Hi, everybody. My name is Chris Gibson, Co-Founder and CEO of Recursion, delighted to be joining you on our L(earnings) today. And I'm joined by our Chief R&D and Commercial Officer, Najat Khan and the Interim CEO and I hope very soon to be CSO of Recursion, Interim CEO of Exscientia and soon to be CSO of Recursion Dave Hallett. We are coming to you live from Oxford, UK. We are in the Exscientia facility, where behind me, they are using a closed loop automated synthesis platform for chemistry to advance new medicines towards patients and we're just delighted to be sharing the news today that our two businesses have announced a combination. What I would like to do today is walk through first that combination together with Dave and Najat and I'm going to start by talking about some of the complementary factors that we see. First, a pipeline of nearly 10 or approximately 10 readouts over the next 18 months in the clinic. I think this is a really important milestone for a company like Recursion, a company that is trying to prove this next generation of medicines, a new way to discover medicines, and being able to generate this quantity and quality of potential readouts in the coming quarters, I think is going to be really, really fantastic. Next partnerships. Recursion has some incredible partnerships with large companies like Roche, Genentech and Bayer. Our partners at Exscientia have fantastic partnerships with companies like Sanofi and Merck, KGA. And we are just delighted not only for the opportunity to combine our businesses and work against all of these partnerships, but actually to deploy the tools and technologies, the teams that we will be assembling against the partnerships of our counterparties. And I think this deal, in many ways,…

Yes. Thanks, Chris. Look, at the end of the day, we talk a lot about biology, chemistry, automation, tech, et cetera, but it all converges to the medicines we make for patients that are waiting and that's really what this slide is about. So I'll talk about two portions. First is about our internal pipeline, and then also about the external pipeline that we're building and learning with our partners, which you heard Chris mention. So in the first piece you see that there are about 10, as Chris mentioned, clinical and near clinical readouts coming out in the next 18 months. There's breath, and then there's depth. The depth really coming from both of us doubling down in precision oncology, going after both heme and solid tumors, both of which have significant unmet need. And then in terms of other areas where we have depth, rare diseases, different types of rare diseases, significant unmet need, and quite a few of them, quite frankly, with no standard of care that exists today. And then also in infectious diseases, going after areas that have huge unmet need for the aging population, so that's one piece. The second piece I do want to mention is the fact that we have multiple readouts coming out over the next 18 months, the 10 readouts across safety, tolerability, preliminary views, and efficacy. And then the third piece is the external pipeline. As Chris mentioned, this is expanding us from oncology, rare disease, infectious diseases, to immunology. Two sides of the coin, many people would say oncology and immunology in terms of understanding the biology and being able to address it. So we'll speak more about that with some of our large partnerships that we have that are transformational, but I want to mention one other point. The milestones that we'll talk about also are diverse. And what do I mean by that? Some of them are focused on therapeutics and milestones, and others are focused on products such as the biology maps that we recently optioned to [indiscernible].

Thanks, Najat. I think it's important to also mention that beyond this internal pipeline, as Najat mentioned, there is this external partner based pipeline. And as we're sharing today publicly, there are 10 programs that have been optioned by one of our partners. So we really are building a pipeline of pipelines, both internal and external, within multiple therapeutic areas. Dave, for those tuning into our learnings call, they may be less familiar with the pipeline of Exscientia. Certainly, we've become incredibly familiar with it over diligence last few weeks and months, and we're really excited. I want you to tell folks a little bit about what you've been working on, and I know we'll have a chance to dive into your lead program in just a minute.

Thank you, Chris. Thank you, Najat. What I like about this proposed combination is the complementarity as Chris mentioned about the pipeline. Our lead assets have focused in large indication spaces, high unmet needs and broadly in the oncology space, solving design problems that other people have failed to solve. And over the course of the coming next six to 12 months, you'll see significant updates on each of those.

But if I may kind of I'd just like to concentrate on CDK7. Some people may be aware that we recently acquired 100% of commercial rights to this asset. That's because we believe in it so much. This is a truly potential best-in-class compound that operates in kind of a related but broader space to the well-known CDK4/6 inhibitors. This compound is currently recruiting deep into a monotherapy dose escalation study, and I'm hoping to provide an update on the progress of that towards the end of this year.

But if I may kind of I'd just like to concentrate on CDK7. Some people may be aware that we recently acquired 100% of commercial rights to this asset. That's because we believe in it so much. This is a truly potential best-in-class compound that operates in kind of a related but broader space to the well-known CDK4/6 inhibitors. This compound is currently recruiting deep into a monotherapy dose escalation study, and I'm hoping to provide an update on the progress of that towards the end of this year.

