. So the journey continues beyond optioning the target. The second thing is we expect potential for more targets from the same data set. Now, remember, if you think about the Internet corpus of data, that really was available, and that's what was used to build a lot of these LLM-based companies. Guess what? That doesn't exist in biology. That doesn't exist in science. This is why having a data factory is so differentiated for recursion and not just having a data factory, but having the wet and dry lab connected so you can validate and see if the stuff is causal and then actually design the molecules and take it to the clinic. So the fact that we have connected it end-to-end and then we have the data factory means these maps are reusable. That's the point I'm trying to make. It's a durable asset because you're mining it over and over again to find novel targets and expect to see more of that.

Sean Larman, Analyst — Morgan Stanley

Sure, sure. Maybe just to clarify my mind, so the neuroscience maps used whole genome CRISPR knockouts in iPSC-derived neurons. Is that data set reusable across other partners or is it exclusive to Roche?

Najat Khan, CEO

It's exclusive to Roche. We have a 10-year exclusive neuroscience partnership with Roche. I mean, kudos to Roche, and they're phenomenal partners, just like Sanofi. These are not, we don't do volume partnerships. We do very integrated partnerships because, again, we're doing things no one has ever done before. It's not a rinse and repeat play. This is a complete frontier play. And so, yes, it's exclusive with Roche.

Ben Taylor, CFO

I think an important note on that. So Roosh has paid us over $215 million so far for the program, but we actually own all of the data. So through that partnership, they only have access to the work product. To Najat's point, we do have another five years on the partnership and hope to continue it. They're a fantastic neuroscience partner, but we are actually keeping the in-house capabilities and data that come from it.

Sean Larman, Analyst — Morgan Stanley

Awesome.

Ben Taylor, CFO

Thank you, Ben.

Sean Larman, Analyst — Morgan Stanley

You notched a fifth Sanofi milestone. how should we think about the Sanofi collaboration going forward and what are you focused on in that collaboration?

Najat Khan, CEO

Yeah, so you know the Roche Genentech Partnership, which is focused on novel biology and now moving into programs? For Sanofi, it's focused on developing molecules, small molecules, for intractable INI and or oncology targets. So, Sean, as you mentioned, the five milestones we've achieved so far, It's for lead series for making those compounds against those intractable and first-in-class INI and oncology targets. The next one that we've given guidance, potential one that we've given guidance for, would be a milestone and development candidate for a first-in-class oral small molecule program. That would be the most mature one. I think that, as a standalone from a value proposition, is a highly valuable asset, but there's also a read-through in terms of the design part of our platform. Just remind everyone, we don't design everything small molecule degraders. That's what we are focused on because we think that the activation energy to figure that out is pretty high. So that's why the competitiveness is high. And if that is optioned development candidate by Sanofi, which was given guidance for, that would then enter their pipeline. And this is what Ben was saying. then it becomes we don't have operating costs and we start to recoup the milestones. Again, just to reiterate, the milestones for Sanofi are over $300 million and the team-level royalties as well. So these are meaningful milestones. But I want to underscore, for a company like us, as we're building our engine, I keep talking about everything you've heard about, the Roche targets, the clinical drugs that we have in our pipeline, the Sanofi partnership. It's all coming from the same engine. And the recursive learning from the engine is incredibly important. So these partners have been phenomenally helpful for us to not just have cool data, but to actually learn how to turn the platform into an AI-native product or asset engine.

Sean Larman, Analyst — Morgan Stanley

Sure. Thank you. Maybe on to one of my favorite topics, the platform and the AI question. Yeah, so you cite roughly 330 compounds per development candidate against 2,500 industry-wide, and the target to candidate in one and a half years, there seems to be versus four, there seems to be consistency in the industry on that metric. But, you know, that's a productivity claim. Can you walk us through the evidence that the molecules are better rather than just cheaper?

