All right, great. Thank you. Thanks, everyone, for joining us. I know we got this webcast also. So I'm Vamil Divan, for those who don't know me. Arseniy Shabashvili, also from the Guggenheim side, up here on stage. And pleased to be our first corporate presentation here after the panel this morning with the Absci Corporation. We have Zach Jonasson, the CFO and CBO of the company; Alexander Khan, the VP of Finance; and also, the Head of Investor Relations. So, thanks so much for joining us. It's been fun picking up coverage of Absci over the last few weeks too and getting into some of the conversations. So, we have some questions lined up. If anyone out there wants to ask any questions, let us know. If anyone wants to email me questions, I can look at them that way as well. But maybe just for that, the thing is still a little bit of a maybe shifting story with Absci and sort of a newer story from the biotech, you know, biopharma investors that we speak to. So, and maybe a little bit unique story also with Sean sort of starting this in his garage, so to speak, and kind of building this up to what it is. Maybe Zach, I don't know, or both of you, if you want to just a little bit of the history of the company and kind of where you've gotten to today and kind of the transition you've made over the last several years to what the company is now. It would be a good place to start. Yeah, absolutely. It is a true founder story. He started, Sean McClain started the company in a basement laboratory. I've had a long tenure with the company that goes back to leading the Series A and the Series B financings. I was the venture capital managing partner at a venture capital firm before I joined Absci, and so I've been part of this journey, and I could say it's been a remarkable journey. The company started as a synthetic biology company and was the first company ever, and I think still today, the first company that can produce full-length antibodies in E. coli, and it took that technology and created a screening system out of it, whereby it can interrogate up to 3 million unique antibody sequences. Typically, we do this in a Fab format against a given antigen and look at the binding data as well as some of the developability data at that scale. And around the turn of 2020, Sean, the founder, and the board recognized that these generative AI models, so the first transformer models were coming out of Google at that point with really interesting publications. We realized we could use generative AI approach to design novel therapeutic molecules, but what was missing was data. And that's what we were sitting on, is a way to really create data at scale unlike nobody else could do. And so, at that point, the company made a pivot and began building in AI infrastructure, initially through acquisitions, to leverage the data and build models around that data. If we fast forward to today, we're working with global leading pharmaceutical companies and biotech companies to design novel differentiated therapeutics to their targets of interest, so we have partnerships with companies like Merck, AstraZeneca, Almirall, and smaller companies like Twist and MSK, and in addition to that, I think the more recent chapter for us has been building in a world-class drug discovery team that's led by Andreas Busch, who used to be the head of R&D at Shire and then was the head of R&D at Bayer before that, so our discovery team, I think, is unprecedented for a company of our size, certainly. There's 20 approved drugs underneath their leadership, and so we've integrated that with our AI and data creation capabilities so that we're now generating our own assets as well and taking those forward into the clinic. Okay, great. And maybe just from a sort of investor perspective, stock perspective, maybe you can walk through a little bit of that. You had an IPO a few years ago, and the story again has sort of evolved, I think, and maybe your investor base at the same time has evolved. So maybe you can just kind of walk us through that history and kind of where things stand now. Yeah, Alex, do you want to comment on that? Yeah, so we had our IPO in the summer of 2021, brought in about $200 million around that time, supported by top-tier investors. You know, in 2022, a lot of the market did see a little bit of some headwinds, and we weren't fully immune to that, and we spent most of that year really focusing on refining our strategy, executing on our objectives, and creating value for partners and for shareholders, and the way we saw that play out over the course of 2023 then, where we saw the stock recover, was entering into these large partnerships, high-quality partners with these high-value programs with companies like AstraZeneca, like Almirall, in addition to Merck that we entered into the year before. And beyond that, also creating and building out our own proprietary pipeline of internal asset programs, which we unveiled just a little over a year ago at this point, and showing some data for those. And I think altogether, those were seen as proof points of validation for investors to see these types of high-quality partners coming to us and also seeing the validation of the platform able to create these asset programs and create the data showing that we have potential best-in-class programs, potential first-in-class programs in a really rapid and efficient way. And so, you know, on the back of that, this past year, we went out and raised a follow-on offering of about $86 million, supported by some, again, top-tier investors across the biotech ecosystem and also just a lot of, I'd say, generalist long-only funds who are very interested in the platform that we have too. Not only the assets coming off of it, but also the platform that's able to generate it, I think, has gotten a lot of investors, you