Thank you, Chris. And just the example that you mentioned in terms of microglia is such a phenomenal example of how our platform and the combination of the wet lab and the depth of the wet lab approaches that we have at Recursion with, of course, our dry lab really creates something of unique value. So if you go to the next slide, we shared this slide before. And I just wanted to spend a little bit of time on it today and double-click on a couple of areas. So first of all, this represents the heart of how Recursion operates. You hear a lot about the Recursion OS. And I love to kind of pull the hood and like really show what are the various components that we're focusing on here. Step one, we're applying AI where it matters, where it can truly change either quality, speed or the impact of our decision-making. There are 3 specific modules here. The first is focused on deep biological understanding that's actually connected to patient outcomes. The second, how do we leverage AI to design better molecules that are more drug-like. And then the third is a ClinTech approach on picking the right patients as well as recruitment -- fast recruitment so we can get through our trials faster. All right. Having said that, I wanted to double-click on a couple of areas that we're really focusing on. Next slide. Scientific agents. So we've heard a lot about agentic agents. One of the most exciting parts of the OS is how we are using scientific agents, AI systems that actively participate in the entirety of the scientific process. And these agents are helping us thinking about the data that Chris just mentioned, genomics, transcriptomics, real-world data with partners like Tempus and others public data sets like PubMed, [ JetMa ] and so much the list goes on and on. And we have early proof-of-concept agents that we're leveraging in order to really not just analyze and interpret the data real time, but to actually select the optimal tools, workflows, generate hypothesis and design new experiment. This is going to supercharge our already extraordinary talent. The other reason I'd like to mention this is also around it captures the decision-making trail. That's really important. The way you get these agents to be highly effective is to actually understand the logic behind the recommendations and iterate in real time with our scientists and our clinicians. And that is something that we're doing. And having that inherent data, this platform helps us do it in action, not just theoretically. I often get the question in terms of how do we drive economies of scale. We are a tech bio company. This is going to be one of the very important levers for us as to how do we do more given a lot of the insights that we're generating with what we have today. So just keep an eye on this, but I wanted to double-click on a really important area of progress for us that is truly applied to what we need to do to create programs that are differentiated. The second area, if we go to the -- one more click, I will do it, is really around automated ADMET. Look, we talked a lot before around [ Bolts 2 ] and other programs that really help you understand binding affinity and so forth. But as we all know, in order to actually design programs, a critical element of it is to ensure that they are drug-like. So this is an area that is critical for us. And what we are doing, this is the automated platform that we have in Salt Lake City, combining high-throughput experimental automation with advanced machine learning. It's a fully automated closed-loop system that integrates ADMET property predictions directly into that middle module that we have, which is our AI-enabled precision design. So it does a couple of things. Number one, we are generating proprietary data around ADMET, not just what's been published, but all of the successes and failures that we see as we are generating our own data. Failures are incredibly important to design better models. Second, the comprehensiveness of the ADMET properties over 50 or so is just a starting point, and that's only going to increase is important in order to ensure the algorithms are actually generalizable. And then the third, you've heard about models such as [ Mole GPS ], and there's new models that are coming up all the time. But these data feed directly into the model, which is actually iterating and helping our models to be retrained real time. So both examples of real-time iteration is critical for us to not just have a platform that's useful, but a platform that stays ahead of the curve and learns from both our successes and our mistakes. So with that, I want to walk to the next part, which is how is the platform being leveraged to actually generate programs. And this actually slide came to my mind during the flight post popular demand from analyst questions and investor questions. Here's what you have. I'll just walk you through it. On rows, the 3 design components that I just mentioned, the biology, the chemistry and the clinical development that you saw on the last slide, everything from phenomics, transcriptomes, et cetera. And as I do the build for the columns, these are the various iterations generations of our platform. So just to give you a clear view on which programs using what component of our platform. So it's important to note, as you'll see, the earliest programs built on our first-generation platform, which I will call V0.1, and we've shared that before, the programs that are now in the clinic, tangible proof that we are generating programs and also learning fast, the flywheel with every turn of the crank, we're learning really, really fast as to how to improve on what we're doing well and what we need to do better. So the first one here, MEK1/2, more data coming in December. I'll share a little bit more of our plan