Najat Khan, CEO

Yeah, I mean, look, quality matters the most, and then faster is definitely a nice couple. Here's what I would say, what we just talked about right now. I mean, we have brought in over 525 million in upfront and milestones. 125 million of that is in milestones. I think we have one of the highest milestones achieved for any of these AI-native drug discovery and development companies. That matters because upfront is promise, milestones is true. So it's the quality of the molecules, for instance, with Sanofi, for some of the other work that we're doing, that's a signal. The other thing I'd also say is the work that we have in our programs that we already talked about. So this is what I mean. Like, look, we're learning as we go, but we're building this body of evidence across biology, chemistry, and the clinic. And just to reiterate the speed part of it, Speed, this is important because if the thesis is that you can have high-quality data that generates better and more performant predictions, then you have to validate only a few to get to the right answer. We believe that's true. You see that in every other industry. Our timelines are about 3x faster for small molecules, which are hard, than industry, and we are physically making about 90% less molecules physically. Because the ones that we're physically making are closer to the end point of the desired state. I think that's a velocity in the engine that I would, again, by itself, you don't claim victory. At the end of the day, the drugs have to work in the clinic. But I always say these are green shoots to watch. Anything else you want to add?

Ben Taylor, CFO

The only thing I'd say is one of the really important elements is even though we use AI, we actually create physical things. And so you can take all of these chemistries into the lab and test them against other chemistries that have been developed for the same targets. So there's a very quantitative analysis that you can do to say, is this exceeding what has traditionally been done or not? And so we've had more than a dozen different development candidates, as Najat mentioned. And, you know, we've had external partners that have been validating it. We're seeing really powerful output from the design platform as well as the biology platform.

Najat Khan, CEO

And, you know, I will say, just like, I mean, everybody is using some sort of LLM. It gets better the more you use it. That recursive learning is actually really important. So do we get everything right? I mean, but look at our success rate in industry, 10%. You get to 20, you double it, right? So that recursive learning, it would be hard to do that if you just do novel target biology. You have to make the compound, and then you have to go in the clinic, and you have to learn from that really fast. And I think that's why our focus is do all of that to unlock a new TAM. That's why the first-in-class programs matter, whether it's our own pipeline or with Sanofi or with Roche. And do it fast so you can learn fast, right? You can start things, double down on things that work, and what doesn't work, learn from it really fast. That's how we're trying to collapse that engine and the timeline. But the reason is to improve that recursive learning.

Sean Larman, Analyst — Morgan Stanley

Sure, thank you. The modus described as the data factory, the lab in the loop, and the differentiated pipeline. You know, frontier models, as you just mentioned, keep improving and available to everyone. Which of those three is the hardest to replicate?

Najat Khan, CEO

Look, I think the hardest to replicate is actually integrating it together and having an end-to-end engine. There are so many steps that go into, like, biology all the way to the patient and showing data. I think unless you learn fast, you're not going to bend that probability curve that we all want to bend. But in order to do that, Sean, I would say the models are great. Model architecture is important. But unless you have the right data to teach it, and then the predictions go back into the physical world to experiment and learn from it, what's working versus not, that's, I mean, I call it almost the patient in the loop. Because it's not even just the lab, because we're going all the way to the clinic. But the data factory is critical, especially in this space, because unlike some of LLM and others, LLMs are not going to be sufficient for science. You do need other modalities of data, and, you know, we have proprietary protocols for the cells that we're making. We make a trillion cells, IPSC-derived neuronal cells. I used to be in lab, didn't even want to make a hundred. I mean, it's super hard. I used to just work with Gila and toy cells. These were so much easier. The level of sophistication in our science data factory, the amount of automation that it takes, we've been at it for 10 years. And there's something to be said about that because you really become much more mature and seasoned on what works or does not. And then now we have the output to show for it, not just data sets, but actually novel targets being optioned by really high-quality research organizations.

Sean Larman, Analyst — Morgan Stanley

Well, thank you, Najab. We've just run out of time, So thank you, Najat. I appreciate you participating.

Najat Khan, CEO

Thank you, Charles.