know, excited about the Absci story as well. You know, one comment I would make too. I was on the board at the time of the IPO, and I'd say one thing Absci has been very careful to do is before we launched into designing our own programs, our own assets, we really took the time to build the infrastructure. Bringing in Andreas to lead discovery, and then he tapped people he had worked for him before at Bayer and Shire, so we built up that infrastructure before we started moving towards building our own assets and taking those to the clinic. I think that's a challenge for a lot of companies because as soon as you raise capital, typically there's a tremendous amount of pressure to design your own assets and move them towards the clinic. For us, I think we made the right call. We took our time in building out that strength so that we could select the right targets and we could have the right human capital to sort of interface with the AI and make the right decisions on which assets to make forward. And I think the market is appreciating that. Yeah, one thing we just, as a sort of comment from our side, covering the name and kind of learning more about it over the last several months, is the investor base does seem different than what we've seen with some of the other sort of more tech bio names, which have been more tech-driven, you know, good line of biotech specialist investors. So, Arseniy, you want to take it from there? Yeah, so talking about that broader TechBio space, maybe you could talk a little about your platform in terms of its core technologies, the acquisitions that you've made, what possibilities it unlocks on the drug creation side, and maybe what is unique about those possibilities or unique about your technologies within that broader tech bio landscape. Yeah, look, I would break our platform down into three key elements. The first is the ability to generate data at scale, and I mentioned a little bit about that earlier, but it's not just creating the training data. So, we create that at scale, and we use that to build models from, but we can then use that same assay system to do validation of what the model designs are, the predictions. And we run that in an iterative loop. It's a six-week active learning loop, and that's how we're continually improving the performance of the model. So, number one is data, but it's not just training data. It's also the validation data and how you integrate that into an active learning loop. Number two is really recruiting a world-class AI team, and we've done that at Absci. We have AI folks that come from places like OpenAI, Tesla, Google, Harvard, like all the premier institutions that have made great headway in that field. And then the third element is bringing in the drug discovery expertise, and I mentioned that with Andreas and his team already. If we can put those things, if you put all those elements together, we believe that's the recipe for success. And then I would say a fourth element is accessing compute. That's something you can access with third parties, and we have a partnership with NVIDIA where we can scale our compute in parallel with what we're doing on the data side. I guess how should people think in terms of the uniqueness of these capabilities within that broader tech bio landscape, whether they are more on the technology side or, again, I guess the mixture or the confluence of those elements? Yeah, I mean, two comments there. One is before I joined Absci in the role I am today, I was on the investment side and did diligence on a lot of companies in this space, and I think it's very unique to have a data capability. There's only a handful of companies that have data capability that's anywhere in the realm of ours. And I think of data in three ways. One is quantity. So, you have to have enough data to train models. That's something that we focus on. That's the three million potential designs that we can test in a given active learning loop. The other is the quality of the data. Is it high enough quality and resolution that you can train a model around, and the fourth is that, or the third is, is it usable? And for us, we certainly use public data on the structures, but our data is functional data. It's looking at how the antibody design actually interacts with the epitope of interest on an antigen. So it's functional and it's highly usable for the kind of models we create. So that's a key differentiator for us. But at the end of the day, it's results, right? And I think what's been, I mean, really transformative for us is to have been able to show investors how we used this capability to design molecules in our own set of programs. So, starting with ABS-101, and then later this year, we'll release some more data at our R&D Day around how we've applied our models to other programs in our portfolio. And then more recently, or I think about in August, we 8K'd some results in our partnership with AstraZeneca. Again, another proof point where we were able to use our de novo model to design antibodies against a very difficult antigen with no known binder. So, there's no known framework to start with. We were able to use our model to design all the heavy chains and use the common light chain structure and created high-quality binders. We haven't seen anybody else in the industry do anything approaching that in the antibody space. So again, a key differentiator is just results. And then this technology gives you a lot of flexibility in terms of the targets you can go after. I guess any target that you can go after with a conventional antibody, you can develop an antibody against, as well as some targets that maybe traditionally were challenging for antibodies because you have that capability of targeting like an exact epitope. So, but taking a step back, can you talk about your