Hello everyone. My name is Brent, and I will be your conference operator today. At this time, I would like to welcome everyone to Exscientia's business update call for the third quarter of 2022. All lines have been placed on mute to prevent any background noise. After the speakers' remarks, there will be a question-and-answer session. If you would like to ask a question at that time, simply press star followed by the number one on your telephone keypad. If you would like to withdraw your question, again, press star one. Thank you. At this time, I'd like to introduce Sara Sherman, Vice President of Investor Relations. Sara, you may begin. Thank you, operator. A press release and 6-K were issued this morning with our third quarter 2022 financial results and business update. These documents can be found on our website at www.investors.exscientia.ai, along with the presentation for today's webcast. Before we begin, I'd like to remind you that we may make forward-looking statements on our call. These may include statements about our projected growth, revenue, business models, preclinical and clinical results, and business performance. Actual results may differ materially from those indicated by these statements. Unless required by law, Exscientia does not undertake any obligation to update these statements regarding the future or to confirm these statements in relation to actual results. On today's call, I'm joined by Andrew Hopkins, Chief Executive Officer, and Ben Taylor, CFO and Chief Strategy Officer. David Hallett, Chief Operating Officer, Michael Krams, Chief Quantitative Medicine Officer, and Garry Pairaudeau, Chief Technology Officer, will also be available for the Q&A session. We're excited to update you today on our recent progress. We believe that our patient-first AI strategy, in combination with our world-class team and innovative platforms, places us in a position of strength as we could capitalize. With $625 million in cash at the end of the quarter, this provides us with several years runway to advance our near-term programs, while we also invest in tandem for long-term growth. Within our platform and pipeline developments, we have made meaningful progress throughout the year, which has continued to validate the technology that Exscientia stands on. Today, we provide more insights as we get closer to treating the right patients with the right drug in a clinical setting. We are also excited to highlight data demonstrating how we plan to innovate and increase the probability of success in the clinic in a similar way to what we've already achieved in discovery. Following our top-line data early this year for EXS21546, our A2A receptor antagonist, we remain on track to initiate a phase Ib/II study by the end of the year. Importantly, our A2A program not only learns about the compound but also allows us to continue to build our confidence in our translational signature on how best to enrich our patients who are more likely to benefit from this treatment. We have also made exciting progress with GTA-EXS617, our CDK7 inhibitor. New data we presented at the EORTC-NCI-AACR Annual Symposium in October demonstrated the potential of our precision oncology platform to assess need to respond to our therapy. Data also highlights the potential of 617 to be less cytotoxic compared to other therapies such as CDK4/6 inhibitors. We believe that our platform, which combines deep learning, functional models, and multimodal omics data, not only helps us better understand the potency and activity of 617 better patient outcomes. We are on track to file a clinical trial application or CTA for this program by the end of 2022 and expect to initiate a phase I clinical trial. In our partner programs, we are adding an additional oncology target into our Sanofi collaboration. More generally, we look forward to sharing more details on our pipeline, including additional drug candidates progressing through IND-enabling studies. We recently shared a couple of exciting announcements, including a new strategic collaboration with The University of Texas MD Anderson Cancer Center to develop small molecule therapies in oncology and our expansion into biologics design. We will talk more about these shortly. As our clinical pipeline grows, I'll now hand over to Ben to walk you through how we plan to be as innovative in development as we have been in discovery. Thank you, Andrew. Looking at the life cycle of a drug, all of its future potential is determined during the initial design phase. However, the validation of that potential only occurs during clinical testing. Our strategic goal is to shift the economic curve of the pharmaceutical industry by improving probability of success, accelerating development cycles, and reducing costs. As you will see, nearly all of the tools that we use for drug discovery also apply to more effective clinical design and execution. I'm going to spend a minute covering our clinical strategy before Andrew discusses recent advancements in precision medicine, but it's important to understand that the two are closely linked. When we use real patient samples in our precision medicine platform to guide molecular