Welcome to Q3 Investor Summit virtual. We appreciate your participation in today's virtual event. Up next, we are pleased to introduce Nautilus Biotechnology, Inc. If you'd like to ask questions during the webcast, you may drop them in the chat box button on the left side of your screen. Please type your question into the box and click "Send" to submit it. At this time, it is my pleasure to hand over the session to Sujal Patel, CEO at Nautilus Biotechnology, Inc., who will lead the presentation. Sir, the floor is yours. Great. Hello, everyone, and thank you for joining us. My name is Sujal Patel, and I'm the founder and CEO of Nautilus Biotechnology. I'm joined today by Anna Mowry, our Chief Financial Officer, who will be presenting some of the slides this morning/afternoon. Today, we're going to talk about unlocking the power of the proteome to revolutionize biomedicine, and I'll discuss what the proteome is and why it's important. Before I begin, this is our Safe Harbor statement. Anna, if you want to forward the slide for me. She skipped over the Safe Harbor, but the Safe Harbor is that we're going to make forward-looking statements in both presentation and my oral statements, and they're subject to risks and uncertainties, which are described in all of our SEC filings. You'll find those on sec.gov. Let's talk about drug development. Drug development today is a massive market. Pharma globally spends $300 billion on R&D every single year, and those dollars are spent incredibly inefficiently. 90% of drug programs fail, and the problem is getting worse and worse and worse. The number of drugs that are reaching the market is going down, and over the last couple of decades, the R&D spend, the ratio between the spend and the sales of those drugs, has been getting worse and worse and worse. We believe the primary reason for that is proteins. You might be confused. What are proteins? What's the proteome that we discussed earlier? The proteome is the makeup of all the proteins in your body. You may only know proteins as the thing that's on the back of the cereal box, but proteins are the machines that make up your cells that do all of the work in your body. An average human has 37 trillion cells, a million proteins per cell. They're massive, massive little machines inside of your body that do all the work. The technologies that we have today to understand proteins inside of a sample, inside of your cells, they're incredibly limited. We don't understand how these proteins work at a molecular level. We don't understand well how proteins interact with each other. Because of that, it's really hard to develop drugs that target these proteins. 95% of our FDA-approved drugs target proteins. Over the course of the last decades, we've been recycling the same targets, the same mechanisms, and it's led to more and more and more failures. Nautilus is pioneering a completely new approach to measure proteins inside of any sample from any organism. In doing that, we believe that this technology will lead a revolution in biomedicine, bringing new, much more effective drugs to market and bringing a new round of diagnostics and precision and personalized medicine solutions. If you skip over to the next slide, I want to dive into a specific example of how this problem plays out. We're going to discuss Alzheimer's disease. I'm sure that every single one of you has a family member or somebody that you know who's affected by Alzheimer's disease. For me personally, my own mother is affected with Alzheimer's disease. There are over 7 million people just in America living with this disease, and the estimated economic impact by 2050 is over $1 trillion. Today, there are no effective treatments for AD. How bad is that? It's been 40 years since we discussed two key proteins that cause aggregation in the brain, Tau and amyloid beta. It's been 100 plus years that we've been studying this disease. Researchers have tried to build novel therapeutics for AD, but they have a limited understanding of the mechanism of action of this disease, and the results have just been failures and expensive, cumbersome drugs that do very, very little to improve patients' lives. This is a key example of why proteins matter and why we need platforms that measure them much more effectively. AD isn't the only place that this problem exists. If you look at a broad range of diseases, autoimmune diseases where we're looking for different protein signals that are received by immune cells, if we look at cardiovascular disease, which causes proteins to have malfunctioning forms that contribute to blood vessel blockages, if we look at aging, where all of these different aging processes are driven by proteins, there are billions and billions of dollars of just NIH dollars that are going into studying these diseases. That's what these numbers are here. This is unrelated to the $300 billion pharma spend on top of this. The inability to measure proteins effectively, the key driver of biological insight in the human body, the inability to measure that causes a huge, huge inefficiency in developing new drugs. If you think about the current dogma that's out there and what I hear from health care investors, the Anna for the next slide for me, the key thing I hear is, we've solved DNA. Isn't that enough? DNA is not enough because it doesn't really change from the day you're born to the day you die. It doesn't tell you anything about what's really going on in the body. I hear AI is going to solve these problems. I mean, that seems sort of intuitive. We've