Good afternoon. Thank you everyone for joining us. Really pleased to have with us the Generate team. With us, we have Michael Nally, Chief Executive Officer, and Jason Silvers, Chief Financial Officer. To start here, Michael Nally, would you introduce Generate, your platform, your clinical programs, and the catalyst path for the next 12 months- 18 months? Yeah. Thanks, Salveen. It's great to be here, excited to share a little bit more about Generate Biomedicines and the story. Generate Biomedicines a company that was founded in 2018 with the fundamental premise that a data-driven approach to protein design could lead to superior outcomes. At the time, the prevailing paradigm was largely biophysical-based tools to design proteins, it was a convergence moment in the field where the Transformer paper was coming out of Google at that point. We saw masses of increases in compute and two foundational data sets, the Protein Data Bank and the companies like Illumina that had sequenced every species on the planet, allowed us to start to understand sequence to structure interrelationships. The company was founded to add a third leg to that stool, which is function. The company operates with a very tightly integrated dry and wet lab to generate experimental data at a pace and a scale that was previously unimaginable, feed that data back to train models to design novel proteins that nature hasn't discovered. Over the past seven years now, we've been able to put five molecules into the clinic. The productivity of the platform is pretty extraordinary in terms of the efficiency in which we can find new proteins, but also the scalability of drug discovery. Our lead program's an anti-TSLP antibody that entered phase III in December of last year. It was designed basically five years ago today, the ability to go from kind of concept to phase III in less than five years is, I think, pretty impressive for a chronic care program. The advantage is it binds the target with exquisite affinity, 100 femtomolar binding. You couple that with an extended half-life, and we believe we'll have an every six-month dose anti-TSLP antibody. We're looking at it in severe asthma in the first instance. We'll have regulatory interactions over the course of the summer in COPD, we're excited by that program. We then have a couple of oncology programs that are just entering the clinic right now. The first of which is a payload neutralizer program, this is a kind of a unique concept where we've seen enormous advances in antibody-drug conjugate technologies, and this molecule selectively binds the cleavable payload of MMAE-based ADCs to hopefully reduce the rate of peripheral neuropathy. In the PADCEV phase III trials, you saw about a 65% rate of peripheral neuropathy, much of that leading to either discontinuations, down dosing, or dose stoppages. It is a clear in terms of the adoption of these ADCs, especially in the community setting. By selectively binding, we believe we'll be able to clear the toxic part of the regimen while not interfering with the efficacy in a very targeted fashion. We think it'll open up the therapeutic window in a meaningful way. The next program is a CAR T program, so we're working with Roswell Park Comprehensive Cancer Center. They have been pioneers in a number of different CAR T domains. They hired a key scientist from Sloan Kettering, Reinier Brentjens. Reinier had asked us could we use our technology to optimize the binding and the framework for CAR Ts in solid tumors, and we will be testing what we believe will be one of the most potent CAR Ts on a target in ovarian cancer, MUC16, imminently. What we're seeing with the technology is a huge breadth of potential impacts across these different domains, I think we're just scratching the surface of the potential of the technology where we're, in the first instance, using it to optimize existing molecules. Where we think the technology will ultimately have greatest impact is being able to drug undruggable domains with biologics. Maybe just stepping back with regard- Yeah to the platform here. We've heard the term AI as an umbrella for different strategies and technologies, particularly in healthcare. Yeah. What do you see as the key differentiator of your platform versus traditional companies or traditional computational biology and those employed by other companies in this field? Yeah, I think maybe I'll start, then Jason, please jump in. I think there's a lot of noise in the field. I think many people are declaring that AI will be a panacea for drug discovery and development, and while we think it will be transformational in certain by any means in biology. With a great degree of humility. Where we see the real impact, and what we're seeing very tangibly at Generate is we now are able to design molecules that were almost impossible to discover using kind of immunization campaigns. As you all know, the way we've historically found biologic agents is you either immunize a human, a mouse, a llama, then you try and manipulate that protein to have drug-like properties. What we're seeing with these technologies is an ability now to de novo generate desired protein sequences that perform in a way that we want. We think we're migrating from what has been an artisanal craft of drug discovery, borrowing from nature, to an endeavor that is much more based on programming biology. I think the key for us has been this tight integration of experimental capability and computational capability. If you look at and you think about the domains of generative AI that have had the greatest impact to date, there's a reason Demis Hassabis focused on gaming first. There was a rapid verification engine with gaming where you had a scoring mechanism that allowed you to continue to train the model. Coding, math, both have rapid verification engines. Biology, at its core, doesn't have that verification engine, and we don't have the internet to train models on. Having this tight intersection of experimental capability with cutting-edge computational tools allow us to have that verification engine to really refine the answers and get to answers that were previously almost unimaginable. I would just add a few things that are differentiating, I think, for us. Number one, we're large molecule only. Two, we're modality agnostic, this is not just an antibody company. We're working across eight different modalities in terms of what our products are, including with our partners in Amgen and Novartis. Also, we've spent a lot of time and effort and money on cryo-EM, looking at structure, not just experimental verification of what comes out of our machine learning, but also we can look structurally at protein-protein interaction space down to a few angstroms or even lower than that. At an atomistic resolution, so that we can not only look at assays and functional and biophysical, but we can actually look at what's happening at the binding site, and that's real-time, very high throughput. Maybe a couple of years ago, it could take three to six months to actually create the structure from using cryo-EM. We're now solving at least a structure per day per scope, and we have four microscopes, which is pretty unique in the industry. Wow. Maybe starting with the lead program here. Yeah. GB-0895 is in phase III development in severe asthma. Speak to the bar that you hope to achieve on exacerbation rate reduction in the overall population and the low EOS group. Yeah, it's a great question, Salveen. If you think about