All right, guys, thanks so much for joining. We are starting now our innovation across drug discovery, molecule synthesis, and precision medicine. We've thrown everything that we possibly can- -into the title. Hopefully, we've encapsulated enough. I'm Liza Garcia, UBS Life Sciences Tools and Diagnostics analyst. I'm being joined by my co-coverage, John Sourbeer. Then we have Sean McClain, the CEO of Absci. We have Patrick Finn, the Chief Operating Officer and President of Twist. We have Anna Marie Wagner, SVP, Corporate Development at Ginkgo, and we have Ross Muken here as well, the CFO of SOPHiA GENETICS. We have quite a number of companies to kick it off, and I think we'll start on some broader themes, then we'll drill down to the company-level questions. Just to start it off, and obviously, maybe we'll go down the row from Ross, left to right. All right, a broad one to start. Just thinking about utilization of data and new technologies to better deliver on molecule synthesis, drug discovery, patient care, you know, why don't you kind of, Let's start with a quick intro, actually, just so that everybody can kind of get to see and how you. A high-level overview of how your company sort of, you feel like you're tackling these areas in space. Perfect. Thank you. Thank you for having us. Here at SOPHiA, we're really focused on what we would consider data-driven medicine, right? This is the incorporation of, you know, a community insights around the globe into all elements of the care continuum, including obviously, clinical care at the bedside, if you're an oncologist, but also in the drug discovery and drug development and commercialization process. For us, it's really around the animation of data at scale. We started in genomics. Now that we've moved into what we would consider multimodal data, we can support radiology, so images, EMR data, pathology data, any type you want, being cleaned and organized, right, in a common data lake, and then used for the purposes of drawing insights, whether you're trying to figure out, you know, the correct patient population for your new IO drug, or how, what therapy someone should be on, right, in a category where there's many options, or if you're trying to figure out why I had super responders or non-responders in a given area. There's a full spectrum of, of use cases, but for us, we're trying to embed this collective intelligence and the power of being in, you know, 750 institutions in 70 countries in the world, producing that data at scale and, and what you can learn from it, and, and the algorithms. We have the power that enable that in, in a more predictive way to aid in all elements of what you, what you sort of described. That's, that's SOPHiA in a nutshell and how we're positioned in this space. Great. Great, thanks. At Ginkgo, we're really focused on the early side of drug discovery, although can also play across the value chain. Our mission is to make biology easier to engineer. What does that mean? It means making it predictable, making it more like an engineering or science discipline than just an art. The way that we approach that is really, on the one side, trying to decouple the R&D process from manual human labor so that you're driving scalability, accuracy, and quality data generation at a lower and lower cost over time as you scale. Then on the other side, capturing that data to better inform future experiments and to create reusable architectures and reusable biological parts, if you will, so that we're not recreating the wheel every single time. Our view is that one of the things that's really held biotech back over the last 40 or so years, since we created our first major biologic drug, is the fact that the tools, techniques, and IP that is enabling the industry is not being shared, and it's being siloed in companies that are focused on individual medicines in their individual space. The reality is that a lot of that learning can be cross-applicable across many different areas. So Ginkgo is working on bringing that together and bringing together the toolset that is required to de-risk the process of doing R&D in this space. As it relates to data, in particular, we do view data as one of our core assets, and the, the Foundry, this, this automated lab is, is the core tool that we use to then generate that data. We're able to apply that data in, in many different ways, from creating better predictions in our traditional kind of AI and ML tools, to design the DNA sequences that we're, we're testing for our customers, and to create very specific applications that we're then able to use across the platform. Then we're able to use the Foundry to then test the quality of those predictions and iterate those tools over time. I think it's a very, very interesting time to be sort of in our sector and at this sort of confluence of big data, the ability to generate lots of data at a lower and lower cost, and then use some of these advanced tools to be able to really drive the space forward. Okay. Thank you. You're welcome. Great intro. Tough to follow. We, we also buy a lot of DNA from this guy right here. We're very grateful for that. Yeah. Yeah, Twist Bioscience, we have a, a truly disruptive DNA synthesis platform that we, we think has really opened up the application space. I think to describe the scale, we're capable of synthesizing tens of millions of DNA building blocks every day, which is opening up all sorts of interesting applications and, and business areas. For us, that's in synthetic biology, whether it's a, a clonal gene that's used by Ginkgo, and with just prodigious volume, in pharma for antibody discovery, it's all the nucleotide pools, feeding the gene editing space, libraries for antibody discovery, you know, any product at all in the industrial biotech segment to break out of our dependence upon oil. We're, you know, enabling the community there. For next-generation sequencing, we have a target enrichment product and some workflow solutions that really, truly maximize how people use sequencers. What we've learned over the last few years