design, we are also defining our future clinical programs. The phenotypic, genotypic, and transcriptional signatures that we identify with our development candidates are the same profiles that we will target in our clinical programs. We have seen this be very effective in single gene loss of function mutations, and our goal is to apply that same targeting strategy to more complex biology. We have given previous guidance that we expect to initiate the next phase of our A2A molecule, 546, by year-end, and our CDK7 compound, 617, will follow shortly thereafter. Looking forward, we expect that we'll have at least three compounds that are in clinical development by 2023 and four by 2024. This only includes compounds where we maintain significant economic interest and excludes the existing clinical programs with Daiichi Sankyo and Sumitomo Pharma. On slide seven, you will see how the strategy translates into practice. When we launch the trials for 546 and 617, we'll provide you details on their individual trial designs. This slide outlines what you can expect for all of our internal oncology programs. We start with what we call ex vivo clinical trials using our precision medicine platform. We evaluate the drug on a multitude of heterogeneous patient samples with in-depth profiling to understand why some of the samples respond better than others. This provides us with a biomarker that we believe will correspond with the most sensitive patients for a specific drug. We can also perform multiple iterations of these prospective ex vivo trials to create a statistically significant sample set before initiating an expensive and lengthy in vivo clinical trial. Next, we start with an initial response signature. While the trial is validating the compound characteristics, including safety and preliminary efficacy, we are also evaluating all of the responses prospectively with our precision medicine platform to validate the biomarker. Once confidence in our biomarker signature has been reached at a predefined level, we can apply that biomarker signature to directly enrich the patient population in confirmatory trials. All of this is with a specific goal of increasing the probability of success in clinical development. We believe that many clinical trials fail because they are not treating the right patients. Precision medicine needs to be incorporated into clinical progression so that you not only define a drug's properties but also who should receive it. In addition, we want to make the trial design as efficient as possible. In our industry, we often see novel products tested using clinical methods that are decades old, even when newer methods have been proven more efficient and accepted by regulators. Our Chief Quantitative Medicine Officer, Dr. Mike Krams, oversaw clinical innovation for all of J&J's portfolio and now makes sure that we are adopting state-of-the-art clinical strategy. For example, we utilize simulation-guided clinical trial design to virtually identify the best statistical models as well as critical variables that may influence the success of a trial. Another way for us to advance our clinical capabilities is to partner with a world leader in clinical sciences. The University of Texas MD Anderson has not only pioneered many of the adaptive clinical trials that now define intelligent trial design. With this partnership, we'll leverage their extensive experience in oncology and clinical trial execution as well as the next generation of innovative clinical design about our progress with patient-centered precision medicine. Thanks, Ben. We'll now take a few minutes to walk through two of our ongoing programs and how we are doing a step-by-step trials, highlighting important work for our 546, our A2A receptor antagonist, and 617, our CDK7 inhibitor. Now on to slide 10. For background, we know that other A2A candidates in clinical development have seen some, albeit limited, clinical success with overall low response rates. We believe that clinical success can be improved through enriching and targeting for the right patients. The key to success in the A2A field, we believe, is matching the drug to the right patients. We now have identified a preliminary gene transcript signature measuring ex vivo adenosine burden in primary samples, which we believe could be predictive for 546 response. Our 546 response signature, or Adenosine Burden Score, which we refer to as ABS. In the heat map here, you can see ABS in 43 sample patients from six different tumor types. The signature is made up of six genes, shown on the side, and two normalized genes, which are not shown. This gene signature was initially identified through differential expression of genes in primary mononuclear immune cells after ex vivo perturbation with stabilized adenosine. The ABS is currently set relative to biomarker levels in unexposed healthy immune cells. The more we increase adenosine, seen in the yellow box here, the more adenosine-driven immune suppression there is, and thus the potential for 546 to have a real effect on blocking adenosine detection. We see that a number of samples across tumor types have high scores, and we believe this