been studying Alzheimer's disease since 1906. There's millions of papers. Can't AI just go and ingest all of these papers and come up with a new drug? The answer is absolutely no, you can't. The reason is that the AI technologies that exist today, the tools that we see out in the world like ChatGPT, are so powerful because they've trained on a massive amount of text data that's on the internet. That's what large language models were built for. If we want to apply that to biology, it will be applied to biology. If we want to apply it to biology, we need to have a complete proteome training set to go and predict biology. That's what's missing today. Today's platforms produce such limited data, and we've unlocked, as a scientific community, such a small fraction of proteomic data. We don't have the data to effectively train AI models. If you look at the quote that's on the top of this slide here, this is from a report from the National Academies of Science, Engineering, and Medicine. What this quote essentially says is we do not have the data today that allows AI models to change the rules of biology and help us to understand what's going on. This is the problem that Nautilus Biotechnology is building a platform to solve. Let's look for a minute at why the proteome is really, really complex to measure. One of the things I mentioned on the previous slide is that the genome is unchanged. Sequencing DNA is something that's become commonplace. You can take a drop of blood or some saliva for a couple hundred dollars in a day or two. You can completely measure the genome. The genome doesn't change really from the day you're born to the day you die. While it's relatively easy to measure, you need to understand what's going on at the protein level to understand what's going on with disease mechanisms, to understand what the state of your human body is. Are you afflicted with a disease? Will this therapy work? Is a therapy working? When we talk to researchers about what they need to do, the first thing they tell us is that they need to confidently quantify proteins within a sample. They need to understand, for example, in a cancer cell, is a protein more abundant or less abundant? They need to be able to deeply explore all of the different variants and forms of proteins that exist in the human body, and they need to be able to measure large changes and small changes. Doing these things is extremely difficult. When we solved, as a scientific community, when we solved the genomics problem two or three decades ago, we borrowed many of the same mechanisms from nature to copy DNA, to read DNA, and we made a machine to do that. In proteins, there is nothing in nature to borrow from. There's no way to copy a protein in nature. There's no way to read a protein in nature. All new technologies have had to have been developed over the course of the decades to try to chip away at this protein problem, and those technologies are inefficient. If you look at the state of the art today, the state of the art is a set of technologies that are fundamentally flawed. On the left side here, you see various technologies that make up the tens of billions of dollars of market for protein analysis. There's western blot analyses, which have been around for many decades. In the middle is mass spectrometry, which is considered the gold standard of protein analysis. Billions and billions of dollars of mass spectrometers are sold by companies like Thermo Fisher and Danaher and Bruker every single year to measure proteins. There are a set of assays and other types of analyses, like an ELISA, for example, that measure proteins. All of these methods are deficient in a variety of ways. They give you very, very incomplete coverage of the sample that you're looking at. For example, the best analyses that you can do with a mass spectrometer, if you're analyzing a drop of blood, for example, the very best that you can do is accurately quantify what's about 10% of the proteins that are in a sample. It's woefully inadequate to understand what's going on in disease mechanisms and build drugs. The technologies that exist are very biased. They're extremely hard to scale and extremely hard to use. The measurements are often fuzzy and imprecise. The outputs are confusing. To put a fine point on that, the next slide here is a very recent study, which was just published. In this study are two platforms, Somalogic and Olink, which are some of the newest platforms on the assay side of the protein analysis world. On the bottom left, you have CRX-T, which is an exemplar for the very best analysis that you could do with a mass spectrometer. What you find here is that, you know, the numbers that are in this Venn diagram are the number of proteins that the platform saw and how they agree with each other. I think you would agree with me that this chart's a complete mess. I mean, these three platforms can only agree on about 2,000 proteins. They each see things that the other one doesn't. There's very little data, very little agreement between what's going on. The data quality is poor, and the coverage is less than half, at the very best for any of these solutions, in a very fuzzy way. This is the reason why scientific progress in drug development and precision medicine has been hampered. All of the technologies that are out there provide very incomplete, very inconsistent data, and Nautilus Biotechnology is out to solve that. Nine years ago, my co-founder, a man named Parag Mallick, set out to found Nautilus Biotechnology. Parag is a well-known