Thanks, Dave. I'm very excited about the potential for bringing these two pipelines together. Beyond the pipelines, of course, as we mentioned before, are the partnerships. At Recursion we have a number of partnerships. You can see here our colleagues at Exscientia, a number of extraordinary partnerships. I think some of the most exciting partnerships in this space are technology enabled drug discovery. And as we mentioned earlier, there are these 10 programs that have been optioned. We believe that between the two companies, if this deal is able to be closed, we have the potential to drive roughly $200 million worth of milestones over the next two years or so. And there's the potential between these partnerships, assuming no additional partnerships for more than $20 billion worth of milestones before royalties and I think that is really, really unique. We have had interest from large pharma not only in finding really exciting medicines, but finding medicines at scale. And I think that speaks to the platforms that both companies have built. Najat, I wonder if you want to talk a little bit about how you see the complementarity of the partnerships.

Absolutely. And I want to pick up on something you just said around scale. And as you were talking Chris, we're talking about 10 programs in the clinic, in our internal pipeline, over a dozen in discovery, and 10 in our external pipeline. That is a pretty significant portfolio that we built with our partners and also internally. How do we make it happen? The partnerships are a big part of it. Roche, Genentech, Bayer, those are extremely important partnerships where we are working on new hard targets that can be first-in-class with potential to be first-in-class medicines. But there are two other partnerships I want to note, which I think has a lot of complementarity with Exscientia as well. One is with Nvidia. Need I say more? The need for compute to do everything that we do, whether it's in biology, chemistry, it's complex problems, lots of data. I think there is so much value in terms of what can be done with the work that Exscientia is doing in generative AI, 2D, 3D design, quantum mechanics and so forth. So that's going to play really into our favor to accelerate what we both are doing. multiomic:

Thanks Najat. Dave, can you comment a little bit on how we can use our complementary sort of superpowers at both of these companies in service of the complementary partnerships as well?

Sure. I think one of the many things that really excites me about the journey ahead post close is how on the existing partnerships that Chris and Najat have just mentioned from looking through the lens of Exscientia say our Sanofi collaboration is, how can we leverage the combined capabilities to really accelerate and add further depth to existing collaborations? But looking to the future is just the end-to-end capabilities that the two organizations bring together and looking forward to how we can leverage additional relationships through this combination.

Thanks Dave. So finally, I want to talk a little bit about the platform. We're here in the Milton Park facility where the automated synthesis platform of Exscientia is running behind us, and I've just been so impressed over the last few months and even the last few years as we've gotten to know this team by what they've been building, true, true extraordinary depth in leveraging technology for precision chemistry. I don't think there are many organizations on earth that are better at taking a program from hit to dev candidate, especially in the context of challenging chemistry, where there are perhaps multiple different parameters that have to be optimized against at the same time. And with this new automated synthesis platform that you see behind me, they're able to now integrate automation, robotics into that entire process, and we think, drive down the time, the cost, and increase the probability of success. And what's most important, I think, is this philosophy of design being test and learn that they built here at Exscientia, where not only will we be generating data that can improve the potential medicine in each program, but we'll be generating data that can be used to build algorithms that can understand difficult challenges in chemistry and ADME and tox, and even data that can be used together with the data that Recursion is generating across biology to really start to build these foundation models that have generalizable understanding of biology, of chemistry, of the interaction of those two. And when we put those platforms together, we really have built what we believe is the end-to-end solution. There's more to build, but we don't know of any other company in this space that has focused more on trying to build the full stack solution with…

No, I mean, look, rare diseases, in terms of having any standard of care, these patients, huge unmet need, the standard of care is not what it needs to be and on top of that, the diagnosis, we've seen this across industry. A lot of rare diseases, once there is a therapy viable, the diagnostic rate actually goes up and you start to see a whole shift in that area in terms of other therapeutics that are coming to address the unmet need. So, yes, very, very excited for next month to see the results, primary safety and tolerability, and then also looking at some of the early [indiscernible] as well.

Thanks, Najat. And finally, on our broader pipeline, we've already hinted at this before with the 10 potential programs that are going to be reading out, but we've got detailed information here and on our website around the specific timing of the seven clinical trial readouts that we've already given guidance around here at Recursion. So this is really an exciting day for us. We continue to, I believe, really boldly chase this vision of trying to leverage technology to discover and develop medicines. We've got incredible partnerships, an incredible platform, we've got a fantastic pipeline, and now, I think, a fantastic business combination in the works, and we are very, very excited for the coming quarters. I think with that, we're going to go ahead and transition over to questions which I know are coming in. All right, we've got Scott, who's asking, even though there is no competitive overlap, is there anything to be learned from each other's internal pipelines that can allow you to accelerate the advancements of your programs regarding the Exscientia business combination? You know, Dave, we've talked a lot about this, deploying our tools for each other's programs. Do you want to give your insights here?