signature is likely applicable across several cancer types as it is based on the immune system. The next two slides will demonstrate how we modulated ABS ex vivo and how it relates to a validated Tumor Inflammation Signature. This work also reveals potential PD biomarkers beyond pCREB. We are working to confirm those. This is really important and differentiating how to think about designing clinical trials to maximize the probability of success for these patients. Here on slide 11, on the left, we can see here that our ABS behaves more consistently and as expected in the presence of stabilized adenosine than the adenosine signature from Fong et al., published in Cancer Discovery in 2020. The score can be seen here from transcriptomic studies conducted on four primary mononuclear samples stimulated with stabilized adenosine in the presence of T-cell activation. As we increase ex vivo stabilized adenosine and take away EXS21546, there is a progressive increase in our adenosine signature score, as shown in the orange box. On the very same data set, our signature outperforms other published signatures in terms of specificity and sensitivity to detect adenosine-rich microenvironments. On the right, we show that signature allows the detection of adenosine-rich microenvironments as presented with primary lung and RCC cancer patient samples exposed to increasing doses of stabilized adenosine. We believe that this ultimately shows that the immune response can be controlled by treating primary patient samples with EXS21546, as confirmed in the T-cell activated blood mononuclear cells. We are continuing to collect adenosine and EXS21546 specific data and look forward to sharing more about how we expect to enrich for patients in the clinic. The adenosine gene signature that we have developed will be further validated alongside our planned phase Ib/II study starting soon. To take us further on slide 12, as we move towards validation, we've identified a relationship between our adenosine signature and important published score for inflammation and checkpoint immune response. We see that as we increase adenosine, we are also decreasing the TIS, or Tumor Inflammation Signature. From the chart, we show how we can increase confidence in the score by mining public data sets such as TCGA. For background, we already know from published research that adenosine is an alternative way by which tumors can escape the immune system even if treated with an anti-PD-1. Indeed, we find that the most predicted high adenosine cases are among patients that are least likely to respond to current immunotherapy according to the TIS. This helps us understand potential responders to treatment with EXS21546 in combination with checkpoint inhibition. Our hypothesis is that disease progression under PD-1 therapy may be associated with local immune suppression mediated by higher density levels in the tumor microenvironment for a significant proportion of patients, as highlighted by these specific tumor types. This idea is further sustained by the data presented here on slide 13. In our lung cancer patient tissue cohort exposed to increasing doses of adenosine, we can observe a trend for TIS reduction on the same samples, notably of highest doses of adenosine, underlining further the potential connection between adenosine-rich environment and checkpoint inhibitor responses. Ultimately, this reveals potential implications for the clinic in terms of studying 546 in combination with a checkpoint inhibitor. Bringing it back to the example of 546 we mentioned above, we have seen other signatures are inaccurate, which means that it's very challenging to identify which patients may respond to therapy. This has been evident from data observed from current clinical candidates. The data we've just shown highlights a highly stable biomarker behavior, and our platform performs this ex vivo. We're taking a similar approach with 617, our CDK7 inhibitor, as we prepare for the clinic. On to slide 14. As mentioned earlier, we recently presented data at the EHA meeting of our functional precision medicine platform, combining single-cell sequencing and transcriptomics of disease-relevant primary patient material to maximize understanding of the potential 617 effect, aiming to select patients we believe will be most likely to benefit from treatment. For the first time, we're showing how we apply multi-modal machine learning to integrate data from primary human tissue samples and multi-omic sequencing capabilities to predict tumor efficacy of 617. Here we highlight some of that important data, including data shown that 617 induces less cell death of immune cells than other investigational CDK7 inhibitors, potentially indicating a differentiated clinical safety profile. Using our deep learning-powered high-content imaging platform, we previously confirmed 617's activity in primary human samples with example data in ovarian cancer samples shown at the AACR earlier this year. These expanded results that you can see here on the right-hand side of the slide in the waterfall plot led us to generally define two groups of patient samples, effectively high and low responder groups