QuantumSi at UBS Precision Medicine Summit: Advancing Proteomics

On Thursday, 14 August 2025, QuantumSi (NASDAQ:QSI) took the stage at the UBS Precision Medicine Frontiers Summit Conference. The event provided a platform for the company to showcase its cutting-edge advancements in proteomics, while also addressing the challenges it faces in technology adoption. The discussion highlighted QuantumSi’s strategic focus on innovation and collaboration, alongside its financial resilience.

Key Takeaways

• QuantumSi is pioneering next-generation protein sequencing technology to study single amino acid variants.
• The company is leveraging a strong balance sheet to support its strategic initiatives until 2028.
• QuantumSi plans to launch its Proteus platform in 2026 and is collaborating with NVIDIA on AI design tools.
• Cytec Biosciences, a conference participant, is expanding globally and focusing on FDA approval in the US.
• Both QuantumSi and Cytec Biosciences are integrating AI to enhance operational efficiency and technological advancements.

Financial Results

• QuantumSi reported a robust balance sheet capable of sustaining operations until 2028.
• Cytec Biosciences is experiencing growth in instrument deployment and service revenue, with revenue growth outpacing instrument growth.

Operational Updates

• QuantumSi is offering rental, leasing, and placement programs to drive adoption.
• The company is preparing to launch the Proteus platform in 2026, with prototype data expected by the end of the year.
• QuantumSi is collaborating with NVIDIA to develop AI design tools.
• Cytec Biosciences is focusing on global expansion and exploring replacement opportunities to maintain growth.

Future Outlook

• QuantumSi aims to improve sequencing coverage, depth, and sample requirements.
• The company plans to scale the Proteus platform to billions of wells.
• Cytec Biosciences is working towards obtaining FDA approval in the US.

Q&A Highlights

• There were no questions from the audience during the session.

For further details, readers are encouraged to refer to the full transcript of the conference call.

Full transcript - UBS Precision Medicine Frontiers Summit Conference:

Lily, Life Science Data Analyst, UBS: Hello. Good morning. So, are about to start. Great. Thank you.

Great. Good morning. Welcome to the UBS Precision Medicine Frontier Summit. My name is Lily. I’m the life science data analyst at UBS.

So happy to kick off our first panel today, which is a new dimension in proteomics and cellular research. With me today are Doctor. Wenbin Zhang from Cytec Biosciences and then Jeff Hopkins, CEO from QuantumSci. Great. So I guess what the theme today is talking about new dimensions in proteomics and cellular research.

So we’re thinking about like the new tools, new application in the space. So both of your firms bring in new technology to the space, either like full spectrum, both cytometry or protein sequencing. So I wonder with your new technology, what kind of new application that we were able to do it now that we were not able to do in the past?

Jeff Hopkins, CEO, QuantumSci: Sure, I’ll start. So Quantum SI offers what we call next generation protein sequencing, so reading out individual amino acids. I think what we’re seeing customers want to do with our technology obviously there’s a lot of technologies in the proteomic space, typically fairly dedicated to some sort of set of applications. What people tend to want to do with a new tech like ours is study things that have either been very difficult to study with current technology, maybe requires really expensive equipment or bespoke sort of bioinformatics pipelines, or they just couldn’t do it all. So single amino acid variants, literally wanting to see if there’s a change at one position.

They want to look at something called an isomer that they can’t detect on mass spec, but they could see it with RTEC. Or they want to look at post translational modifications, these small changes to individual amino acids that happen when the protein is being expressed. So it’s those types of things they want to look at because they believe they’re going to be very important in the context of response to therapy or prediction or progression of disease, those types of things. So that’s what we’re seeing people want to do with the technology. When you’re here in The United States, I’d say maybe a little different outside The US, where maybe there’s less access to proteomics tools in general, and you’ll see people wanting to do more basic protein characterization and quantitation.

So that’s sort of what we see in the two different markets.

Wenbin Zhang, Doctor, Cytec Biosciences: Sure. Flow cytometer, we are a company, a life science tool company, to do flow cytometer. And clearly it becomes more well known these days because of the BD cell, it’s BDB life science business to water recently. So first I come to look at the name basically to count cells. It’s a median, right?

