Good morning. I'm Sean McClain, the Founder and CEO of Absci. We're a generative AI drug creation company looking to use generative design to solve some of the toughest problems in biology that still exist, to ultimately get differentiated assets to patients faster. We've always been a data-first company. Our original technology was scaling protein-protein interactions, going from screening thousands of antibodies to millions. We've leveraged this technology to build out a world-class AI platform. It was a purpose-built team. We have world-class scientists that have over 10 drugs approved under their leadership and an amazing AI team that comes from OpenAI, Google, Tesla, and NVIDIA. Additionally, we have an integrated data flywheel, which I'll get more into on subsequent slides, where we're able to use this wet lab technology to generate data for training and use that same technology for validation. We like to call this our lab in the loop. We use these models to ultimately create differentiated assets. We have leading de novo AI models where we're able to go after hard and undruggable targets, such as ion channels and GPCRs. Additionally, we're utilizing this platform to build out both our internal pipeline as well as within partnerships with large pharma and biotech. We've had a lot of really great momentum with our own internal pipeline. We have ABS- 101 that just entered the clinic in May. This is an anti-TL1A antibody for IBD. We'll have a phase I interim readout the second half of this year. We have ABS- 201, which is a category-redefining asset going after the prolactin receptor. This is an anti-prolactin receptor antibody for androgenic alopecia, so common baldness in both males and females that affects over 80 million patients in the U.S. alone. We also have an exciting early-stage pipeline that's really focused on leveraging our platform to go after these undruggable targets like ion channels and GPCRs. As I had mentioned, we've always been a data-first company. Our original technology was figuring out how to scale protein-protein interactions, how antibodies interact with the target of interest, the epitope, and the binding affinity, essentially looking at the antibody functionality. We leverage this data for training. We are able to take these models that have been trained with this data, publicly available data, as well as simulated or synthetic data. We're able then to validate these models in the wet lab with that same technology. This is our lab in the loop. This occurs in a six-week time period. This is what enables us to rapidly iterate on our model designs and architectures and has ultimately led us to the breakthrough models that we have in de novo design. We are leveraging these de novo design models to be able to design antibodies to targets that have not been drugged before or that have been hard to drug in the past, such as ion channels and GPCRs. We are already seeing success with this. In our partnership with Amaro, we were able to show that we could design a blocking antibody to an ion channel in an indication for dermatology. This target has been known for the past 30-plus years, but no one has been able to drug it with traditional methods. We are able to use our generative design platform to drug this particular ion channel, unlocking new novel biology for patients. Additionally, we want to use generative design to engineer in properties that previously have been difficult to engineer. Let's say being able to have an antibody that can bind to the tumor microenvironment in an acidic condition but not bind to healthy tissues in neutral pH. Additionally, being able to engineer an antibody to have antagonism versus agonism. These are all ways that we're leveraging this de novo design model to create differentiated therapeutics for patients, both internally and in our partnerships. Now let's dive into a couple of case studies. We have two main models that we've used and deploy. The first is our de novo design model. This is where we're designing antibodies from scratch, where there is no known binder to a particular target of interest. We're able to leverage the model to design the CDRs of the antibody to bind to that particular epitope of interest. Late last year at our R&D day, we disclosed and shared some really exciting data in our partnership with Caltech and the Gates Foundation. This was a collaboration with Dr. Steve Mayo and Pamela Bjorkman at Caltech, where they discovered the Caldera region of the HIV virus. This is a highly conserved region within the HIV virus. The exciting part is, if you could actually drug that, you could potentially create a neutralizing antibody against all different variants of HIV. The issue has been that it's a very deep crevice. It's been hard to drug with traditional approaches, a hard-to-drug epitope. We were able to use and leverage our de novo design model to be able to design an antibody that could bind to this very deep crevice within the Caldera region. This is where traditional technologies had failed, yet we were able to drug this particular epitope. You can actually see it. We have it modeled here. The blue and the red are the antibody, and then the blue chain is the heavy chain. You can see that it has created a deep binding