in the indications tested. The waterfall plot highlights the ability of our platform to identify from primary human tissue samples responders and non-responders to our CDK7 inhibitor across tumor types, which is really exciting. It's important to note that this is not showing a percentage of response rate, but it's helping us to understand who the right patients to study are, and we can see that the type of cancer is not the determining factor in the patient tissue response to CDK7. Moving on to slide 15 then. Using transcriptomics of the same patient samples, we have identified a gene signature, the first established in ovarian cancer for which can potentially predict an outcome for patients. We are actively working to expand the signature to other tumor types. Here on the left, we're showing the gene expression levels of genes that make up our CDK7 predictive signature in an ovarian cancer patient cohort using 30 samples as a training set for a statistical model. We are currently undertaking single-cell ex vivo functional screening combined with transcriptomics after CDK7 perturbation in disease-relevant primary human cancer samples to refine and improve this gene signature further. We are aiming to use this baseline transcript data combined with a functional assessment of 617 in primary patient samples to model and then predict patient response to CDK7 inhibition. We are currently adding more indications to this model and further confirming the model biologically ahead of a trial for validation alongside the trial. What we are doing is essentially running a clinical trial ex vivo to understand which patients will respond based on the new biomarker prior to entering the clinic, therefore increasing the chances of treatment response and trial success. As a reminder, the functional layer is from the same technology that we clinically validated in the separate prospective trial of EXALT-1 in hematological cancers. In that trial, we showed that the AI-driven platform was able to select the right treatment for an individual patient, significantly improving their progression-free survival. Finally, which is not shown here but was presented at the ENA. From single-cell transcriptomics data collected after treatment of a sample, so a CDK7 inhibitor, we are validating existing and defining novel CDK7 specific pharmacodynamic biomarkers that may enable us to track 617's activity, potentially non-invasively during our planned clinical study. By combining AI-based patient tissue analysis and transcriptomics data, we identified both a PD biomarker and a gene signature specific to CDK7 and the drug candidate 617, which may distinguish patients that will be optimally responsive to our therapy. This advance of using disease-relevant patient tissue pre-clinically to predict the response of cancer cells to our CDK7 inhibitor means that we have taken huge strides towards the overall goal of being able to deliver the right drug to the right patient. We plan to validate this data retrospectively alongside our planned future trial. Our goal is to make precision medicine a reality for patients through the application of AI. Before I turn over to Ben to cover financials, I'd like to highlight another key development, the exciting progress with biologics. We're now expanding into generative AI design of novel antibodies. We've taken the potential of AI combined with automated experimentation to build a process for biologics design that we believe is more efficient and will lead to better results for patients. This expansion effectively doubles the addressable target universe of our precision medicine platform, allowing us to develop the most effective drug for patients regardless of modality. How do we actually refine how biologics is developed? It is by design, not discovery. On slide 17 today, the current process is driven by experimentally discovering binders, which have many limitations. By virtually generating and designing precision-engineered, fully human biologics, we can overcome these challenges. We believe that our method can explore a much broader target universe and create the right antibody for a specific target. Moving to slide 18, we've already been able to show that our virtual screening methodology for antibodies is now over three times more accurate than the published state-of-the-art. Additionally, we can produce accurate protein modeling for antibodies up to 35 times faster than our result. We've also generated data in new ways when analyzed using AI will allow more complex and complete understanding of human antibody biology. Our new automation laboratory will be up and running next year and will allow rapid assessment of essential qualities like affinity, immunogenicity, aggregation, and stability that feed directly back into our AI models. By combining this knowledge with the ability to generate complete novel antibody designs virtually, and as we mentioned last quarter, we've already used our precision medicine platform to help select patients for biologic programs in our current collaboration with Sanofi. I'll now turn it over to Ben to cover our financial highlights. Thank you, Andrew. I'll now