And it looks at the phenotype of the cells, then looks at the population of different type of immune cells. The conventional technology started quite a few years ago, many years ago actually, but it had a bottleneck. And because of the limitation of the conventional technology with the number of parameters it can look at. So this is where we come from, and we realized the conventional technology actually threw out a lot of information when they detect the cells capture the signals. And so what we come up with is what we call the full spectrum profiling technology.

Basically, we capture all the information coming out of the detection from the cells. And from there, that enables us to expand the number of parameters that can detect using a flow cytometer tremendously. And by doing this, that enables many of the applications which conventional technology wouldn’t be able to. For example, one of the examples is because it’s a spectra, right? And with spectra, in the conventional sense, many of the signals, for example, in the cancer cell studies, autofluorescence from the cancer cell is considered as a noise, which causes problems with the typical detection.

But now with our full spectrum technology, we treat those autofluorescence from the cell as one of the parameters that enable us to drive the application, improve the sensitivity of the detection substantially that enable us to drive into application, for example, like MRD. Of course, MRD typically is one of the application for the conventional flow cytometer, but the sensitivity is very low, a reason why you need to go to a different tool like a PCS or sequencing. But with the flow cytometer, our technology, that improves the sensitivity tremendously to orders of magnitude. So that can really drive one of the applications conventional flow cytometry is not being able to. That’s the one.

And second part is in the drug discovery. As you see the more and more new studies, the new drug being developed, they want speed. And that means they need to look at many different types of parameters. And with our technology that can really help them to look at lots of parameters very quickly and really speed up the kind of pharmaceutical early discovery work for the new drug development. Those are typical applications.

Of course, there are many due to our technology that have been enabled enabling.

Lily, Life Science Data Analyst, UBS: Great. That’s very helpful. I think both of you mentioned that there are, like, conventional technologies out there and then you bring new one. How do you really drive the new adoptions, right, for your technology? What will be like kind of the key factors out there and what are kind of like the challenges that you have seen?

Jeff Hopkins, CEO, QuantumSci: I mean, for us, we’re truly something new that has never been in the market. So it goes through the challenges, I think, anybody who’s ever been a part of bringing a new tech. You have the initial sort of skepticism of does this work, at what fidelity, with what accuracy, how broadly applicable is it. I think especially in proteomics, people recognize this is not DNA sequencing where sort of you can use PCR and just amplify the low abundant thing and see it. So people understand how hard the problem is.

So I think a lot of our early work has been getting the instrument into those leading research centers, whether academic or in pharma biotech, generating the data, proving that it works. So I think a lot of academically, it’s probably a little more focused on the outcome is the publication. I think with our experience in pharma and biotech is they’re often looking to just prove out it’s gonna work in their system, whatever their workflow is. They don’t necessarily make the data public, but they do all the work to prove it’s gonna improve their workflow or improve their some attribute of that they’re looking to improve. So for us, it’s really that, like, prove it is big chunk of it, and that’s where we’ve been focused with the tech.

Wenbin Zhang, Doctor, Cytec Biosciences: For us, certainly we know the market demand, the unmet needs, right? And we found the key pinpoints. So we started with those key opinion leaders and worked with them and developed new applications that really enabled them to solve their issues, their problem. And suddenly they realized, yeah, this is a tool I have been looking forward to for a long time and now it’s right there. And clearly, they become so enthusiastic, become your supporter, your endorser.

From there, we move actually into one of the key critical application and for CRO. And clearly, you know, CRO has been working with many farmers, solving their problem, doing work. But once you start to convince the CRO to adopt your technology, of course, you solve their issues, which is to provide solution for almost all pharmaceutical companies with various different needs, as well as the cost associated with supporting those customers. If you solve those two problems with CRO, they will jump onto it. This is where we start from as well.

From CRO, that gets us into pharma the other way.

Lily, Life Science Data Analyst, UBS: Got it. And we all know that the market has been pretty challenging this year, either from academic side or maybe the biopharma market. So I wonder in this environment, how do you really manage your business? And I think both the firms are also launching new products this year. So I wonder, how do you actually identify the funnel opportunities and go after those very limited amount of budget?

Jeff Hopkins, CEO, QuantumSci: Yeah, I mean, the academic market has been very challenging this year. I think for us, we’re fortunate that we’ve despite being we’re not a profitable company, but we have we just talked about on our earnings call we have a balance sheet that supports us out into ’28, which sort of in this market is sort of almost unheard of for companies at our stage. So I think because of that, we have the privilege of being able to stay on strategy. So we’re obviously very focused on only really investing in the right programs, really managing our expenses so they’re not growing and sort of burning that cash faster. But we don’t have to do the big pullback.