there. This is a proof point around how are we leveraging genomics, an unbiased approach to drug discovery in order to really ascertain which compound, which mechanism, which we do not know going in, could actually help attenuate the hallmark of the disease, which is polyps, hundreds to thousands of polyps. More to come on that in a second. Now often, I get the question that if this is part of platform 0.1 is that it? The story never ends there. As you will see with ClinTech, if you go to the third row, we are actually leveraging recruitment solutions as well as patient selection and stratification. So even programs that are in the early part of the platform, we're leveraging some of our recent components in CinTech to add more value creation. The second iteration of our platform, I’ll call this is V1.0, we're beginning to combine genomics and biology as well as chemistry design as well. So examples such as RBM39, CDK7, et cetera, and of course, components of the CinTech platform. And then one more click. These are programs in discovery, which I hope to be able to -- we hope to be able to talk about soon that, as you can see, is incorporating all of the various components of the platform from discovery, biology, novel insights, chemistry and CinTech and not just for our wholly owned programs, but also for our partner programs. So more to come on that. So where are we making progress? You've seen this slide before. I'd like to update it every time that we meet. So first of all, CDK7 combination cohorts have been initiated. I'll speak a little bit more in a moment in terms of some of the analysis and some of the data that we have from the monotherapy dose escalation. PI3K 1047R, the development candidate has been nominated, which was again one of our milestones for this year. And MEK1/2, we'll have a webinar in December in order to share some of the additional data from our 4 milligram cohort. As you also heard from Chris, the $30 million option milestone received for the microglia map. This is in addition to the 6 Phenomaps and also some of the programs that we're generating and great traction across the other programs and partnerships, including Sanofi. I just want to pause and take a moment to say both of our internal and our partner programs are critical, critical to us delivering tangible proof as well as also learning fast to keep evolving our platform. All right. So I'll take a moment to go through CDK7. So just a quick context, we've talked about this before. CDK7, really important master regulator, transcriptional kinase that has generated significant interest in oncology for some time, but has been plagued with historical challenges. And the main -- one of the main reasons for those challenges is the narrow therapeutic window and just the molecular properties that limited tolerability and efficacy. So what are we doing leveraging our platform that's different? Number one, leveraging our platform, we set out to design a molecule that directly addresses one of these core limitations around therapeutic index, which is optimizing permeability and [ eflux ] so that we reduce the GI-related toxin variability that others have seen before. That's number one. Number two, we are also leveraging preclinical data as well as multimodal real-world data, causal AI, all of those components of our platform in order to hone in on patients that might most benefit, how do we steer a product into the right patients, patient stratification. That's another area of differentiation that we're focused on. And of course, from preclinical models, we've seen tumor regression in both ovarian and breast cancer. We also shared some early data last year, early clinical data from last year that showed manageable safety profile as well as some partial response as well. What is our next focus? So in terms of our next focus, next slide. We have completed our Phase 1 dose escalation. So MTD has been achieved in advanced solid tumors. I'll get to that in a second. In addition to that, concurrently, the team is also looking into alternate dosing schedule just to figure out ways to even further optimize the therapeutic index, especially for long-term dosing. The other areas of near-term focus for us, which is already well underway, is a Phase 2 dose expansion in the cohort that we had mentioned, the platinum-resistant ovarian cancer as well as combination, which is going to be key in this space with a couple of combination standard of care that you can see on the slide. Recruitment ongoing across all of these cohorts. The other thing that's important to note is a lot of the trial design is focused on rapid and efficient go/no-go. And we'll share more in terms of the Phase 1 dose escalation data at a medical congress next year. And then in addition to that, we should have some of the ovarian combination data in 2027, safety, PK/PD, maybe early signs of efficacy, more to come in next year as we see the recruitment shaping up in terms of details on when in 2027. So stay tuned. All right. So let's go into the monotherapy dose escalation. So a couple of things to note. As of September 29, which is the cutoff date, we have 29 heavily pretreated patients with advanced solid tumors that have received 617 across 6 dose levels. Just as context, these patients represent a rather challenging population, most with multiple prior lines of therapy and limited standard options. We have now established a 10-milligram once-daily MTD, maximum tolerated dose with a manageable safety profile, we'll talk about in a second and also preliminary antitumor activity that's consistent with what we shared in the 2024 update. The most common dose-limiting toxicities were nausea, which is to be expected and some thrombocytopenia, which are both on-target effects for this target