Sure. I think, post close, I think one of the things that motivates me is the combination of the data. So we're a learning organization saying that Recursion is. And so every program that we execute on, every piece of data that we bring in house allows our operating system to actually learn and to get better. Imagine the excitement about actually bringing the huge datasets and the competence that Recursion have been generating, particularly the last few years, with not only the pipeline that's visible today, but the pipeline that sits within our partnerships. That's a huge amount of information, a huge amount of data that we can leverage to both benefit current partners, future partners, and also our pipeline as it goes forward.

And maybe if I could just add very specifically, we look at CDK7, there are CDK therapeutics on the market. Let's face it, the response is not the same for all patients. There's resistance mechanisms, so many things that we need to understand from a patient stratification, patient selection perspective. That's where Recursion with the Tempus partnership that we've done already in the last six months, identify novel targets in non-small cell lung cancer and other areas using causal AI and many other algorithms that we've developed. Now think of the merger of the two to say, how do we design these really important programs for CDK7 and other programs in a much more effective way? Precision medicine at the core is being able to predict the right patient, the right therapy for the right patient at the right time. And I think there's a lot we can do to shape the industry, leveraging real programs for our patients in the near and future.

Thanks, Najat. Thank you, Scott, for the question. Let's go to Auror [ph] who asks, following Recursion's acquisition of Cyclica and Valence in 2023, what is the vision to integrate Exscientia and GenAI capabilities in the new company? And Najat, I'm going to turn to you because I know you've spent a lot of time, not only in diligence the last few months or a few weeks, but also working with, it's been a swirl, working with our internal teams on the GenAI vision that we have. Do you want to talk a bit about this?

Yes, absolutely. So in terms of generative AI, especially, let's talk about it in the molecular design space. There's hint to lead this lead optimization. Most of the times we end up getting really challenged in the industry for small molecules around lead optimization, improve potency, this trade-off for some other parameter. So what Exscientia has from using active learning end-to-end to improve the multi parameter optimization, which is the problem that we're trying to solve, we want to be able to integrate that into what we do at Recursion today. Not only that, we want to go earlier, because if you can actually solve the problem and hit to lead with some of the solutions that Dave and his team have developed, that will improve our probabilities of success and hit rates even better. The last point I want to mention, because I get this question all the time, whenever you integrate two platforms, isn't there a lot of integration challenges? This is the beauty of it, where we spend a lot of time in diligence. It's been built in a modular way, which means we can be agnostic to the best models, whether it's inside our two homes or outside, to make sure we have the best range of molecules to our portfolio and our pipeline. So that's some of the ways we're thinking about integrating it. And last thing, some of the phenomics and multiomics data that we have will also benefit Exscientia. And the team has already started looking at ways to integrate that. So we're very, very excited. There's so much work to do and can't wait to get started.

Thanks, Najat. Let's go to Alec, who asks, how do you plan to leverage Exscientia's automated laboratories? Well, it's a great time to answer that question since we're sitting in them right now. So I think this is really, really important. Dave and I had a very long talk about this during the time that we spent in diligence. And I think really this team has done an incredible job of building a state-of-the-art automated synthesis platform. And really the only one that I'm aware of that has integrated this vision of using active learning, machine learning to be able to drive very flexible decision making throughout the process of synthesis, and then through the other side of the physical U-shaped platform, to be able to drive the molecules that come out of the automated synthesis platform into a variety of biochemical assays. This is technology that Recursion has not built. We have built incredible technology that can take a potential small molecule and explore its biological functions across these large scale multiomics data sets. Combining these data sets, we think, just like combining the Tempus dataset with patients, just gives us this extraordinary opportunity to build models that have the potential to learn not across one layer of biology or chemistry, but across many. What I think is important, probably to note is that this facility is now up and running. We believe it should stay up and running. We should build it out from here, and we're going to continue building the biology organization in Salt Lake City. We do believe that the learnings from this platform could be used in the future to help us build a next generation microsynthesis facility that we can tack on to the platform we built in Salt Lake City, where smaller…

If I can just maybe just add, please, just a little. This closed loop design, make test learn platform that's literally sat behind us is so it's designing molecules in the cloud, but not only making the molecules, but in a target centric way, actually generating data against them.

I agree. All right, next up, we're going to go to Moni [ph], who asks, what do you see as the bar for efficacy in the upcoming SYCAMORE readout in CCM? For that, I'm going to turn back to you, Najat.