We’re able to stay focused on, okay, as an example, in academia, harder to get capital, while we’re offering people other ways to acquire the platform. They rent the platform. They could lease it. We might place it in certain places. So we have some optionality there with a good balance sheet and a fairly low cost to produce our device.

Pharma biotech, we haven’t seen a big drop off, but it is a longer sales cycle, but we can stay with it and keep working it because we have sort of the balance sheet to support that. And I think R and D wise, everything we do is really based on people who have been using the tech, What are they saying they want to do next? What are they saying they’d like to be able to do that they can’t do? And we just factor that into our pipeline and again, continue to focus on delivering that pipeline, stay on the strategy, don’t get distracted by other things. We’re fortunate to have the balance sheet to be able to do that.

Wenbin Zhang, Doctor, Cytec Biosciences: Post cytometry is a basic life science tool, and you can find it in almost every lab. And so long as they want to do studies in that area, they need a flow cytometer to come along. So there are lots of flow cytometers in the field. Many of them up for replacement. So we want to even though, yes, the market is tough and capital expenditure is kind of tight.

But as long as they want to do experiment, they want to work. They need it. And we need to so what we do is we grab every opportunity with tools, and clearly if they can perform with our advanced features, high performance, and we want to outperform the older technology out there to replace them. So that’s one thing. And second part is we are global.

Clearly, we see an opportunity in other parts of the world, and clearly we want to drill down to it. And hopefully, from there, we will be able to capture those business to supplement certain weakness in other territories. From there, that enable us to maintain our business, make it continue to grow, as you can see, even though under today’s very tight environment last quarter, and we continue to manage to grow our deployment quantity instrument, our full spectrum core technology continue to grow in both number of counts as well as the revenue side.

Lily, Life Science Data Analyst, UBS: Great. Since you mentioned the replacement opportunity, wanted to stick with that concept, right? So I think there are probably around like 50,000 photocytometers out there. How do you frame your optometry in that? Like, how many can you replace and what would be kind of like the key, like, key factors to to really capture those opportunities?

Wenbin Zhang, Doctor, Cytec Biosciences: There are two aspects. One is replacement. Older tools, typically those tools run seven to ten years, and that’s the time they need to be replaced. And then we look at them clearly, when they replace a few aspects, one is the new technology and the new performance, second part is backward compatibility that needs to be ensured. Third is, I want to make sure it’s cost low.

The cost is not only from the instrument acquisition perspective, that also includes the maintenance usage as well as the recurring region consumption, all those aspects with regard to cost. That’s where we provide them the solution to enable them and to lower the overall cost. Then through Cytec Cloud, which is a very popular platform now, to help our users once they get onto Cytec technology, they will be able to reduce their overall cost in designing panel, maintaining their operation. And that’s a very important aspect, especially for many farmers that helps them to reduce the overall operation cost. So that’s one.

Second part of the business, of course, is to enable earlier when we mentioned about new opportunities, new applications which cannot be done with the old technology that actually helps them to drive toward a site, right, because now you are having a tool not only to support your existing needs, but also the future.

Lily, Life Science Data Analyst, UBS: And you mentioned so we have some market disruption here, given that you mentioned the BDDs, why not go after that? What do you think will be your opportunity with the potential disruption? Any share gain that you can frame about? Any color will be great.

Wenbin Zhang, Doctor, Cytec Biosciences: As you can see, we come from the high end of the technology and clearly we are leading And any disruption from our competition clearly provides us an opportunity because SITEK has already been very well established in this market as a leader. And it’s not about competing against with regarding to performance or technology side. It’s about whether you can ensure you can continue to support them going for long term and from operation perspective. Even on the recurring revenue side, the agents panel design, all those things SITEK has been working very hard to help them.

As you can see, our revenue has been growing and actually outpaced our instrument growth. That’s good because leveraging upon our great installed base and our service revenue is growing. This is two areas for our business creation services plus the instrument continued additional installment that drives our business to grow. And I think anything happen to out there is going to help us clearly.