loop that goes into the Caldera region of the HIV virus. There was no known binder, and our model was able to design this antibody from scratch, being able to potentially create a universal neutral HIV antibody, which is really exciting and one of the reasons why we are so excited about this particular technology. On the right-hand side, we have our AI lead optimization models. This is where you have a binder, and you're wanting to optimize the particular properties of the antibody. You want to increase the affinity. You want to increase potency, developability, manufacturability. Additionally, we have shown last year at our R&D day an exciting example where we're able to engineer in pH dependency. We were able to engineer an antibody that was selective at binding a target at acidic pH but did not bind in neutral pH. This could be used in various different oncology applications where you want the antibody to bind to the tumor microenvironment but not bind to healthy tissues. Again, another example of how we're being able to engineer properties with generative design. I like to think of this industry as a team sport. It takes all of us to ultimately get better drugs to patients. We have leveraged partnerships to be able to see our vision through. We have AI drug creation partnerships where, with large pharma and biotech, we're leveraging their domain expertise in a particular indication that we're not focused in on to design molecules to hard-to-drug targets, like our partnership with Amaro. Additionally, we have data and compute partnerships with AMD and Oracle, helping us scale the compute needs for these AI models. There are three critical aspects within AI. You have to have the talent, the data, and the compute, and then obviously the models as well. These data and compute partnerships help ensure that we're able to successfully scale the AI models that we create. We do have a track record of being able to partner with the leading companies within the space to, again, ultimately get these better biologics to patients faster because it is a team sport. We have a multilingual team. It's been really fun to bring two industries together, biotech and tech. They actually have very different cultures and how they approach problems. Being able to bring together a team that deeply knows how to push model architectures and designs and create these models that are able to create these drugs, that's one problem. You actually then have to be able to develop these preclinically and then ultimately get them into the clinic and develop a clinical strategy. We've built a team that is versed in all these different aspects. It's seen. You see it with the success that we've had with being able to drug the Caldera region, our success with our partnership with Amaro, and then additionally developing our own assets, being able to, within the next 12 months, we'll have two of our own assets in the clinic. This is the team that's built this platform and one of the reasons, one of the main reasons we've had the success that we've had. I've already mentioned the two lead assets that we have. We have an anti-TL1A antibody for IBD and an anti-prolactin receptor antibody for androgenic alopecia. Let's dive into those. ABS- 101 was purpose-built to deliver best-in-class properties in potency, durability, and patient convenience. We've been able to show that we could develop a next-generation anti-TL1A antibody with our generative design platform. We're seeing potency advantages, greater than 3x increase in target engagement versus the first-gen TL1A antibodies at equivalent doses in NHPs. We're able to have convenient care. We've been able to formulate this in a sub-Q formulation, 200 mg per ml, which will enable at-home self-injection. Additionally, we've been able to show good durability and safety. We have a clean 13-week GLP tox, which confirms our extended half-life, which we believe we will be able to go from once monthly dosing to once quarterly. As I'd mentioned, we are in the clinic with a phase I interim readout the second half of this year. Additionally, we're expanding the TL1A asset and going into bispecifics. We've been able to use our generative design platform to go after another undruggable target, which is a known target. It's an adjacent pathway that we think will be synergistic with TL1A. This is not an alpha-4, beta-7, or an alpha-23. This is a known novel target that's been difficult to drug. We are developing that as a monotherapy, but then also as a bispecific. That is early in our pipeline. We have already gone through the clinical trial timelines on this. Again, just to highlight, the second half of this year, we will have the phase I interim readout for our anti-TL1A program, ABS- 101. Now moving on to ABS- 201. ABS- 201 has the potential to unlock a wholly new category of hair regrowth. This is a really exciting opportunity. This is going after androgenic alopecia, again, common baldness within males and females. This affects over 80 million patients in the U.S. alone. Patients are looking for durable and efficacious treatment. Currently, the standard of care is not meeting the patient's needs in this particular category of hair regrowth. How do we plan to tackle this problem? We're tackling