overall pipeline strategy or a higher-level pipeline strategy? Because I guess given all that flexibility, how do you decide which targets to prioritize? Yeah, and look, this is why it's really important to have a team with Andreas at the top. Like we want that expertise on choosing the right targets, and we typically look at targets where we can get good sort of proof of concept in a phase I and that we believe also our platform can design something differentiating into. But from a thousand-foot level, what we're really trying to do with our internal portfolio of programs is balance it and have diversification. So, you'll see right now we have two fast-follower programs and one first-in-class. And so the first-in-class comes out of our target discovery platform. That's a higher risk, but potentially a higher return program. And the other two are fast-followers where the biology is, I think, well validated. And there we've really focused on engineering and differentiation into the assets. So for example, with ABS-101, which is an anti-TL1A antibody, we've designed in a novel interface against a different epitope that's very similar to the Merck epitope, but slightly different to enable a better ADA profile, but much, much higher potency. We've also engineered in half-life extension and a couple of other parameters on the developability side. So we're able to formulate it at high concentration, for example. So we've used that platform to design in all those features to make it differentiated. I think one of the things I'm the most excited about as I look at what's happening in the lab today is we're moving beyond just selecting an epitope. We're moving to a place where we can actually look at interrogating the design of the interface against an epitope to deliver better potency or better, even potentially better MOA. And so we've done that with the TL1A program, but we're doing that with other programs in-house, one of which we'll talk about at our R&D Day. And then secondly, we're starting to design in novel pharmacological features into our antibodies. And one of those that we'll talk about at R&D Day is the ability to design in, using AI, a pH dependency in binding, which we think opens up a lot of opportunities in oncology. And then in terms of your third program, which I know you will have more disclosure at the end of this year or early next year, but can you talk about the platform that enables that first-in-class target identification? Yeah, so we wanted to have an ability to identify novel targets that we could then put into our AI platform. And so we've built, and this was through an initial acquisition of a company named Totient, but we've continued to invest and build out a reverse immunology platform. And this is a platform by which we take patient biopsies and we're able to look at the RNA-seq data from those biopsies. And we look for TLS markers, so we're looking for tertiary lymphoid structure, RNA repertoires. We use machine learning to stitch together the heavy light chain, so basically to identify the antibody repertoires from those samples, and then we de-orphan those. So we can, at the end of this process, we have a fully human antibody that the TLS has evolved towards and the antigen that it's targeting. We validated the platform by finding both antibody target pairs for targets that are well validated, whether it's marketed drugs or drugs in late-stage development, but we're more focused on using it to identify novel targets. And so, our ABS-301 program is a program that comes out of that platform where the initial starting antibody and the target were identified using that technology, and then we used our AI platform to further improve the human antibody that we had as a starting point. So we can improve its potency, we can improve other characteristics for developability and stability, and that's a program that we'll talk more about likely in January. Then in terms of the collaborations that you have, which I constantly forget the exact number of, but I know there are definitely more than 10. I think some of them are more long-term co-developments, and some are more when you're involved at the very early drug design phase, and then maybe the company has the option to develop that further. Can you talk a little about how you see internally, how you see the capability of those programs to validate your platform or maybe to de-risk your platform further? Then relatedly, internally, how are you thinking about how far you want to take development yourself as far as your internal programs go? And obviously it's the early days, but are you potentially looking to build up a clinical development organization, take things into phase two, phase three, or if you would be looking to partner with someone on some of these programs? Absolutely. Maybe I'll take the first part and, Alex will take the second. On the first question, what we're trying to do at Absci is build a portfolio of partnerships. So, you mentioned we have discovery partnerships with companies like AstraZeneca, and what we're looking for in those partnerships is some really good synergy around the target biology. So, AstraZeneca is a great example where they come with a target in the oncology space. They have wonderful expertise in oncology. They know this target, and we use our platform to design a best-in-class, first-in-class antibody, in this case, first-in-class antibody to the target. So those are, I think, interesting for us to pursue. They provide capital upfront. They also help us diversify the target space that our AI models work on. And we're always trying to generalize to new and new, new and more different target structures. So that's one element of the portfolio. The second element is doing co-development partnerships, and that's where we look for other types