of that product is that, you know, the clue's in the name. Customers want to customize their, their assays. So that incredible scale allows customers to get onto our platform and build the assay that they need for applications like liquid biopsy and other research applications. We have an antibody discovery program underpinned by our ability to make incredible amounts of molecular diversity, whether that's synthetically. A recent acquisition of a hyper-responding mouse allows us to create even more diversity, then we'll have a layer of AI, so machine learning, that helps with really improving the quality and the execution of those designs and ultimately the final products we produce. Last, but by no means least, and pre-revenue, is a really interesting application, where we can take what we know in DNA synthesis and really truly scale it up orders of magnitude from where we are today, to then incorporate that into the DNA data storage space, which although it sounds like science fiction, you know, we're all living proof that DNA is a stable data storage medium, and we're starting to see the sort of cold data layer. Or we're seeing the proof of concept experiments, where people are now starting to store modest bits of data in DNA. Over to you. Yeah. Thank you, Patrick. By the way, I like your socks. I need to get some protein socks. I'm Sean McClain, founder and CEO. At Absci, we're a generative AI drug creation company, really going from this paradigm of drug discovery, where you're searching for a needle in the haystack, to drug creation, where you're actually creating the needle, in our case, a biologic. Now, there's been a lot of, you know, buzz around, you know, generative AI, and, like, what does that actually mean when it applies to discovery of biologics? Maybe we can just take a step back and look at how, you know, how have antibodies traditionally been discovered? I mean, Regeneron was really the first company that, you know, developed humanized mice. You know, you, you inject a you do immunization, you inject a mouse with a target, the mouse generates the antibodies. Well, the issue with that is you have no control over what the mouse gives you. The mouse, you know, will bind to a particular epitope of interest, it will have a particular affinity, it'll have, you know, developability parameters, but it may not be what you want. What we've been doing here, at Absci, is using generative AI to actually specifically design antibodies with the attributes that you want. You know, hitting the particular area of the target you want, having the affinity, the developability, and manufacturability, and, and ultimately, that's what's gonna, you know, dramatically decrease the amount of time it takes to get, you know, new therapies into the clinic and, and increase overall success rates. 'Cause we have these, you know, very challenging targets that, that are out there, GPCRs, you know, membrane protein, you know, GPCRs, ion channels, that are really hard to, you know, hit with traditional means. If you can hone in with, you know, generative AI to be able to hit the specific area that gives you the biology, gives you. You know, has the affinity that you want, that gives you that particular biology, that's what's gonna start unlocking that new biology and increasing overall success, in, in the overall clinic. One of the ways that we've been successful with, generating, this, you know, generative AI platform is exactly what, what everyone else has been talking about: synthetic biology. We have this platform that allows us to generate, you know, billions of protein-protein interaction data points that we use to train the model, then we can also then validate the, the, the model in the wet lab, you know, looking at over 3 million, unique AI-generated, designs, in a given 6-week time period. That allows us to, to, to train our models, validate them, and make very rapid advancements, for being able to use generative AI for, you know, de novo design or creation of antibodies. All right. Great. A very diverse group, but I guess let's start with numbers. As you guys think about kind of your companies and obviously the challenges you're addressing, how should we think about kind of as if, if you have any frame of reference or numbers, the potential cost savings that you're trying to deliver here, and the and how to think about kind of output and, and, you know, obviously with data generation becoming more and more complex, how to think about kind of addressing how you're addressing those issues and kind of what you think about in terms of solvability for your customer set? I think we'll start with Ross, and then we'll go down the line again. Then, Sean- Sure. I'll go the other way. You know, cost savings for us, can be a couple different ways you think about it, right? On the core business for us, where we're helping with sort of the production of genomic information, at the sequencer side, right? If you think about an average, you know, LDT or other diagnostic, you're talking about probably reimbursement, let's say, in the US, and we're global, but in the US, let's say it's $3,000. Of that, the sort of prep and bioinformatic cost that Pat and I sort of help address is probably $1,000 of that, right? It's a pretty big number. However, you know, that's based on, you know, folks with their own bioinformatic teams doing it internally, and maybe they're using one of your competitors that's not as at scale as Pat. You know, if we come together or, or we can bring to the market a, a combination solution that allows for, you know, AI and ML and other pieces to automate some parts of the labor, then scale economies to lower the chemistry cost, you can take that down pretty materially, right? I think most of us know that a lot of the, the labs don't, you know, make money yet, in even at scale. Aiding on that side, just purely from an industrial standpoint, can help in the efficiency, and I would say ability to scale to many more patients, no matter the institution, and can also flex down as we look in emerging markets to price