take a minute to close with highlights from our financial results for the first nine months of 2022. Full results are detailed in our press release in Form 6-K. I'll review the results using US dollar the September 30 constant currency exchange rate from pounds to dollars of 1.11. If you are converting our financials using periodic US dollar to GBP spot rates over the last year, there may be volatility in US dollar consolidated figures. However, it's important to note that we hold our cash balances in either British pounds or US dollar based on expected expenditures in that specific currency. Therefore, the actual operating impact of market exchange rate movements are greatly reduced or eliminated. Our cash inflows from collaborations for the nine months ended September 30, 2022 were $117 million, as compared to $67.5 million in the first nine months of 2021. We continue to expect cash inflows from collaborations to remain lumpy around milestones in business development. For the first nine months of 2022, net operating cash outflows were $15 million in comparison to net operating cash inflows of $8.3 million in the first nine months of 2021. This has been an important year of growth with investments in our platform including precision medicine, automation, and biologics. We will continue to pursue a strategy that balances near-term monetization through partnerships with building a truly differentiated technology platform and pipeline. This is why we can feel confident growing the business while also protecting our cash runway. This year we have had a number of one-time capital expenditures associated primarily with our automation laboratories and facilities expansion. Combining CapEx with our operating cash flows and a few miscellaneous items, our net cash burn for the first nine months was $37 million. We ended the quarter with approximately $625 million in cash equivalents and bank. With that, I will turn the call back to Andrew. We are working to transform the industry, not just by bringing our AI-driven drug discovery platform into new modalities such as biologics, but also modernizing the way we select patient populations that may miss clinical trials. With that, we would open up the call for questions now. Everyone, in order to ask a question, please press star followed by the number one on your telephone keypad. Again, if you would like to withdraw your question, press star one. Your first question is from the line of Michael Ryskin with Bank of America. Your line is open. Great. Thanks for taking the question, guys. Can you hear me? Yes, Mike. Good to hear you. Great. Thanks. First I want to start on the biologics focus. Really interesting technology and, you know, most of the time. Could you talk a little more about how you see that ramping over time, you know, in terms of incremental investment going forward between the small molecule platform versus large molecule and just in general, how should we think about that part of the pipeline evolving? Is there any particular target for you for internal in terms of small molecule, large molecule, or are you gonna, you know, follow the target and sort of, you know, play it one by one? Thanks. That's a great question, Mike. We do see if we look at the industry, you know, the relative proportion of small molecules versus antibodies now is very significant. You know, it's pretty balanced across the many companies' pipelines. That's why moving into biologics was so important to us. It effectively doubles our target universe that we can now address with our technologies. As a first sort of proof of concept experiments, the first of early pipeline developments in 2023 particularly as we bring the automation laboratory online for our biologics platform. We do expect actually it to be an important part of both our internal pipeline, but also an important part of our partnership pipeline, actually. That's something which we would, you know, I think you should expect to see in 2023, how actually we develop the biologics platform in both of those directions. We're excited by that. It increases the end-to-end platform ability. I think what's really exciting about this as well, Mike, is that we've already demonstrated our precision medicine platform works in biologics. This is already part of a deal we have with Sanofi, and it's absolutely ongoing work happening right now. What's important about here, I think, compared to maybe other sort of biologics sort of discovery organizations that are out there is, for us now, this becomes a new modality that plugs into our end-to-end platform that can benefit from the target discovery engine that we're also using on the Sanofi platform. The molecules that we're developing could also then fit into our precision medicine strategy that we described today. What I would say is we do expect this to be a major part of our pipeline going forward. Right now, I wouldn't give guidance on the exact balance between small molecules and biologics. What I would say is that as you've seen the field develop in the rest of the industry, now that we have a wider range of targets we can go after, the biology will dictate actually which is the