proteomics KOL. He is a Stanford professor, and his lab sits at the intersection of computing and data science and biochemistry. They use mass spectrometry and all of these other products that I've described to measure proteins. Parag was so frustrated with the inability to measure proteins effectively, to better understand what's going on with biomarkers, to train AI systems. His specific research was very cancer-focused, and it was hampered by the fact that the tools didn't exist to measure proteins effectively. Parag is a very unique character. Half computer scientist, half biochemist. Myself, I'm a computer scientist. You know, this combination of bio and computing is so trendy today. Parag's been doing it for 30 years, his whole career, his academic degrees. The solution that Nautilus Biotechnology came up with to revolutionize proteomics is a combination of all of these three disciplines: computer and data sciences, life sciences, physical sciences. It requires a very unique, counterintuitive intersection of these disciplines and something that the founding team of Nautilus Biotechnology has specific experience with. The approach that Nautilus Biotechnology has been working on for the last nine years is an approach that we call iterative mapping, and it is a transformative, brand new way to measure proteins. If you think about the various approaches I described earlier, they take one of two broader approaches. One is the mass spectrometer, takes every individual protein molecule, and it breaks it into tiny pieces and essentially shoots it as a gas through a tube and figures out how long the fragment took to fly, and so how much did it weigh. From that, they use a very complex, convoluted process to back into how much, what kind of protein that probably was. On the other side, there are assays that go and take a single protein, and they use an antibody, which is a construct from nature that can identify a protein. One of the things Parag realized was that these antibodies and how they interact with protein molecules, this is my protein molecule, those interactions are extremely fuzzy. They're highly subject to variability, and we're never going to have proteins that will have antibodies that are specific to every single protein. The counterintuitive process that Parag came up with was this idea that in computation, we're used to identifying something not with one specific measurement, but lots and lots and lots of very small interrogations to go and reveal the diverse features of a molecule and then use computation to come up with a shockingly accurate identification of what that molecule is. That's, in essence, what the iterative mapping method does. Using a variety of different antibodies, it gathers information about the molecule over hundreds of touch points to tell you with shocking precision at single molecule sensitivity what each protein molecule is, and it does that for billions and billions of molecules at the same time over the course of one or two days. It provides a very, very precise, simple measurement of everything that's in a sample or substantially everything that's in a sample. If you look at what it took or what it continues to take, given that Nautilus Biotechnology is still a development stage company to a great degree, what it takes to build a solution like this, you have a set of really significant technologies that have had to have been built over the course of nine years and next year before we plan to release our full product offering. The company has a tremendous amount of IP and differentiation in some very big problems that had to be solved. One is that in order to run this process that I described of interrogating a molecule over and over again, we have to figure out how are we going to immobilize billions of molecules so that we can perform this analysis. This is technology that is unsolved in the world until Nautilus Biotechnology created a hyperdense single-molecule array that can immobilize 10 billion full intact protein molecules and present them in a way where antibodies can be introduced one after the other to go and probe them and learn these different features. We've developed a set of algorithms that are powered by machine learning and AI that enable you to take all of that data in, take this very complex process that occurred for analyzing a sample, and produce simple, robust data for the customer. It produces actual counts of the molecules that we saw, different forms of the molecules that we saw. This type of technology is extremely unique and very different from anything else that's out there. Nautilus Biotechnology has spent the last virtually a decade building substantial IP and patent portfolios to protect this technology. If you look at this slide, it tries to talk about on two axes how you would compare what Nautilus Biotechnology is creating and the incumbent solutions in the marketplace. On the x-axis is completeness, which essentially is how many proteins can you see and quantify in a sample. On the y-axis is your confidence in your data quality. Really, the goal of Nautilus Biotechnology's technology is to provide a solution that matches the scale of proteins in your body because there are so many proteins that make up your cells, and providing accurate, reproducible data on the other axes that is AI-ready and allowing our customers to develop to generate rapid and really important insights that are actionable and that enable them to build better drugs and diagnostics. Within our iterative mapping platform, there