points. We were talking about before, India and other places, Africa, where, where you have to be at a very different cost level to be able to do that, right? There's that part of savings, but I would say the, the sexier part for us. You know, when you think about today, and again, you know, a few folks were talking about the clinical trial process. You know, as most of you know, if you look at a Kaplan-Meier curve, right, there's responders and non-responders. What if you could know who would fall into what group before you recruited them into a trial, right? That's what part of what we're trying to solve with, in a multimodal datasets and multimodal signatures, where you're taking genetic information, information out of the image, information from the pathology scan, information from the EMR, putting that together, creating a specific signature and algorithm, and trying to figure out, all right, can I predict how someone will respond before they go into trials? There's a lot of use for that different information. If you think about the cost of discovering a drug today, or you think about trying to get more and more targeted with therapies, that's the kind of information you're going to need to be able to take those decisions, right? I would say, you know, it's hard to put numbers around that, but we know the cost, you know, of a failed drug is incredibly high, and the cost to a patient, right? Imagine it was, you know, a friend or loved one who was in that pool that we knew was gonna be a non-responder, that's even more devastating in terms of the cost, right? I think there's the aspects, obviously, on the data side of being able to identify right patient, right therapy, right time, but then there's also the, the piece of it where, you know, for the system, that's also a huge savings, as folks aren't getting unnecessary therapies or other treatments that we know ultimately won't be successful in the end, therapeutically. That's super helpful. Just before we move on, the 3,000 number was really helpful and kind of the 1,000. Do you have any sense of, or any numbers around, you know, the setup of the clinical trial and choosing patients and what that, that number of that spend for a company might be? It's huge, right? I mean, I think if you had the CEO of most pharma companies in today, and you asked them what is their greatest challenge or one of their greatest challenge, it's opening up new sites, going into new markets, moving into new postal codes, bringing it to new parts of the world where there's naive patient populations. It's incredibly costly, right? One of the challenges there has also been, you know, while they're great, and some of us are our partners, the central lab model really was born out of the US. Serving many other parts of the world in a centralized fashion can be quite challenging, right? In the US market, it works reasonably well. We think a hybrid approach of central and decentralized, right, can help on that. You know, for us, part of the excitement is, you know, we can look... I can pull up my phone now and tell you what patients have walked into what institutions in what part of the world and been tested, you know, within the last 24 hours or even less, right? Having real-world, real-time information at your fingertips, I think on that, and the ability to then recruit patients based off of that when someone comes in and not relying on the clinician, can be something quite powerful. You know, we really need to get the scale of adoption of the technology all over the world and be able to get the cost down to where it's, it's viable. The, the savings could be tremendous because it's one of the biggest, you know, challenges to most pharmas in terms of the overall, drug development process. Great. Thanks. I'm gonna piggyback on your, your term, like, the sexy place to, to save the money, 'cause I think we, we sort of have these two opportunities. For us, the real magic happens when we can turn it into our business model. The, the sort of unsexy side, if you will, is like, we're gonna drive down cost to do R&D. That is our core kind of technology offering. The foundry is, you know, working with folks like Twist and benefiting from their scale economics as they, as they, as they advance, using automation, et cetera, to drive down our costs. That's like the unsexy stuff. It is table stakes. We have to do it. Even as that has been happening across the industry over the past decade or two, the cost to develop new drugs, as an example, has been skyrocketing. Yes ... despite the advances that have been made in the underlying technology, and it makes, in many ways, absolutely no sense. What Ginkgo has now been able to do in certain areas of our business, and what I would love to be able to continue to do, is offer a completely different value proposition to the market. We just launched for our enzyme engineering business, so think, you know, biocatalysis and pharma, as an example. Certainly there's a large industrial enzyme segment. Enzymes are used, you know, across many, many different applications, whether it's a component of a, of a small molecule manufacturing process or a drug itself. We can now offer the market success-based pricing for that. You don't have to pay us $1 unless we deliver you a successful enzyme that meets your spec. We have to underwrite that, but we have now gotten our platform to a point where we can offer a value proposition that nobody else in our industry has because we have made it predictable enough and affordable enough to do that work on our platform. Imagine if we could do that in gene therapy or in cell therapy or in traditional biologic drug production. That would be absolutely game-changing for the industry, and that's really how we think about developing our platform, is being able to completely change the paradigm from, "I'm just throwing a bunch of R&D... of money into an R&D problem that will probably not be solved," to, "I am paying for a product that works, and now I just need to figure out how to get it to patients and get it to market," which