best modality to then go for. Right. Makes sense. Just a quick follow-up on that. I mean, is there anything we should take in terms of the improvements that the platform can provide for biologics drug development? By that I mean, you know, you've characterized the value add for the small molecule side of things. When we think about biologics, should we think about it being sort of a similar benefit in terms of cost reduction, time reduction of getting a drug, you know, to R&D phase? Absolutely. That's why we are taking a generative design approach. Actually to give you a bit more color on the technology developments that made us so excited about development platform. I actually wanna bring Garry Pairaudeau, our CTO, into this conversation. Gary? Yeah. Thanks, Andrew. Hi, Mike. I mean, as Andrew said, we're super excited about this, and I think that the benefits are gonna come in twofold, really. Biologics by design allows us to target specific epitopes, allows us to target proteins that aren't accessible through traditional methods. Of course, we're also gonna get a speed benefit and a quality benefit by being able to design biologics molecules that are intrinsically more developable and more suitable for treatment of patients, you know, better humanization, et cetera. If I can squeeze in one quick one for Ben. You know, appreciate the color on the cash burn and cash balance. Any, you know, high-level guidance points we can think about for 2023 as you've got candidates progressing into the clinic in terms of how to think about R&D expense or cash burn? Thanks. Yeah. We're not giving any formal guidance. What I'd say is, you know, there probably will be a slight uptick, but not something dramatic from where we are this year. Okay. Thanks so much. Your next question is from the line of Chris Shibutani with Goldman Sachs. Your line is open. Hi. Good morning, everyone. This is Charlie on for Chris. Thank you so much for taking our questions. I just had a couple questions on the 546 program as we're moving into the clinic and the new trial there. With this ABS patient signature that you've identified, I'm just wondering how does that work from a practical standpoint in the clinic? How easy is it to identify patients by this signature? As we look down the road in terms of a potential future label, how are you thinking about what that label will look like in the context of a signature like this? How would that look in terms of if you were trying to go after a tumor agnostic approach of the label? Just wondering, if you could provide any color there. Additionally, on EXS21546, I was just wondering, as we're looking down the road towards potential checkpoint inhibitor combinations, how you're thinking about which combination partners you could potentially pursue and whether a formal partnership based around one of those combinations is possible? Thank you so much. Good morning, Charlie. Thanks for your question. Good to speak to you again. I'm gonna actually hand over this question to Michael Krams, our Chief Quantitative Medicine Officer, to guide you through how he's thinking about using our biomarker signatures in our clinical trial planning. Mike? Yeah. Hi. Thank you very much. Great question. Ultimately, our objective is to be a matchmaker between the problem of the patient and the solution that we provide. To achieve this, it's important that we identify biomarker signatures that enable us to match the right treatment to the right patients. As we start our phase I and II study, and you will hear more about this soon, we are going to further qualify and build confidence in the biomarker signature. The design is set up to tell us at what level we have sufficient confidence in the biomarker signature to provide to us a decision-making quality in when to start to enrich patients. Our first phase I/II study will learn about the compound, but it will equally learn about the biomarker signature. The minute we have sufficient confidence in it being deployed as a decision-making tool, we'll switch and deploy it accordingly. I really think that the second question is absolutely critical, and it speaks to our philosophy. We develop drugs in a patient-centric fashion. This is not about just developing a particular compound, but the question is, what is this compound contributing to the overall solution to the patient? In the case of an A2A receptor antagonist, we clearly need to think about combinations with checkpoint inhibitors, and the entire program in phase I/II is indeed set up to teach us on how to think about the add-on strategy of A2A receptor antagonist onto checkpoint inhibitors, and more on that soon. Great. Thank you so much. If I could just squeeze one more quick one in just for my own curiosity. You know, now that we're seeing the biologics part of the platform ramping up as well, I'm wondering if you can envision the possibility that you might see kind of a combination between the small molecule and bispecific patient population. That's a really interesting question, Charlie. Actually, the flexibility we have within our platform actually gives us a wide range of possibilities of what we could go for. This