Lily, Life Science Data Analyst, UBS: Okay, great. Maybe lastly on some of the I think last time when we talked about we mentioned I think you mentioned some of the clinical opportunity and you just mentioned MRD will be one of the examples, right? Maybe can you just give us a little bit update in terms of where Cytep is within the clinical market?

Wenbin Zhang, Doctor, Cytec Biosciences: Clinical clearly is depending on geographic locations, and you need to go through the clinical clearance country by country. And we started with China first, and in fact, with 12 of our tools are clinically approved over there. But not just two, also including the reagents panels. And so that part, we are well covered. From there, we moved into Europe right now.

And in fact, our tool has gone through the IVDR clearance, and we have partnered with one of the premier clinical providers over there to drive our clinical instrument adoption in Europe. And we have seen some early attractions and progress also and using our tool to drive the application earlier mentioned like leukemia, MRD, and those kind of new panel design with customers in Europe. And of course coming back always we talk about The US and FDA. And we continue to work through the process right now. It’s going to take a while because Europe, China, the clinical approval process is different from the US FDA.

They need to drive this based on The US process.

Lily, Life Science Data Analyst, UBS: Jeff, I think for someone that really don’t really not close to like protein sequencing story, People will always look into, well, DNA sequencing, which we have seen in Illumina growing. Right? I wonder, can we really copy that adoption curve of the DNA sequencing to your story? What would be kind of like the common threats and opportunities that you have seen so far?

Jeff Hopkins, CEO, QuantumSci: Yeah. I think the number one thing you have to talk about is sort of when can you sort of overlay those stories. And what I mean by that is, you know, a lot of the team at Quantum SI came from the DNA sequencing world. You know, in DNA you have an alphabet of four letters and sort of the properties of that are all fairly similar. It’s all negatively charged.

In protein, the alphabet’s 20 letters. There’s a tremendous level of sequence context. And without PCR, to sort of amplify the low abundance things in proteomics, you have this dynamic range of 10 or 11 logs of dynamic range. So the problem is extraordinarily hard. So I think today our technology is used in some of these more targeted applications.

As it scales over time, it will become a de novo sequencing platform as we bring out our new sort of next generation platform and continue to improve the level of coverage. So I think there will be a point in time when those will mirror more, but it’s quite a bit more difficult to get from zero to what you might think of in terms of like being able to just drop in a sample and do a whole genome and DNA. I think that day will come for proteomics. I think it’s just we’re not quite to the beginning of that run. But our belief is when we get to that level of performance, we’ll see something very similar to what we saw in DNA, which is when that capability was there and when it was there at a declining cost, it sort of opened up lots and lots of applications and capabilities.

So we view it similarly. We just think the journey is a little bit longer to get to that sort of ubiquitous level of capability that we see today the DNA world.

Lily, Life Science Data Analyst, UBS: Got it. Sticking with that point, so what will be the cost point that we’re able to unlock the market demand?

Jeff Hopkins, CEO, QuantumSci: You know, don’t know that anyone knows that cost proteomics sort of a fascinating market, having spent a lot of time in the DNA world. In the DNA world, you can walk in and talk to a customer and talk about their cost per G or their Obviously, now some companies want to talk more about the total cost of a workflow. In proteomics, it can be all over the board. You could have somebody in a small basic research lab maybe doing Western blots and spending $50 or $100 You could have somebody running a big panel of an affinity based platform that’s spending several 100. You could have someone who owns a million dollar mass spec and then only spends a couple $100 in an So there’s very different there’s not like one uniform business model in our space, I think, is the more concise way to talk about it.

There’s some areas that are more reagent heavy and others that are more capital heavy. I don’t think right now pricing is what’s holding the market back from sort of that ubiquitous run like what DNA had. I think it’s more about the technologies available to do this aren’t yet as ubiquitous to do whole proteome or sort of do de novo in the proteome.

Lily, Life Science Data Analyst, UBS: Got it. And maybe talk a little about your pipeline. Anything that you get very excited in the next twelve to eighteen months from your pipeline?

Jeff Hopkins, CEO, QuantumSci: Yeah. So we’ve you know, over the last two years that I’ve been here at the company with the management team that’s in place now, we’ve really been sort of iterating and improving upon our technology at the clip of about every six to nine months, improving the sequencing coverage, the sequencing depth, the number of amino acids we can see, also sort of the amount of sample you need. So we have programs across all these different areas, and I think the big program that is slated for a 2026 launch is a platform we call Proteus. So the easiest way to think about it is our current platform. Essentially, the optics are in the chip.