is a much different, a much different question and value proposition. To me, that's what's really exciting about how we're able to, to really influence the field. Great. It's tough to build on that. Those of you that haven't worked with Ginkgo- If you want to give us success-only pricing, by the way. That's right. That's right. You can feel the pressure on price every minute of the day. The, the, the comment's absolutely right. Just in, in trying to, to, swat up for, for being on stage, I think it's something like approximately $60 billion of R&D budget wasted in failed drugs. Yep in the oncology space, which is meaningful. When you look at that, you know, I, I see Twist role here. We're, we're an enabler. We're picks and shovels, so people will come and go, and, you know, they'll always need nucleic acid-based tools. I, I think what's crucial here is. Well, actually, there's, there's three things that matter. Quality, so if you're making a nucleic acid-based anything, there's nowhere to hide. You either made the right molecule or you didn't, and that, that is hard to get right. That's taken us 10 years to hone that game. You have the quality component, you have a scale component, just as a reminder, tens of millions of oligonucleotides every day, and the third part that we need there is speed to truly enable. That, that's something we're working very hard on building. Speed's huge. Absolutely. What we're seeing now, if you think about the true precision medicine solution. There's a couple of things you have to do. You have to read the disease. You have to understand what that, that means, and you're probably gonna bolt on some other modalities to understand what's going on with the disease. A precision-based therapeutic is gonna come from, you, you know, one of us here, and, and a few others, obviously, but it's gonna be underpinned by genetic content. That speed to right effectively, and then get to the point where whatever biomolecule is going into the patient can be made as quickly as possible, truly matters. You know, often we've had it described to us as sort of needle to needle in, in days rather than years. For the true precision approaches to solving complex disease, we really do think that scale and speed and quality is going to matter. Yeah. We're really focused on solving two, two problems, which are, you know, big cost savers for our partners. The first is time to clinic. It takes, on average, 5.5 years to get a drug into the clinic. We're gonna be putting in the first, you know, generative AI-designed antibody in the clinic within 24 months. Going from 5.5 years down to 24 months, that's a huge time saving. That time savings, yeah, you get, you get it to patients sooner. Also, if you look at the patent cliff, you actually get, you know, additional 3.5 years of patent life, and that's huge for biopharma, our biopharma partners. Then I would say that the, the second big, you know, savings is, is being able to increase that, that overall success rate. It's about 4% success rate from start, from start to finish. If we can actually start designing drugs that have the attributes that, that, that we want, that's what's gonna start increasing success rate. Additionally, you know, at Absci, we have a, another novel target discovery platform that's based on reverse immunology, that's actually delivered new novel targets to go after. Not only do you have to design, you know, better molecules to increase that overall success rate, but we also have to be looking at new novel targets to, to be going after as, as, as well. I would say with our platform, what gets pharma most excited is being able to use this to discover brand-new novel biology, and that ultimately is the value prop that, you know, brings in the, you know, the large upfront payments, the milestones, the royalties. At the end of the day, pharma CEOs are looking to, "How do I continue to develop my pipeline to get first-in-class, best-in-class assets?" That's really what, you know, we're, we're delivering on at the end of the day. It's increasing, you know, the success rate, but and decreasing the amount of time it takes to get these therapies to patients. Thanks. You know, maybe, Sean, starting with you and going back the other way. You know, when you think of that next stage of genomic analysis or, or, you know, molecule synthesis, in your view, you know, where do you think companies are going to differentiate to gain a, a stronger foothold within the market? Yeah, absolutely. I, I, I think it goes back to being able to do things that traditional technologies cannot do. If you... you... Again, looking at, you know, phage display or immunization, you know, like, that can only get you so far. Being able to use, you know, technologies like we've been talking about, like generative AI, to be able to design, again, molecules that create new biologies that current technologies cannot deliver on, That's really where, you know, we've been focused. Again, it's the focus on new biology, being able to create, again, first-in-class, best-in-class assets for our, for our pharma partners. That's where I feel like the, the traction is going. Ultimately, everyone has, you know, a, a better mousetrap out there, whether it's, you know, better phage display, better, you know, immunization. If, again, you can actually design something that, you know, no large pharma or other company can, can design and create, that's a huge value prop for, you know, pharma, and I feel like that's ultimately what gets these new, emerging technologies into pharma, especially in this type of environment. It's a good comment. You just come back to speed and scale for Twist. There, there's lots of great ideas. You know, we're incorporating AI, ML ourselves into some of our designs for, for smarter design. At the end of the day, we still have to produce a high-quality molecule for testing as quickly as reasonably possible. Now, that's absolutely fundamental to, to the industry. That, you know, that's where we'll focus our efforts over the coming few quarters and really