of synergies, but here we're going to put a little bit more capital behind the program but have a lot more return on the back end. And so, these are partnerships like what we've done with Memorial Sloan Kettering, where we have a great synergy there. Again, great target expertise in oncology that they bring. We bring the AI platform to design a therapeutic against the target, and they also bring another synergy, which is important to us, the ability to execute on a phase I clinical program. So, we're looking for these synergies with partners, and in this case, the idea is we'll work together to develop an asset 50/50, and then we'll look to out-license it after we have phase one proof of concept, and we'd share 50/50 in the outcome of that. So, it's a risk sharing, but it's also looking for these synergies. And then the third element of the portfolio are our own wholly owned assets, and those are always going to be centered on places where we have expertise built up. So cytokine biology, we're focused on I&I in some areas of oncology, and this comes back to the team that Andreas has built in-house. And so there, there's more capital we're going to put at risk, but there we own 100% of the outcome. And so if you look at what we're doing across this, that final element of wholly owned, we are also going to look to partner. That'll be made on an asset-by-asset basis, but again, it fits into this broader picture of our portfolio of different risk return across these different types of partnerships. Now, on the wholly owned side, the partnerships are in the future, so we're building, and then so we take some of that risk upfront, but the idea is that, and you can see this in the market, if we achieve phase one validation, some cases phase two, in some cases even at DC, the economic returns on structuring a partnership at that level of development versus discovery phase are much, much different, much more significant. And Alex, do you want to comment on the stage we're looking to take each asset? Yeah, absolutely. And so as we are continuing to build out and optimize the platform over the last few years, we realized that there's a lot of key advantages in terms of efficiencies, speed, and low cost, and potentially building in differentiation for each of these assets that we saw that we could potentially create and capture more value by doing some of these programs ourselves and looking to partner them later at a different point, so to Zach's point, you know, there's really no one-size-fits-all approach to these, but we do see potential anywhere from during the DC phase to during or after phase I, or even during or after phase II, depending on the asset, depending on the partner, when we could look to partner or transact these particular assets. And if we think about our ABS 101 asset, for example, and just how that came to be, you know, it's a lead asset in our pipeline. It was a matter of just 14 months from the start of the program to getting to the drug candidate and the cost about $5 million or so. And so, what we see there is the potential to keep creating these differentiated assets in a really rapid timeframe. We see potential now at this point with the current version of the model to get to the drug candidate in about 12 months and at a similarly low cost, which we could then look to take through R&D enabling studies, potentially run a phase one, for example, and then transact at the right time with the right partner. And so across the portfolio, again, no hard rule to each of these assets, but we do see potential value in each of these that we do and have entered the pipeline. Let me pass it back to Vamil to talk about your upcoming. Yeah, we got a few minutes left here. Just so you mentioned in December, the R&D day, obviously can't, don't want to front-run what you're going to disclose, but maybe just a little bit of a preview of what we should expect, I guess, on sort of all three of the internal programs and then maybe just lay out sort of catalysts for next year as well. Yeah, absolutely. So it was last October is when we had our first R&D day and unveiled the internal pipeline, so showing the three wholly owned assets that we have in our portfolio. And then for this upcoming R&D day, December 12th in New York City, we'll plan to share some new data around ABS-201, our potential best-in-class dermatology program, plan to show what the target is on that, plan to share some market data, have some exciting guest KOL speakers to talk about that, and then also showing one new asset that's being added to the pipeline in addition to ABS-101, ABS-201, and ABS-301 are sort of the main key points we're excited about next month. But you're right, I don't want to give too much away at this point, but that's what we're seeing for R&D Day next month. Looking into 2025, planning to have our lead asset ABS-101 enter the clinic with an interim phase I read out in the second half of 2025. And just to build on your December 12th, you guys will be joining us for the Tech Bio Summit the same afternoon, so it ties in nicely on that day. And then maybe just to wrap things up, just around your capital position now, cash runway, any updated comments there? I know you're reporting earnings tomorrow. We'll report earnings tomorrow, but if you look back to Q2, we were sitting with about $145 million in cash, cash equivalents, investments, short-term investments on the balance sheet. In that quarter, the net burn was roughly $16 million. So, we're right on track with the guidance that we've put out. I think we're on track also with all of our program development. Okay. But there'll be more to share tomorrow. Tomorrow, yeah. Okay. No, great. Exciting time. So thanks so much for joining us, and I look forward to hearing more over the next few weeks. Great. Thank you.