Sure. Now, in terms of our SYCAMORE study, I mean, primary endpoint is safety and tolerability. So that's something we're going to be watching very, very closely. In terms of the efficacy, we have two different types, I would say categories of endpoints that we're looking into. One is very objective, MRI based endpoints, so for instance, looking at lesion volume and so forth. And then the other is PROs. And these PROs are extremely important to patients. For instance, CCM health index, which is something that was recently developed and many other inputs, so we're going be looking at both of those. And then the third piece I will mention, because, again this is a signal finding, signal seeking study, as we're looking at various different doses, is looking at some of the biomarkers. It's just going be important to understand what's happening to the vasculature, what's happening to the inflammation, and so forth. So those are the three areas that we're going to be focusing.

Thanks, Najat. No, I think that's great. I will say, just from a safety and tolerability perspective, the vast majority of patients in this trial have already rolled into the long-term extension, which gives us a lot of confidence on that side of things. Next, I want to go to a question, I believe, from Paul. A question from Paul who asked sorry, we just, there we go, who asked around the biggest bottleneck in drug development is clinical trial process. And as much as I want to answer this question, but Najat has spent the last six years doing exactly this at her former employer. Najat, please take us through your vision for this part of the platform.

So, Paul, I'm so glad you asked this question. I love this question. Backdrop for everybody watching, like 70% to 80% time cost is actually spent in development. And where does that get spent? Two areas. One is the design of the trial. You have to design it, right. This is where precision medicine comes in. And then the other is the trial execution, which is what you're alluding to, I think, in terms of the trial process, site selection, getting the trials executed. So one of the things that we're working on, in addition to all of this, is building out the AI capabilities and tech capabilities on the clinical development side. So, number one for clinical trial design is really using multimodal data, global data, such as Tempus Helix, but much more in order to be much more effective in terms of how we design our programs, knowing which patients to treat, simulating inclusion-exclusion criteria, so that we don't do what a lot of the industry suffers from, which is many protocol amendments, which leads to time, cost, and yet patients are waiting. The second part is clinical trial operations. I'll give you an early example. For our 4881 program, AXIN1 and APC, we actually used rural data, machine learning and just in time sites. What does that all mean? Basically, it means instead of using the traditional processes today, which is you go to a site and say, how many patients do you think you have that fit this criteria? You actually use all of the claims and real world data to be able to understand where the eligible patient population is. It's anonymized, but you engage with the PIs early, a much more proactive approach. And we were able to recruit that cohort from what would take four to six months to four to six weeks. That is just one small example. But watch for the next few months of using much more of these innovative approaches where we can pull in our recruitment timeline so we can get medicines to patients faster.

Great. And we're going to finish up here with a final question from Marcel, who asks, could you share more on potential or ongoing efforts to use platform for preventative healthcare? Specifically, are there plans to develop drugs or form strategic partnerships aimed at reducing the risk of diseases like cancer or neurodegenerative conditions such as dementia and Alzheimer's? And I think, Marcel, this is a fantastic question and really is part of the vision of what we're building at Recursion. We already have programs that are targeting genetic diseases that are essentially genetic diseases that are predispositions to cancer. That's already in our pipeline. We very deeply believe that there is a huge opportunity to go after areas in neuroscience like neurodegeneration. And while we cannot speak to specific diseases that we could be tackling alongside our fantastic partners at Roche and Genentech, we certainly do agree that that is a really, really important part of the future. And I think what's so compelling about what we're building at Recursion and what we believe we'll be able to build together, is that these maps of biology are not just giving us insights into one pathway or a couple of proteins that are interacting. We are building maps that are showing us the causal model of how biology itself is operating inside many different kinds of cells. We can start to understand this extraordinarily complex interplay of different pathways, the way that different pathways are regulating each other. And my belief, my fundamental belief, the founding belief of Recursion, was that this biology is fundamentally too complex for any human to understand, and that we would have to deploy technology enablement across our entire process to really start to understand the way biology is interacting in truth, not the way we can put it on a whiteboard or put it into a nature paper. And I think the same philosophy holds with our colleagues here at Exscientia. Chemists are incredible. They can do incredible things. But it is very difficult for humans to hold in their head a 40 parameter multi-optimization problem. It's a very difficult thing to do. Technologies like machine learning give you the capability to actually start to simultaneously optimize against dozens or maybe hundreds of parameters, and that's just something humans are not able to do. And I think this similar philosophy on biology, on chemistry coming together, gives this company true, true potential to deliver on the kinds of preventative medicine that you allude to. So, with that, I want to thank everybody for joining our learnings call. So thankful for your team for hosting us here, so excited for the combination that we're putting together. And like I always say, we're 10, 11 years into this and it still feels like the beginning every time. Thanks everybody.

Thank you, so much.