class. If you go into safety. Look, the safety data is consistent with what we saw last year. First of all, we have about 30% of patients that experienced Grade 3 treatment-related adverse events, and the majority were low grade 1 or 2. There were no grade 4 or 5 treatment-related events and only 2 patients, about 7% discontinued due to an AE. One thing I want to note here, and again, this is early, we're learning more, of course, the GI-related toxicities that we have seen, diarrhea, nausea, vomiting were relatively manageable and in line with class expectations. And to just put that in context a little bit more. Okay. To put that in context a little bit more, in terms of diarrhea, we saw about 69%, nausea 41%, vomiting 28%. Of course, looking at some of our peers also in the space, the numbers for diarrhea are about 82%, 77% for nausea and vomiting for 80%. So it's trending in the right direction, but of course, much more work to be done, but trending to be slightly lower than what we have seen in other prior published data. All right. In terms of efficacy, first of all, on the left-hand side, this highlights the PK profile. highly selective inhibitor potent and also flexibility in terms of how we can dose it given the short half-life -- the relatively short half-life of around 5 hours. One thing that's important to note, so going back to the established MTD, which is 10-milligram once daily dose, the exposures exceed, as you can see, the CDK7 IC80 while remaining below CDK2 IC80, supporting the selective inhibition that we wanted to see. In preclinical models -- what does that mean? In preclinical models, 10-milligram equivalent QD showed robust tumor regressions with about 2 to 4 hours to target coverage. And the early PD data that we have indicates that this is about 80% to 90% of transient [ POLR 2A ] engagement, which is one of the key PD markers that we are tracking in this space, again, consistent with that hypothesis. So from all of the data that we've seen so far, 10-milligram QD is pharmacologically active dose. In addition to that, we're also looking into alternate intermittent schedules such as second day on off to further maximize the dose intensity while maintaining tolerability for long-term dosing. On the right-hand side, you're also seeing some of the early clinical translation. Look at 10 milligram is where we see stable dose and also the patient that had the PR. Of course, in this patient cohort, monotherapy is not an area that we were expecting outcomes and which is consistent with what we've seen with other CDK inhibitors, which is why our combination is going to be incredibly important. So stay tuned, more to come on that. And last thing, speaking about the combination, I've shared this -- we've shared this data before, but ovarian cancer is the current area of focus, which is different from where others have gone, which has been much more primarily in breast cancer or a broad basket of solid tumors. pulling together everything that we have seen in cell lines on the left, in terms of ovarian cancer models, the sensitivity to CDK inhibition, combining that with what we have seen in vivo at 10-milligram dose, we saw complete tumor regression by day 27 as well as on the right, leveraging our patient level data from over 30,000 ovarian cancer samples, integrating DNA, RNA and clinical outcomes to really showcase that CDK7 is a likely driver of poor survival and some of the work we've done with our causal and friends and AI works. So -- but again, the proof is always in the pudding. So much more to be done and expect more on the full data set I just mentioned for the monotherapy dose escalation next year at a medical congress as well as combination data in 2027. We'll give you narrow guidance or more specific guidance as we get full flow into our recruitment. All right. And just wanted to heads up on REC-4881, which is our other program that has an important readout coming out next month. So just as context, high unmet need, 50,000 patients diagnosed across U.S., EU5, rare inherited disorder, APC loss of function. And standard of care is quite challenging. Surgery is a standard of care, [ colectomies ], et cetera, and no approved therapies to date. We also have orphan drug designation for this compound. Just a recap of some of the earlier data that we've seen in May that was shared in DDW, 43% median reduction in total poly burden, that is the hallmark standard of care today, off-label use of celecoxib is usually 20% or so or 25%. But again, there is a range from 30% to almost to actually 83% in the small cohort that we had seen in May. We've seen about 6 patients. We expect that to be double or close to double by the end of this year. And what we're looking for, again, as I mentioned before, is to see if these trends will hold and a significant benefit over the 20% that has been seen so far. In terms of what we're seeing from a treatment-related AEs, 19% grade 3, majority is rash and the prophylactic approach has really made it much more manageable and cardiac tox more grade 2 so far. So again, December, we'll share more information in the coming weeks, exactly when in December. We'll have the Phase 1b/2 update. This is going to be an important update, as I mentioned, and then we'll also discuss some of the next steps for the program. If the trends hold, one of the core next steps will be to actually have discussions with regulators on a pivotal study. And I just want to say this is one of those, as I like to call green shoots in terms of leveraging our platform to see color burden reduction, both in vivo and then also starting to see in patients. But again, small data set, more to come. We're looking forward to the data cut in December. With that, I'm going to hand it over to Ben for our financial update.