support, you know, the, the folks chasing the big discoveries, like with Sean and, and Ginkgo, and obviously on the reading side with, with Ross, and we will push it forward. Yeah. You'll never hear Ginkgo bet against speed and scale, but I would add a third... I'd add a third component to it, which is really around IP and this ability to try to democratize access to tools. I think, again, in the last 40 or so years, as IP has developed, let's say, traditionally in an academic lab, company gets formed around that new technology and looks for a nail for that hammer that's been developed. Turns out you need a lot of different tools to build a successful drug, not just a hammer. like, just to put an example out there, you could have one company that's done some amazing work developing a therapy for a particular, let's just say, cardiac disease, and another company that's developed an awesome vector that targets the heart, but has no idea how to develop therapies. You will never see those companies cross-license to each other. Instead, you'll see them try and largely fail to fill in the gaps around their core technology, and you end up with a bunch of suboptimal drugs trying to make it to clinic and through clinic. Ginkgo has approached the market with the mind that if we're going to advance this field, we need to bring together the tools that customers need, especially as therapies get more and more complex and more advanced. No one vertically integrated therapeutic developer is going to have the breadth of tools they need, especially as the market continues to evolve. One of the things Ginkgo is really focused on, in addition to being able to just experiment faster and cheaper to develop new technologies, is bringing together that much wider toolkit, so that when you come to us. No, it's not that we're trying to sell you the best little mousetrap for the one little thing and give that to you exclusively. It's that we can really integrate a wider solution so that you're really filling all the gaps that you might otherwise have, and we can incorporate whatever you think your special sauce is, but we can help round out the program to optimize the chance that you make it, you make it through successfully. Again, it's really about bringing together tools and not just trying to optimize around one particular technique. I think I'll build on sort of the, the comment around scale, because I do think scale, particularly when you're talking about the problems we're trying to solve, is incredibly important. I think for us, as we step back, there's a lot of amazing technology in the market, and there's a lot of impactful potential modalities. In most institutions, right, whether it's pharma or it's academics, or central laboratories, that data and those modalities, even within the same institution, is siloed and, and sort of not, unlocked, right, at, at its, at its greatest power. So for us, you know, I think in terms of the ability to differentiate and where the future is going, you need to be able to touch these institutions across many different, data modalities, but in a way that, you know, you get true network effects, right? This hasn't really existed in healthcare before, right? You know, people kind of kept their data siloed. They used it for themself and their own sort of R&D purposes. The reality is, if we really want to do much of what we're talking about on the stage, it needs to be unlocked and unlocked at massive scale, because the only way AI and machine learning and all these different techniques work is at massive data scale, right? What we're looking to enable is sort of that collective intelligence network around the world, where data streams, no one owns it. We don't believe we need to own the data, but we can control it and enable it to be safely utilized and transferred. By doing that, it'll enable more production of it because people will know it's safe, and it will have good use. Our view is, the more data that is produced and the more usability of that data at scale around the world, right, in a diverse global population, the more that each of us will be able to ultimately do more of what we want to do, which is find new discoveries and extend life and help people live, right? Which is a lot of what the end goals of our, our customers, are, because at the end of the day, there is a patient, right? In all of what we do, in at least on the drug side, there's an individual that, you know, is afflicted with something, and we need to find them a solution to be able to live. Sure. Great. I think we're gonna transition to the company-specific lightning round. Ross, since you're next to me, we'll start with you. Then I think John will have a couple as well for some other com- All right, why don't... I mean, you've talked a lot, but I guess kind of let's talk about the portfolio that SOPHiA brings. You have your roots kind of in advanced cloud-based genomics, data analytics. You talked about kind of the balance between the decentralized and centralized models. Can you bring us to kind of where you think about the portfolio today and, and kind of the, the pieces that it's tackling in terms of the, the data analytics side? Sure. You know, we started being able to support any precision medicine application in oncology or rare disease. That was kind of our core backbone. You buy whatever sequencer you want. Hopefully, you buy Paddy's chemistry, and then you use and create your own test, and we can support that at scale, right? That was sort of where we were. Now, as we think about sort of some of our newer solutions, as we think about CarePath, right? That was sort of the core DDM for genomics. As we think about CarePath, which is more of our multimodal tool, now we're taking that great genomic data, we're, we're generating and enabling it to be comboed with other modalities to, again, lead to, let's say, more conclusive outcomes. An example would be what we're doing with our DEEP-Lung study, where, in the case of several thousand non-small cell lung cancer patients, you know, we developed a very complex