is what's exciting actually by having these different sort of design engines at our disposal, is that actually such a possibility of design in ADCs is now actually within our wheelhouse. It's something I'm sure I know where actually many of our sort of targeted indication teams have been thinking about the possibility now of what other modalities and combination modalities actually could be put together with the two design engines we now have on board. Great. Thank you so much for taking our questions. Thanks. Your next question is from the line of Peter Lawson with Barclays. Your line is open. Hi, this is Shayon for Peter. Congrats on the quarter. I just wanted to know if you could add some color to the changing sentiment around AI from pharma companies and whether you're seeing that there are certain pharma companies that are more likely to do AI collaborations and what would convert the slower ones. Thank you. Hi, Shayon. Good to meet you. Great question, actually. In fact, we're finding an increased interest actually in the use of AI within big pharma companies. A couple of years ago, it still felt we went in sort of half minds in how we are moving forward in totally large companies. What we're finding now actually that I believe that battle actually has been won. I believe actually people now see that this is the way forward, that actually drugs are gonna be designed and developed using AI. I think actually a proof point that we've already brought to the table, showing that we already have molecules moving forward, showing the kinds of patient selection strategies that we talked about today and the results we've seen in things like the first AI-driven sort of clinical trials of EXALT-1, are now coming together into a crescendo of showing potentially the direction trial risk comes. What I would say is that we continue actually to have deep discussions with many companies. We hope to be unveiling, you know, in the next few months as well, sort of further collaborations, you know, from the MD Anderson collaboration we had yesterday to sort of deeper collaborations with pharma companies going forward. Also what's important as well is that once we start collaborating with companies, also the continued deepening interest. You know, our deal with Sanofi started off actually with a, you know, 1 collaboration working on bispecific small molecules. From that one sort of in-license lead molecule then led to a $5.2 billion deal. Our work with BMS already we've licensed in the first molecule, which we'll be hearing about hopefully in the next few months and more to come there. That then expanded the deal then originally from three projects now to eight projects, you know, all of which now are ongoing. It gives us real confidence as well that we see our major partners, when they start working for us, really come back and more than double down on their work. To give a bit more insight into this, particularly on how those relationships are developing, I just wanna get Dave Hallett, our Chief Operating Officer, actually to give some of his thoughts. Dave? Thank you, Andrew. Yeah, I think you said the bulk of it already. I think what we're seeing is, I guess, using BMS and Sanofi as, obviously, as two significant examples of organizations who engaged with us many years ago in terms of a first generation of a collaboration, that's both Sanofi and BMS. Then based on the output and the productivity that we're able to kind of show to those organizations, they came back to the table to obviously do not only expanded deals, but also to gain access to the expanding capabilities of our organizations. I think that's the journey we will continue to pursue, to find kind of larger pharma organizations that are open to kind of utilizing the way that we're trying to re-engineer the entire drug discovery and development process, and to kind of join us on our journey. I think we will see more of those collaborations, but I think as we've reiterated on a number of occasions, our preference is to go very, very deep with a subset of major partners. There are very good operational reasons for that. I I think that's the direction I think you'll see from a business development perspective over the coming years. Just one point I wanted to add on, this is Ben. What we've really seen is the commitment from the top makes a lot of difference. I think if you look at all of the organizations across the industry, they are all convinced that technology is going to change the way that we do drug discovery. They're all doing different experiments. If you look at the ones that do the larger transactions, really the sort of things that we focus on, because to Dave's point, we like to go deeper, we like to do larger deals. Those are really gonna be deals where they've got the executive buy-in, and they're making it a priority in the strategy. That's one of the biggest items that we look for when we're evaluating our partners. Shayon, just one last word from Mike Krams as well, I'd like to add some color to his question. Yeah. I mean, it's really just to like, to highlight the experience that I've had coming from big pharma and joining Exscientia