Exscientia Plc

Hello, everyone. My name is Stuart, and I will be your conference operator today. This time, I would like to welcome everyone to Accenture's business update for the third quarter 2021. At this time, I'd like to introduce Sarah Sherman, Vice President of Investor Relations. You may begin.

Thank you, Operator. A press release in Form 6K was issued yesterday after U.S. market closed with our third quarter 2021 financial results and business updates. These documents can be found on our website at www.investors.Xentia.ai, along with the presentation for today's webcast. Before we begin, I'd like to remind you on slide two that we may make forward-looking statements on our call. These may include statements about our projected growth, revenue, business models, and business performance. Actual results may differ materially from those indicated by these statements. Accenture is not under any obligation to update these statements regarding the future or to confirm these statements in relation to actual results unless required by law. On today's call, I'm joined by Andrew Hopkins, Chief Executive Officer, and Ben Taylor, CFO and Chief Strategy Officer. Dave Hallett, Chief Operations Officer, and Gary Paradue, Chief Technology Officer, will also be available for the Q&A session. And with that, I will now turn the call over to Andrew.

Thank you, Sarah. And thank you to everyone who joined us today. It's my pleasure to welcome you to our first earnings call. Today, I'll review our recent accomplishments, and then I'll be joined by Ben Taylor for a discussion around the business models that help fuel our pipeline. First of all, I'll start on slide three to navigate you through the progress and to give you an idea of what we're building towards. I'd like to start with our vision of using AI to discover better medicines faster. As we go through the results, you can see how each of these accomplishments builds upon the foundation of a much bigger vision to transform the pharma industry to accelerate the creation of the best possible medicines for as many people as possible. Accenture is an AI-driven pharmatech committed to modernizing drug discovery and development with AI and advanced experimentation to develop drugs faster and fundamentally better for patients. Better medicines faster. These are three simple words, but let's reflect on what I mean. FASTA. For people facing serious disease, time is the enemy. By using AI-driven drug discovery and development, we believe we can accelerate the discovery of novel molecules and improve the probability of clinical success, potentially saving years of time to get a novel drug candidate approved. More important than FASTA is the concept of better. With our patient-first precision medicine capabilities, we're able to integrate primary human tissue samples into early drug discovery, truly putting the patient at the center. In practice, this means we are developing highly translatable models that can yield better clinical successes, bringing us into a realm where we can potentially make better drugs for specific patient groups. We've already seen in the real world how this approach can help tangibly improve the outcomes of patients. Our EXALT-1 clinical trial, published recently in Cancer Discovery, demonstrated that patients who were treated with the guidance of our AI platform had significantly better outcomes and more durable responses, achieving a 55% objective response rate. Although extremely powerful for these patients, this is only one example of the use of our AI. The real promise of what Accenture is doing lies in our scalability. We often refer to the company as a learning company. By that, we mean that every scientific idea that we pursue Every target is saved, every compound designed and tested, we are learning. And not just learning, but systematizing and encoding those learnings so that they fed back into the platform to enable us to learn faster and do more with every subsequent project that we tackled. We are truly building the system that can enable us scientists and collaborators around the world to pursue more novel ideas in parallel, with far greater probability of success of turning those ideas into actual medicines for patients. Rather than the long road to failure that many scientists face today, with less than 4% chance of a new idea successfully becoming a new medicine, we are presenting a new way forward, where we can advance more science, more quickly, and with far greater chance to reach people waiting for innovative treatments. Today, we will provide you with an overview to our achievements in the past quarter and what we've built so far towards this vision. Throughout this, I hope you can see a common thread that sets us apart, the continual learning nature of our AI system that powers greater precision and speed that makes it possible to execute on a greater scale than ever before imagined. The efficiency of a platform that we're building enables us to scale. That scale allows us to balance risk, and then to take more opportunities for new medicine creation. Today, we walk you through our balanced business model approach that is fueled by the scalability of the platform that we are building. Rather than one business model, we walk you through multiple approaches. Rather than focus on one lead asset, we review a pipeline of more than 25 programs. And even though these may be considered early stage in biotech's parlance, our platform is already demonstrating real-world results that are benefiting patients. And with that, let's turn to a progress. On slide four. As we generate data, develop new algorithms, and initiate new programs, our platform becomes more powerful. Over time, this enables a system that is not only capable of handling many projects at once, high capacity, but also high performance as it gets better and more precise as the system scales with new data. We're starting to see the concept bear fruit in execution across our pipeline. Over the past several years, we've done many deals across biotech and pharma, but importantly, we've begun to deliver upon these, most visibly in a significant expansion of a number of these relationships. Bristol-Myers