signature, using our proprietary algorithms that can be, let's say, 80% predictive in response to a PARP inhibitor. Before, you know, scan 1, that happens, we can tell you what the outcome is going to be for that patient, where they fall in the Kaplan-Meier curve. You know, imagine that across many different indications and diseases. That's kind of where we're driving and where our portfolio is going. We've talked about that as the balance of, let's say, our clinical customers, which are mainly labs and, AMCs and others. You know, as you think about CarePath, primarily the customer there is pharma, who's using a lot of what we're talking about there. For us, that's where the portfolio over time is going, to where those two pieces balance a bit. But they also feed each other, right? Because to the degree that pharma wants to deploy a decentralized solution or tool in the market, it needs to be deployed in the clinical market, right? They want to sort of improve upon that and have that in a decentralized world, so they can have data at scale, and then that helps those customers, which feeds back into data pharma. That's the flywheel we're trying to enable. Great. All right, we'll move it around, but I think we'll go to Paddy now. Factory of the Future's up. Congratulations. Absolutely. Factory up right now. Yeah. We're very, very proud of the achievements to bring the factory to scale. From a commercial standpoint, obviously, we've got the technology working over the last few years, the products certainly around the sequencing side and the synthetic biology side of the business. We've done well in showing that we can take a segment of the market. We can actually reach into high volume users and compete very, very effectively. Second part, to ensuring that the entire globe has access to our platform is scale. Sorry for the theme. Adding the Factory of the Future has really allowed us to, one, up the number of different nucleic acid sequences we can synthesize, but in addition, it's gonna enable us to drive much quicker to product to the customer. Factory is up and running. We have a big investment in making a super-fast gene happen, which is gonna open up the, the pharma drug discovery space. It's gonna keep one of my very, very important customers to my right here very, very happy, and will also allow us to drive into the academic segment and to just open up interesting new areas of research as well. That's good. I'm really very privileged to serve with the team to, to make that happen. Then the other thing it's going to enable for us is what, what do we do downstream of DNA synthesis? There's a whole value map of what a customer does once they have a piece of DNA. Again, our job is to enable the community, is to enable companies like Ginkgo, SOPHiA, and, well, we'll talk later. You know, to do a very, very good job, make sure we can, we can stand behind them. You know, that downstream product mix will, will make us a, a very valuable partner as we go forward, and it just needs square footage once you've come off the microfluidic chips. Steady progress. Thanks. One, one for Anna Marie. You know, when, when you think about Ginkgo's approach in terms of unlocking productivity gains, you know, where, I guess, is the sweet spot when you talk about cell engineering and the flywheel you're creating there? Any way just to come, you know, talk about or quantify the number of programs or targets that you're working on? Oh, man, I think our sweet spot is insatiable. Yeah, again, our goal would be the. Let me, let me step back for a second. We have a little over 100 active programs on the platform right now. Some of those programs are multi-tens of millions of dollar bespoke programs, where we are working hand in hand with a customer to figure out a brand-new scientific challenge. Others of those programs could be six-month enzyme engineering campaigns that are largely solved by AI, and we run a few experiments in the lab to validate that we've met the customer's spec, and we hand it back relatively quickly. My goal would be that over time, the projects today that feel like big, 40 million dollar bespoke, messy projects become what the enzyme engineering projects are today. As we continue to work in these new areas, we get better, more scalable, faster, more predictive with our algorithmic tools, and we can take stuff out of the lab and bring it into a more predictive, more predictive space. That should allow us to work on not just 100, not just a few 100, not just a few 1,000, but tens, I mean, all the programs. I would like to make it as easy to make a new drug as it is to write a neat little piece of code for a website today. That, that is, that is really our vision, and to make it such that if you've got an idea, you can create it, and you're not limited by the science. You're really only limited by whether it's gonna actually add any value to the world. I, I, I don't think there's a, there's a sweet spot. I think if the question is more, at what point do we start to reach kind of operational profitability or something like that, that's a little bit more investor-driven, I think we're, we're solidly on that path. We've, we've now made the investments in the platform that allow us to really now take advantage of efficiency gains as we add more programs, just on the upfront economics that we receive from our customers. Again, I think our vision is that as soon as we get products more to that predictive state, where we can really rely on the downstream economics we get from the customers, we would like to give away the platform as cheaply as possible to get as many programs out in the world as we can. There's so much more value for us to capture as well when those products are successful, and they make it to market. Our, our ambition is, is insatiable, and, and while we're solidly marching towards, towards kind of profitably, profitable unit economics today and a profitable platform today, I'm much more excited about the, the opportunities when we cross that, that threshold for us to go beyond that. Thanks. Sean, how