are two key applications that emerge. First is what we call broad-scale proteomics. This is largely what we've talked about today and we believe is the primary near-term, medium-term application for our product. This is essentially the ability to take a sample in and return to the customer what all of the proteins in the sample are. It is a way of developing a very broad analysis and understanding what's going on inside of the system. This technology is directly comparable to what mass spectrometers do and what these other assay products do. On the right is a second key application, which has emerged earlier than broad-scale proteomics, and going one protein at a time. What this enables us to do is see the very significant but very minute molecular differences between different proteins. We started rolling out these targeted proteoform capabilities with the protein Tau, which is implicated as the key biomarker in Alzheimer's disease. What we've been able to do and what we've demonstrated with a preprint that we put out this year is that we're able to take this iterative mapping technology and map very minute features and modifications on the Tau molecule to understand in a greater depth than has ever been understood before what is going on with the pathology of the molecule and the disease. This targeted proteoform analysis capability is a new capability that is well beyond what has ever been done even one time in the scientific community. Today, it's for the Tau biomarker. In the future, we expect to expand to other neurodegeneration markers as well as other diseases such as those that I mentioned on the earlier slide, like autoimmune, cardiology, and so forth. Underneath both of these use cases, which share our same platform, are the use cases of our customers. Pharma is interested in understanding disease pathways, mechanism of action of drugs, understanding the basic biology of a human. They're interested in toxicology and how potential drug candidates will interact with other organs in the body and other cell types within the body. These are all of the core applications that we think make our technology critical. One last slide here before I pass this presentation off to Anna. One of the things that is very unique about our company is that we have put together a cross-functional team that is driven to really deliver on the promise of proteomics, and they're a team that I'm incredibly proud of. I've already introduced Parag, who has spent time as a professor at Stanford, who's been in the proteomics world for decades and is a well-known KOL. Myself, I'm an entrepreneur for a long time. Up until Nautilus, nine years ago, I came from the tech world, having founded and was CEO of a company called Isilon Systems, which was founded in January of 2001. That company went public in 2006 and grew to just about $100 million per quarter of top line, 20% positive profit margins before we sold it for $2.6 billion. A number of our management team members, Anna Mowry, our CFO, Gwen Welder, our Chief People Officer, joined me on that journey. Kenzah Zukini, Senator, ran the mass spectrometry business for Agilent Services, our Chief Marketing Officer. Subrah Sankar, who's on the right, is our R&D executive. Subrah runs an organization of over 100 inside of our company. He was the one who put the first genomic sequencers out when he was at Illumina. He was the one who built the next eight platforms afterwards, running all of those teams. Rare talent for a company that needs to put together all of these disciplines to build a complex biological, electrical, computer science hybrid. Lastly, Matt Murphy was the General Counsel of 10x Genomics and PacBio, two well-known public company names. This is a team that knows how to execute and works really well together to bring to reality what I've been discussing. Let me go pass the slides off to Anna, our CFO. She's going to talk about business model, the market strategy, and our financials, and then I'll wrap up. Great. Thanks, Sujal. Now that we've heard the transformational product that Nautilus Biotechnology is developing, let me talk about how we build a business around that. The market for proteomics is huge and growing. We anticipate it will reach $57 billion by 2030. Our primary entry point into that market is through academic institutions and pharmaceutical organizations doing discovery proteomics. As Sujal talked about, the gold standard for that is the mass spectrometer, and billions of dollars are spent every year either expanding mass spec capacity or replacing aging instruments. Given our value proposition relative to the mass spec, we intend to go after those budget dollars directly. While that's a really exciting opportunity, we see significant market expansion potential with the capabilities that come from our platform that unlock the proteome. We see us bringing in a whole new class of researchers into proteomics today that's completely inaccessible given the difficulty of using mass specs. We also think the completeness of the proteome can drive a new wave of AI-powered drug discovery, as well as our customers ultimately pulling us into clinical diagnostics and finally delivering on precision and personalized medicine. As Sujal said, we are still a pre-commercial company. What we've said is that we expect to launch our platform in late 2026 with broad commercialization thereafter. This is when our business model shifts to shipping instruments and consumables. While we have yet to release final pricing, what we've said is that we