Nautilus Biotechnology at Investor Summit: Innovative Proteomics Pathway

On Wednesday, 25 March 2026, Nautilus Biotechnology (NASDAQ:NAUT) presented at the Investor Summit Virtual Conference, unveiling its strategic advancements in proteomics aimed at transforming drug development and precision medicine. The company highlighted its pioneering Voyager platform and strategic partnerships, while also addressing financial sustainability and operational growth challenges.

For further details, readers are encouraged to refer to the full transcript below.

Sujal Patel, Founder and CEO, Nautilus Biotechnology: Hello, everyone. I’m Sujal Patel, Founder and CEO of Nautilus Biotechnology, and I’m joined by Anna Mowry, our Chief Financial Officer. We’re here to talk about Nautilus unlocking the power of the proteome to revolutionize biomedicine. As we begin, this is our standard safe harbor statement. This presentation and my oral remarks have forward-looking statements. All those statements are subject to risks and uncertainties that are in our SEC filings on Form 10-K and 10-Q. One of the major developments over the last two decades in biotechnology is that as a community, scientific community and humanity, we’ve conquered genomics. We can take a drop of your blood, we can run it on a sequencer, tell you what 99.9% of your genes are. It’s reliable, it’s accurate, it’s really low cost.

Your genes really don’t change from the day you’re born to the day you die. They don’t tell you anything about the real-time state of disease. They don’t tell you about what’s going on inside of your body, and so they have very little power to affect drug development, to affect precision medicine. Outside of oncology, which itself is a disease of the genome to some degree, the genomics revolution hasn’t really had a huge impact on drug development and biomedicine in general. The scientific community has recognized over two decades that the issue is that we need to be able to analyze proteins in samples, not just your genes. Proteins are the little biomolecules that do all of the work in the human body. 95% of our FDA-approved drugs target proteins. Most molecular diagnostics target proteins.

Today, the state-of-the-art technologies that analyze proteins within a human body from a blood sample, from cells, from tissues, those technologies return extremely poor, extremely low-quality results. This is a huge challenge in the scientific community that has led to over 90% of new drug development programs failing. Billions and billions of dollars are spent on every single drug program that begins. We recycle the same mechanisms, the same targets, and most of those programs fail. One example of that is Alzheimer’s disease. Alzheimer’s disease is one of the first applications of the Nautilus platform, which I’ll describe shortly, that has emerged. It’s a disease that affects 7 million Americans. It’s a disease that has a worldwide economic impact that’s over $1 trillion. We’ve known about this disease for 120 years.

For 40 years, we’ve understood that the disease’s pathology is driven by the aggregation of proteins within the brain, which causes neurodegeneration. In 40 years, we still have zero effective therapies. We still don’t understand, outside of understanding that there’s a single protein in the human body, the tau protein, which has a accumulation of errors and modifications that cause this disease, we don’t understand what’s the molecular root of the disease. We don’t understand how to stop it. This is the sort of application that we expect to be able to use the Nautilus platform to begin to make a significant dent. This slide here outlines a little bit around the market opportunity, and it takes a very specific lens here.

Just looking at how proteins play a role in different disease areas and just looking at the NIH research dollars, just America, just NIH, that goes into these areas, you see there’s massive spend. Autoimmune diseases, $1 billion. Cardiac, closing on $3 billion. Almost $7 billion for aging. Almost $4 billion just in neurodegeneration and Alzheimer’s and other related dementias. These billions of dollars go into these programs, and in none of these areas have we had significant game-changing new drug introductions over the last decade. We haven’t seen a significant improvement in the understanding of these disease areas. Our belief is that in order to make a dent here, you really have to have a huge upgrade in the types of protein analysis tools and platforms that exist out there, and that’s what Nautilus is focused on.