should we think about the benefit of leveraging AI to improve the clinical discovery, and any financial aspects or commitments you can share with us there? Yeah, no, absolutely. One of the, you know, marquee partners that, that we have is, you know, Merck. Last year, we closed a $610 million deal with Merck to discover three new AI-discovered antibodies to three targets. Really, the reason why they had, you know, reached out to us was, again, that, that ability to, you know, specifically hit targets that they, they previously couldn't have hit themselves, as well as actually using. We have a non-standard amino acid platform that allows us to do click chemistry of different payloads for ADC development, as well. Being able to make a, a next, you know, next gen ADCs, that's really been a, a, you know, a huge kind of, you know, value driver for, for, for us, in addition to the AI drug discovery, is being able to kinda create those, those new modalities. Again, the, the AI is, is really being used to specifically target the attributes that, that, that you want. 'Cause a lot of times what you see is, is drugs that, you know, have actually very, you know, promising functionality actually never make it into the clinic because you, you, you can't manufacture it. It doesn't have, you know, the, the, the best, you know, let's say, you know, half-life or the best PK, PD, PD properties. Again, if you can use AI to hone in on all the attributes that, that you want, the functionality, the, the manufacturability, the developability, that's what's gonna, you know, increase that overall success in the clinic. Great. All right, back again. Another round. Ross, start it off. You know, you've talked about traction in some of the initiatives like HRD, liquid biopsy. Can you help frame kind of the opportunities that you're seeing there? Then, kind of a more thoughtful thought, how do you think about, like, comprehensive genomic profiling and the uptake curve, right? I think right now, people have said it's about 30% of advanced cancer patients in the US are actually kind of even getting CGP. Kind of how do you think about the evolution of that? Sounds pretty high, by the way. I think it's probably lower, but maybe that's a U.S.-specific comment versus- Yeah the rest of the world. I mean, I think most parts of the world, and again, we, we address the globe, the access to many of these, I would say, advanced products are, are much less, right? I think, you know, from our perspective, when we take a step back, you know, the market grew up over the last number of years on small panels, right? It's still primarily what's being used across the board, but I think if you think about it from pharma's perspective, right, and the type of information they wanna have for their drug discovery purposes, or drug development purposes, bigger is better, right, in terms of the amount of data you can get on the patient. I think a lot of this is now around how you can push into some of these newer areas with larger panels and more content, and be able to drive that information in a way that can then inform on what you're doing next. You know, HRD was a great example in terms of that relative to, you know, the, the, PARP market, right, which has been exploding on the, the pharmaceutical side, and you can look up some of our, our partners of where we've helped them there. You know, they're still sort of just scratching the surface, given the number of indications, you know, those PARPs are, are likely to expand into of, of where we are. So we're very excited about HRD on a global basis, including in the US, and think, you know, there's still quite a lot of legs to go there. I think if you think about that story versus the others, it's not too different, right? I think in general, you know, if every pharmaceutical company can have a CGP or a whole transcriptome or, you know, a liquid biopsy available for every patient, you know, in some areas they would want it, right? Particularly, you know, liquid's really useful as you go to many parts of the world, just given the challenges on tissue, and storage, and sampling, and other elements, right? Blood draw is a more, I would say, easier way to do it. In our mind, it's where the market's going. You know, if you think about for us from a business model perspective, it's why our ASP keeps moving higher, because it's higher and higher value, you know, sort of tests that are being computed, where the amount of information we're processing on our platform keeps going up. You know, we're still not even scratching the surface, particularly on a global basis, of any of those tools in terms of their penetration. I think what you're gonna start hearing more and more is, you know, more post codes or more zip codes in more parts of the world for all patients, right? Equal access and how you get into different geographies, and that even is in the U.S., right? If you look... That's why I was wondering on, on the adoption, 'cause I think that's maybe the addressable market within major metros in the U.S. If you go out to the community or you go out to the more rural parts of the US, you're definitely not seeing CGP testing, right? I think, you know, as we just think about that availability, et cetera, that's what we're trying to allow for, that all populations get access, and I still think we're probably 5, 10 years away from that reality, but the market's moving there, and I think for everyone, including us, that's a positive. Hopefully. CGP for everybody 10 years from now. Yeah, or even, you know, and again, we didn't even talk about whole genome, but, like, that's another conversation. And it isn't appropriate for all things or, or exomes, you know, I think Paddy would tell you, are still quite, quite, quite all- Very relevant. Popular. Our growth there was also tremendous, and this is all, I think, a positive development for the market, 'cause it's more information, more data on all patients. Great. All right, Paddy, I guess why don't you talk about... You, you alluded to the DNA data storage and kind of how that's