recently. What struck me has been the interest and ability to connect the dots and to integrate AI expertise and experience in the discovery platform, AI expertise and experience in the translational science platform, and now the ambition of applying AI in a clinical development platform. What I haven't seen before is the tight integration and the connecting of the dots in a very strategic fashion in an end-to-end ambition, which we at Exscientia here have written on our business cards in a patient-centric manner. Great. Thank you so much for the color. Your next question is from the line of Vikram Purohit with Morgan Stanley. Your line is open. Good morning, and this is Gospel on for Vikram. Congrats on the quarter. We have two quick questions. For the recently announced collaboration with MD Anderson, could you discuss the terms of the agreement and what the focus areas in oncology are? Additionally, your release notes that your Sanofi collaboration has progressed an additional target in oncology. Could you speak about the review process this target went through and what the next steps are? Could you discuss how specifically Exscientia's platform was used to identify this target? Thank you. Excellent. Gospa, good to speak to you again. Great questions. Actually the best person to answer this actually is Dave. Dave, how about it? Hi. Thank you, Andrew. Let's start with the question on MD Anderson. As we announced yesterday, the collaboration with that group is really predicated on a couple of Exscientia's small molecule design capability, but also its translational platform. Marrying that with the significant kind of discovery and clinical scale research that is conducted at MD Anderson. When we sat down over the course of summer to kind of put this collaboration together, what I particularly liked about the relationship was that, as we're looking through potential targets that we could discuss and evaluate that would come into the collaboration, we had kind of principal investigators from MD Anderson kind of in those calls. These are physicians who are interested in the biological pathways that we were contemplating. I think it's kind of highlighted in the press release that we have a more than interested kind of clinical set of investigators within MD Anderson, so should we be successful in these clinical trials. I'm not kind of limited to go into the kind of financial terms of the collaboration, but this will be a joint venture between both parties. I'm personally kind of very excited to be working with MD Anderson and with a very old former colleague of mine, kind of Philip Jones, who I once worked with kind of over 20 years ago at Merck. In terms of Sanofi, rather than answer that third target in particular, let me give you a sense of the broad way that we're kind of value that Exscientia brings to target identification. The kind of things that we're doing is that using our Centaur Biology platform is that we're doing significant disease area landscaping within the areas of interest for Sanofi in subsets specific kind of landscapes around kind of oncology indications, specific landscapes around areas of kind of inflammation and immunology. What we've been able to integrate there is not only the kind of corpus of kind of public data and applying the deep learning methods that reside therein, is that we can also integrate and have integrated with that proprietary datasets like GWAS datasets, for example, that Sanofi have brought to the table. By integrating, if you like, the know-how from the two organizations on top of our kind of AI capabilities, that's allowed us to identify kind of significant kind of short list of kind of potential targets to explore in more detail, including experimentally. Part of that experimental validation of kind of those kind of targets is that we can, and as well as doing kind of functional genomics, et cetera, and working cell lines, one of the beauties of the collaboration on behalf of Sanofi is that we can do further validation in primary human tissue settings on our precision medicine platform. Not only validating the target from a biological pathway perspective, but from the very start of the program, highlighting the potential patients we might want to go after in the clinic. Mike, did you want to add something as well to that? I'm sorry, Mike, you're on mute. Yeah. I think you summarized it perfectly. Really not much to add. Yeah. Okay. All right. Thank you very much. Thank you, Victor. There are no further questions at this time. I will now turn the call back to Andrew Hopkins. Great. Thank you very much, operator. Thank you to everyone on the call today. Thank you for your continued support of Exscientia. Over the coming months and into 2023, we look forward to advancing multiple programs going forward, including 546 and 617. This work is just the beginning for us, and we remain excited by the potential of our AI and deep learning platforms that they hold for future drug development. Thank you again for joining us today. Operator, you may now disconnect. Ladies and gentlemen, thank you for participating. This concludes today's conference call. You may now disconnect.