The last slide that I wanna highlight as we set up our conversation here is that one of the things that I’m sure as investors in healthcare companies you’ve heard a lot about is AI. AI is a technology that has been transformative with technologies like ChatGPT, creating new applications in consumer and large enterprise types of environments. The existing AI tools like ChatGPT trained on the entire internet. They consumed every piece of text, image, and video on the internet to create a massive training set. In biology, we wanna apply those same technologies. There’s no training set today. No one can accurately tell you what proteins are in our cell, and without that, we can’t create a cellular model. We can’t understand how cells work with each other. We can’t understand what goes wrong in disease.

Proteomics data is the key piece of data that needs to exist in a high-quality, accurate, reproducible form to be able to leverage AI to the fullest for this next generation of drug development, and we think that Nautilus is a key enabling technology of that. A couple of times in this presentation, I have pointed out that existing technologies are really inadequate. If you look at the types of technologies that exist today, there are a few types of technologies, and you see some graphics that show the outputs of them. The gold standard to analyze proteins today is using a workflow around the mass spectrometer. The mass spectrometer is a $1 million-$2 million instrument sold by large cap companies like Thermo Fisher, Danaher, and Bruker. These technologies have a wide range of applications, food safety, metabolites, airport security, metallurgical analysis.

There’s been a workflow created over the last couple of decades around these mass spectrometers to analyze proteins. There are also other types of things. There are assays from companies like SomaLogic, which is now owned by Illumina, and companies like Olink, which is owned by Thermo Fisher, which is one of those large caps I just discussed. These approaches all take a slightly different approach to trying to analyze proteins in samples. Ultimately, what you have is you have a bunch of methods which haven’t improved a whole lot in 20 years, and that produce very, very incomplete results. The results don’t show you any meaningful subset of the 20,000 basic canonical proteins that exist in a human. They give you very limited information on the millions of forms of those proteins that exist in a human body.

They have results that don’t agree with each other. They don’t, they’re not reproducible across samples. The results are very biased. They show you things that are the most abundant. They show you the largest changes. Biology is often driven by small changes, and scientists need a completely new way to analyze proteins to be able to effectively impact drug development programs, diagnostics, and precision medicine. This next slide really just highlights for you what I said with actual data. This is a chart that’s based on a paper that was published in 2025. There’s a second paper in 2025 which corroborates this data. Nautilus has nothing to do with these papers. These papers completely highlight the problem that I just described.

What this is a Venn diagram that shows the overlap between Seer at the bottom, which is essentially, you could call it a gold standard mass spectrometry-based analysis approach, SomaLogic, and Olink, the other two companies I mentioned earlier. Here, we’re taking the most basic analysis. How many proteins did you identify, and what proteins, and how did it agree with the other solutions in the space? This is the most basic question. Customers are really looking for more complicated questions. How are these proteins changing over time? How are they modified? But just the most basic question, did you see the same thing that the other products saw? These products don’t agree at all. They agree on less than 2,000 proteins between the three of them. The coverage is incomplete. They don’t agree. There’s very poor data quality.

What we find in talking to customers who use all these proteomics technologies, they combine the data from all of these and try to put together insights, but it’s extremely complicated, even though $ billions and billions are spent every single year on these solutions. If you think about the requirements from a customer standpoint, one of the things that we as a company have done is we’ve spent two separate formal customer studies and spent time with hundreds of biopharma organizations, companies building new drugs, companies in the diagnostic and precision medicine space, and we’ve really determined what the customer believes the core requirements for this next generation set of technologies in proteomics is.

They’ve told us time and time again that we need to be able to be sensitive enough to see very minute changes in cells because those minute changes often drive disease. We’ve heard time and time again that the data needs to be reproducible and robust, that they need a very easy-to-use solution, which has been elusive with current products in the market today, and they need to be able to analyze these samples with enough throughput that they can get through a large number of samples in a reasonable amount of time so that they can accelerate their drug development programs, which speaks immediately to efficacy of those programs and speed and cost of those programs. This w