gonna be a differentiate or distinguishing feature for the company. Can you kind of dive into that a little bit and help people kind of understand, kind of the work and how you're scaling these solutions? Sure thing. Again, pre-revenue, you know, I think we're, we're talking about potential product in calendar year 25. It's kind of core to who Twist is as a business. We're an interesting combination and collection of skills in microfluidics, DNA, chemistry, silicon. It's fascinating, fascinating, eclectic gathering of scientists and engineers. The key, quite frankly, is density. It's very expensive to go into DNA as a storage medium today, so our investment and our focus is on really driving the density at which you can write high quality nucleic acid. We've got some proof of concept work coming out in the next sort of 12, 12 months or so that's going to show the end-to-end prototype and workflow, and with a view to having a much higher data, or excuse me, a quite higher density coming through calendar year 2025. It's all underpinned by what got us to the start line, you know, going from the 96-well plate, which is constrained biology for the last however many years you want to look back, breaking out of that onto our silicon chip platform, which is now at tens of millions of oligonucleotides per day. Coming up to this, where it's going to be, you know, many millions of oligonucleotides per run, which again, that's what's going to open up that space. There's a meaningful part of the market segment that really is about cold data or, you know, write once, read never, read seldom, which we do think will. You know, DNA as a medium is gonna make a meaningful contribution to enabling capacity for other, you know, more, more rapid read type applications. We've been here before. We have the right collection of people to make it happen. It's real. We have done proof of concept work in the existing platform with many customers. I think it's just a matter of just proving out the technology, bringing to scale. Great. Anna Marie, I was wondering if you could talk a little bit about some of your recently launched enzyme services. Remind us of the offerings there and just how is that adoption curve trending? Sure. The background for this is that we discovered that, you know, throughout our history, when we had been going to customers and saying, "What is it that you want us to do for you?" They would ask, "Well, what is it that you do?" We would say, "You know, we, we can do anything. What do you need?" You go in, in circles for a little while before anyone actually figures out what we're gonna do together. So we realized it would be helpful if we started going out to the market and saying, "Okay, well, you know, in addition to being happy to discuss with you whatever your needs are, these are a few products that we actually offer." Some of the earliest ones we sort of formally launched were in the protein space, because if you think about it, effectively, any program that Ginkgo works on, whether the product is a small molecule or, the biologic drug involves some element of enzyme engineering inside of a cell. So it was an area where we had a lot of experience, and so, that was the first, sort of service that we productized. We started going out to the market to talk to folks about this offering, and this tool as part of our broader portfolio. We've since launched a number of other formal kind of product lines. Again, there's no real difference to Ginkgo. It's just a way to start the conversation with the customer about what they, what they might need to do. The real innovation, as I mentioned a little bit earlier, has been that in some of these more mature areas now, including enzyme engineering, we're able to offer a very different value proposition around success-based pricing as it becomes predictable and very high probability of success for us to be able to do, and that's something I'm really excited about that's recently launched. Thanks. You know, Sean McClain, how do you think about pharma companies will eventually think of R&D, you know, if this generative AI and the Absci model becomes a part of the standing operating, within pharma, you know, how do you see that? Absolutely. I think one of the things that you're gonna start to see is pharma companies start partnering at a later stage. Instead of, you know, partnering at the target, they're gonna start partnering, you know, at IND. Because what we're gonna start seeing is the costs start to shrink in terms of getting to in vivo validation. Let's say, instead of costing, you know, 15 million, it can cost you $3 million, and you can get there in 18 months. That's what we're seeing as actually a big value inflection point when you partner, not at the target stage, but when you have in vivo validation or IND, you know, you can, you know, start looking at, you know, much higher upfront payments. Ultimately, again, putting on my large pharma CEO hat, they're looking for assets. They don't care how you got the assets, they just want assets that are gonna be first-in-class, that are gonna be highly differentiated, and have the ability to be best-in-class. If you can, you know, with a in vivo model or an animal, you know, model that is, you know, you know, that, you know, pharma believes in, and you can show some, some really great data there, you can, you know, start cranking out IND packages in a, in a manner that you haven't been able to before and capture much more value, but you're also not taking the clinical risk as well. That's how can I see, you know, business models, you know, shifting. I think even Absci is starting to, to see that as well as we've started to build out our own, our own pipeline and, and starting to take things, you know, later and later, and, you know, because we're able to do so, in a much shorter amount of time and in a more cost-effective way, delivering differentiated assets. Great. Well, we're already 1 minute over, so thank you so much, everybody, for joining. This was a great panel and a great discussion. Thank you. Thank you.