expect to be able to price our platform at $1 million for that initial solution, and our consumables will be priced roughly around a few thousand dollars per sample. That gives us a path towards $1 million in consumables pull-through over time, which is well in line with what we've seen from others in our space. In the months and quarters leading up to our launch, we plan to have an early access phase, and this is really intended to give our customers access to the data coming off of our platform through a services engagement. This is less about generating revenue, but more about generating excitement ahead of our commercial launch. We are in the pre-market phase today, and in our last earnings call, we announced two collaborations that are really around the Tau application. Given that this is data that's not been seen anywhere else on the planet, those collaborations are really intended to show the value of the data coming from that Tau assay. Lastly, on our financials, as part of going public four years ago, we raised $345 million, and we still have half of those funds on our balance sheet. In Q2, we reported $180 million, and what we've said is that we anticipate cash lasting through 2027, well beyond our launch timeline. We do anticipate needing to raise additional funds to support our expansion, but you can see from our headcount and operating expense results down roughly 20% year over year that we've taken the steps we need to really ensure that we have the ability to raise funds at a time where it's supported by product and commercial catalysts. As Sujal said, we've done this before. We know what it takes to build a highly profitable, growing business. Sujal, I'll give it back to you. Thank you, Anna. Let me just wrap up and say, hopefully, we leave you with the impression here that this is a company that's focused on an incredibly important marketplace with really disruptive science, and we have a business model that has a great deal of potential to generate profits. We've got a high ASP product. We've got an efficient selling motion. We've got a product that's got high gross margins, and there's a world-class team behind it to execute. One of the key points I want to make on this slide is that we believe that there is a great deal of alignment with shareholders. Founders own 32% of our company. Roughly every single open market transaction founders have made is a buy. Me personally, I've bought over $2 million of company shares in the open market between 2021 and today. About 70% of our company is concentrated in our founders and flat-footer holders. We are all big believers in what the company is doing here, and we think that creates great alignment with shareholders. We also believe that there are some real near-term catalysts that represent value creation opportunities, and myself and my management team are incredibly focused on executing and getting to those catalysts and building our share price. Thank you very much, and I think we can open up for some questions. Sujal, we do have a couple of questions. Maybe I start with other companies similarly claim to have scalable proteomic solutions. What makes your solution the right one? OK, that's a great question. I mean, in any market that is worthwhile and has lots of profit potential, there's going to be lots of companies that make lots of claims. The point that I want to make is that every one of the solutions that have been brought to market, new solutions that have been brought to market over the course of the last decade, have been really incremental solutions. I can see a few more proteins, but I can't quantify them well. My mass spectrometer is a little faster. It's a little better. There hasn't been a company that has attempted a wholesale, completely new platform. We think that the analogy of what Illumina did to genomics, bringing a completely new platform to market for genomics, is a similar analogy to what Nautilus Biotechnology is trying to do. It's a bold mission. It's a big mission, and it's taken us, and it will take us a long time to get out. We believe that this product represents a fundamental disruption of the technologies that are out there. Do you want to take one more? Sure. You recently announced a collaboration with the Allen Institute. Can you talk about what you expect from that collaboration? Yeah, sure. I'm happy to. One of the things that I talked about in our slides is that the platform has two modes of operation: broad-scale proteomics, which is the primary application we expect to begin to commercialize with a launch before the end of next year, and the other is proteoform analysis, where we're looking at the various modifications in great detail of a single molecule. The Allen Institute is an agreement that we recently announced that is initially a pilot to go and take some of the samples that they have of brains that are affected with various neurodegeneration disorders, including AD, and analyze them using our technology to get much greater insights into the proteoform landscape of the protein molecule. We think that this deal is just the first of a number in Tau, where we're looking at those modifications. We start with pilot studies, we show the power of the technology, and the goal is to then go and land larger agreements to go and analyze more samples, as well as use that data and publications that come from it to prove out that technology for other customers in the marketplace. Fantastic. Thanks, everyone, for joining our presentation. We look forward to catching up with you soon.