the prolactin receptor. What does the mechanism of this look like? On the left-hand side here, we have the hair follicle cycle. It starts off with the anagen phase, which is the active growth phase. Then you go into the catagen phase where you see apoptosis and regression. What ends up happening is patients that have androgenic alopecia end up having prolactinemia on the scalp, increased levels of prolactin. Prolactin keeps and drives the follicle to stay in that catagen phase where you have the apoptosis and regression. By blocking the prolactin receptor, you're able to shunt the follicle back into the anagen phase where you're able to get active growth and additionally stem cell growth. The great thing about the anagen phase is once you're in that phase, based on your genetics, you're in that phase for two to six years. You'll see some really interesting data here on the durability. It looks like once you have a round of treatment, you could have durability for hair regrowth for two to six years, enabling potentially pulse therapy. You're going to see really great durability. Additionally, I'll show you some really nice efficacy data compared to minoxidil. Let's take a look at a really cool model where we're able to see that the prolactin receptor, by blocking it, you are indeed able to revive the hair regrowth and be able to shunt that follicle back into the anagen phase. What you see here are the tops of the heads of these stump-tail macaques. These monkeys naturally go bald. It's a great translational model. You can see on the left-hand side, before treatment, they have bald gray hair. After 28 weeks of treatment, you can start to see their hair starts to grow. Not only is it growing back, but they're naturally colored dark hair. What was incredible about this was post-treatment, for up to four years, you're seeing really strong durability. Once they're in that anagen phase, they stay in that active growth phase. One of the upsides that we see of targeting the prolactin receptor, and you can see it here, is the ability to drive pigmentation. Going from your gray-colored hair to your naturally colored hair. I think we may have—oh, there we go. Now let's look at how ABS- 201 compares to standard of care minoxidil. What we have here is a shaved mouse study. What we do is shave the mice. The mice are naturally in the catagen state. What you'll see is the faster the hair grows, that is indicating that you're shunting the follicle back into the anagen state. If you look at ABS- 201 compared to minoxidil and the control, you can see superior efficacy. ABS- 201 is able to achieve hair regrowth much faster than minoxidil, which is current standard of care, along with the untreated mice. This shows really great efficacy in both mice as well as NHP. We just recently released this data. This is our interim data readout on our NHP data to really confirm that we do indeed have a really nice extended half-life. We believe, based on this profile, that we will be able for a six-month treatment period, we'll be able to dose two to three times. Additionally, we were able to show high bioavailability of greater than 90%. Additionally, we believe that the manufacturability and developability will ultimately enable high concentration formulation of greater than 150 mg per ml. There is one other anti-prolactin receptor antibody that's out there. We believe, based on the PKPD profile, this is not a commercially viable program. To achieve the efficacy that's needed, this molecule will likely need 12-24 injections over a six-month time period, which ultimately is not scalable. Based on this data, we believe that we will be the first to the U.S. market and truly have a first-in-class asset here. To summarize, we believe ABS- 201 represents a significant untapped new market potential. When we talk to KOLs in the space, everyone is looking for treatment for hair regrowth. They see this as the last frontier. If we can see the efficacy and the durability translate from what we see in both the mice and the NHP, we see this being a very large opportunity for patients. Again, over 80 million Americans alone suffer from androgenic alopecia. We see the market being at least $14 billion a year. This does not take into account the repigmentation market that I had mentioned previously. To wrap up here, let's talk about the upcoming catalysts that we have. We have ABS- 101 enter the clinic. We'll have a readout on that at the end of this year. ABS- 201, that is on track to enter the clinic early next year. We expect a phase I POC the second half of next year. The great thing about androgenic alopecia is that we can get a proof of concept, an efficacy proof of concept readout in our phase I because we're able to enroll bald, healthy individuals. We will have that readout the second half of next year. We're really excited about that upcoming readout. Additionally, we are on track to sign a new large pharma partnership by the end of this year, which we do see bringing on significant upfront capital, which will be able to extend runway in a non-dilutive way. As you can see, we are leveraging generative design to be able to create differentiated assets that ultimately are being able to get better biologics to patients quicker. Thank you.