Squibb, for example, BMS, expanded our original collaboration of three projects now to eight projects. We've substantially improved economic terms on the new projects. BMS also licensed their first drug candidate from Accenture this past quarter, demonstrating our ability to successfully discover high-quality molecules in areas that are proven scientifically challenging. In another significant expansion, we entered into our third collaboration with the Bill and Melinda Gates Foundation, adding a portfolio of antiviral therapeutics against coronavirus and other viruses with pandemic potential. There is perhaps no greater illustration of the promise of AI than with a pandemic. both because of the potential to accelerate the nature of drug discovery, but also in the ability to do so with small molecules, enabling potentially better access and distribution around the world. We also made progress with our 50-50 joint ventures, including selection of the first two targets for our multi-target deal with EQRX. We've nominated our development candidate 617 for CDK7 and are actively preparing 617 for IND-enabling studies and expect to submit our IND by the end of 2022. We look forward to providing you with further updates on this important program. We presented data on EXS617 using our primary patient tissue platform using ovarian cancer models, and we're pleased to say that we're also expanding our work to look at breast cancer patient models and other solid tumors too. We plan to share more details in the coming months. We also continue to scale our business with the initiation of automation labs and the expansion of our wet labs. And on October 5th, we successfully closed our upsized initial public offering and concurrent private placement, raising over $510 million in gross proceeds. Given our diversified business model, which we'll go into in more detail on, Our year-to-date operational cash burn was approximately $16 million, including the all-site acquisition cash contribution. And with approximately $784 million in cash and cash equivalents following our IPO and private placement, we are well-positioned for several years of operating cash burn. So now, we'll take you through our strategy and our business models on slide six before we open up a call for Q&A. At Accenture, our strategy is to shift the curve to develop better drugs faster using our AI-first approach. Our technology investments enable us to improve the probability of success to bring more drugs to patients following these three key tenets. Number one, increase your probability of success. Number two, accelerate the time of turning science into new medicines. And number three, lower the costs of our processes so that we can reset the economic model. As I've talked before, we can use technology to solve these problems and therefore shift the curve in the whole economic life cycle of drug development. And what's key to our ability to deliver better drugs faster is our balanced business model, as shown here on slide 7. Our business models allow us to generate substantial cash flows with our pharma partnerships while also creating substantial value for the company through our co-owned and wholly owned programs. Our pharma partnerships provide cash up front to cover search costs, with the potential for significant milestones and royalties. On average, we're eligible to receive approximately $115 million per partner program, and we have 10 projects ongoing and expect to increase this number in 2022. These programs are not only important for cash generation, but we also learn from each project. As the platform solves unique drug discovery problems of each new target, the learnings create a more robust knowledge base and capabilities for the next project. In programs where we own 50 to 100% of economics, our joint ventures, and wholly owned pipeline, we are focused on creating substantial net present value, or NPV. We have the ability to leverage our infrastructure and AI technologies from targeted identification through clinical trials at a much larger scale than traditional biotech drug development. This scale allows us to take a portfolio approach to science, spreading our risks across multiple therapeutic areas and targets. These three models are critical to developing a robust pipeline and allowing us to balance upfront milestones and strong cash flows versus equity ownership with long-term potential upside. As you can see here on slide eight, we provide end-to-end discovery capabilities, and we are responsible for using our AI and core competencies, not only to evaluate a drug target, but also to design the optimized molecules all part of our effort to design better drugs faster for patients. With our wholly-owned programs currently focused on oncology, immunology, and antivirals, we do everything from idea generation to patient selection for clinical trials using our precision medicine platform. Our patient tissue models help us not only to design a better drug, but allow us to find the right patients that will benefit the most from that drug in the clinic. For our partner programs, for example, with BMS, we drive and deliver projects through to IND, and our partner delivers clinical development and commercialization. BMS has a great internal team, and we believe that the fact that they trust us to oversee a significant proportion of a discovery portfolio speaks to the validation of our capabilities. For our co-owned programs, we add to this also by sharing an idea generation at the start of the project. and patient selection as we proceed to clinical development. We are able to leverage our partners' know-how, for example, with Valley Bio in a rare disease space, and share the potential in future successes. We are an integrated and scalable pharma tech that does more than just target identification or design. Innovation and AI are the core competencies of our company that can be applied throughout drug discovery and development. With each new expansion of our capabilities, we have seen that our partners utilize those capabilities with enhanced economics risk. We believe that this trend will continue as the platform grows. And now I hand over to Ben Taylor.