It’s CMOS based, small benchtop instrument, pretty simple in terms of what the instrument does and sort of the brains are in the chip. The Proteus platform flips that architecture. We go to a very simple consumable, we put the optics in the machine, and just as one example, our current chip has 2,000,000 of these nanowells. So you need more and more wells to take on more and more complex samples, very similar to DNA. The Proteus platform, a chip of the same size, first generation, will have 80,000,000.

And it will put us on an architecture that we can scale that through both the consumable size, but also going from point and shoot to scanning over time. We’ll be able to scale that up into the billions of wells. So this architecture change is key to not only unlocking some opportunities that people would use the tech for today if we had that, but also putting us on a clear path to be able to scale to that de novo level of output that we’re going to need. So it’s a key program for us. We expect to show data for the end of the year on our prototype machines and then launch in the second half of next year.

Lily, Life Science Data Analyst, UBS: Got it. Great. The next question, kind of like a mandatory. AI is kind of a mandatory question for every now. So same for you guys.

What’s the role of AI in your companies right now? And how important is that? And then how will they involve in the next few years?

Wenbin Zhang, Doctor, Cytec Biosciences: Go first. Yeah, sure. I think there are two aspects of the AI. One is on the technology side, how it drives our tool, drives the application. And second part is on the operation side, how AI can help improve our overall efficiency of the company management.

We are doing both. A year ago we launched one of the software tools to support our image stream on the data analysis side because AI really is a great tool to help improve the analysis of imaging and simplify the process and make it very efficient and fast. We are continuing to try this application from across our old platform on the data analysis side. In fact, one of the tools earlier I mentioned about SciTech Cloud, and very popular within the Cytec Cloud there’s a tool called panel design. In fact, it has implemented the AI features.

Typically before, it takes about weeks or months to design a panel and to use flow cytometer. Now with our panel design, with AI implemented, it can automate the process, make it very fast. Pretty much few hours, you can have a kind of optimized panel out there. So the second part is help to manage the operation overall, and we are working on it. Then across all our organization, we are looking at using AI to help improve operation and help to scale up our operation and management.

Jeff Hopkins, CEO, QuantumSci: So I’d agree. I think operationally, if you haven’t implemented it in your company, you’re probably over resourced in some areas. You know, everything from efficiencies of how you run meetings, convert that into meeting minutes into a timeline. Like, these things can be sort of automated through pipelines. Market research departments should be able to be automated into custom GPT.

So I think if you haven’t done an enterprise level adoption of AI for business process and business systems and different tools, you’re probably overspending on some of those resources. I think when we talk more on the product side, we talk about the tool, but then also when do we have a proprietary set of data to train on. And I think that’s an element that not everybody has sort of completely understood yet at investor level. And what I mean by that is we’ve historically used off the shelf AI tools to design our recognizers that bind to those amino acids. These are engineered proteins.

They do not exist in nature. We’ve used classic tools, computational tools, directed evolution. But what we’ve done more recently, we just talked about on our call and expect to release some more info. So we have a collaboration with NVIDIA in this area. And what we’ve done more recently is actually train those AI design tools, but on our proprietary database of over a million different candidates where we have inserted mutations, we have information about binding kinetics, about how they sequence, about how pure they are when you make them, stable they are.

And we did a cycle on that recently, and in one cycle of AI design, we saw a 2x improvement in the coverage of an amino acid that historically that would have taken many cycles. So we think the tools it’s not just the tool, it’s also when do you have proprietary data to train it on. We use it similarly on the analysis side, so our entire kinetic model is AI sort of derived and we can take in all the sequencing data happening in the field and internally and retrain that sort of regenerate that database on a frequent basis. So we do that a few times a year, and it’s a way to sort of continuously improve the product just by learning about all the data that’s been generated. So we use it sort of in an external way, but also in an internal way trained on our database.

Lily, Life Science Data Analyst, UBS: Got it. That’s very helpful. We only have a few minutes left. Any questions from the audience? Great.

If not, then we’re going to take a break. And thank you so much for joining me on the panel. And if you guys have any questions, we can certainly delay off stage. Thank you so much.

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