respiratory disease, biologics still aren't standard of care. We've obviously seen the first-generation biologics drive about a 20% uptake in the severe asthma population, but the vast majority of asthmatics that have severe disease still don't have a suitable alternative to ultimately control the deleterious effects of the disease. For us, we think TSLP has shown enormous promise in the domain. What we've seen and what we believe our molecule will ultimately show is that we'll be able to dose this almost in a prophylactic sense, where you'll give an administration every six months. On a blended basis across all EOS levels, we think the efficacy will be in that roughly 55% range. The way that will balance out is in the high EOS population, which is where molecules like the IL-5s are dosed. We'll see roughly a 70% reduction in exacerbations, the trial is specifically powered to detect a 40% reduction in that low EOS cohort. One of the inherent advantages of TSLP has been the ability to reach that low EOS population. We think both in the design but also based on the qualities of the molecule, that those sort of rates are very achievable. Speak to what gave you enough confidence to advance directly from a phase I- phase III. Well, I think part of it is what we saw in the phase I data. We designed this molecule with purpose, I talked a little bit about programmability a moment ago. We had a certain set of specs that we wanted to make sure that this molecule reached. When we revealed the phase I data, it basically hit on every one of those parameters. We always thought this was a potential pathway, largely we were inspired by the work at GSK with depemokimab, so we certainly want to give due credit. GSK was really clever. They went into mild to moderate asthmatics in phase I and then used that data based on the ability to effectively modulate the biomarkers to go directly to phase III. We know this is now a validated pathway. The depemokimab has been approved in the U.S. and Europe. We went to the same sites in the U.K. and Germany. We wanted to make sure that we were not only showing that we were safe, that the ADA rate was low. We also wanted to show that we were modulating the key pharmacodynamic biomarkers in a relevant fashion. What we were able to show in that study was that we were able to modulate IL-5, IL-13, EOS, and FeNO at a level that was commensurate with, if not slightly numerically higher than what we saw with tezepelumab. When you couple that data with the fact that you can effectively model target occupancy at our 300 mg level versus what you see with tezepelumab at the marketed dose of 210 mg, we were able to show over the course of six months that we have the same or better target occupancy at all time points to tezepelumab. The third piece of data that was important in this choice was actually based on the tezepelumab phase II data, where they did their dose finding. They explored an eightfold range of doses. They showed no difference in exacerbations across that eightfold difference. For us, it came down to a pragmatic choice. We could have run a phase II study, looked at multiple different dose levels, showed the same exacerbation reduction rate, and then made a pragmatic choice that would have cost us $50 million and spent two years. Based on our ability to modulate the key biomarker in a commensurate way, the ability to show that we're engaging the target the same or better level while binding the same epitope. Based on the tezepelumab data on a pragmatic choice, we said we're better off just making that pragmatic choice based on that data. Critically, we hit the same epitope as tezepelumab, which was a big important point for the FDA and other regulatory agencies in allowing us to go to phase III. As we think about the potential to improve upon Tezspire here, clearly there's biannual dosing. Yeah. Speak to if that's just the improvement, what physicians are saying about uptake, and if they're comfortable with long-term use of an agent. Secondly, is there any potential to improve upon efficacy? I think when we started the program, this goes back to the earliest days, what we were able to show in the preclinical models was a 20-fold improvement in affinity. Yeah A fivefold improvement in potency. The question always has been, how well does that potency marker translate into humans? What we're seeing based on the phase I data is relatively a commensurate reduction in the biomarkers, we think the base case should be comparable efficacy. Until you run the studies, you never know. Maybe that fivefold improvement in potency preclinically will translate into something, but I think the base case certainly should be comparable efficacy. If you look across all immunological conditions, and we've seen this now as these immunological markets have matured, you see these first-generation agents oftentimes be shorter duration, then over time you migrate to longer duration agents as the market becomes more sophisticated, as patients become more demanding around the types of therapies they're looking at. The psoriasis market's a classic case here, where now you see somewhere between a 40%-60% penetration with biologics, and the long-acting biologics are the disproportionate beneficiaries in those sort of spaces. We think asthma will evolve in a very similar fashion. One of the things that's really important to note is severe asthmatics visit their doctors twice a year. If you look at the current compliance records, and GSK presented some beautiful data at ERS last fall, only 20% of patients on biologic therapy are maximally compliant. The ability to drive a better level of compliance, given the fact that you're dosing every six months, but also you're dosing in alignment with the current doctor's visits, we think could ultimately have a huge real-world benefit of these sort of therapies. We saw this if you go back to the osteoporosis market, you probably remember when Prolia was launched, right? The bisphosphonates were great, Prolia was not able to show a difference in hip fractures in the phase III protocol. Yeah. As soon as you got to the real world, we saw a marked improvement in these sort of key markers. We believe over time, GB-0895 will not only just have a convenience benefit, but will be cost-sparing to the system. Got it. As you mentioned, in COPD, you plan to report full phase I-B data this year and we'll also understand your registrational development pathway. At this point, are you leaning more towards a phase II-III or a phase III? We're certainly going to propose to the FDA that a phase III is the appropriate next step. We'll meet with the FDA in the coming weeks. The range of alternatives reach from a single phase III trial of a single dose to a phase II-B/III design. I think given the changes at the agency, it's a little bit hard to know what is the preferred stance. Clearly, with the change in leadership management had been a big proponent of the single phase III trial. In that case, what you would do is you drop the P value down to the 0.01 level. You do about a 1,200 patient or subject study. Yeah. We think that could be a valid path. Ultimately, that will require regulatory alignment. We'll get that alignment in the next few weeks. As you rightly point out, we'll look to maybe top-line the data around the second quarter earnings time, then present it at an appropriate medical meeting in the fall. How much time would that save you? I guess secondly, your confidence that you'd be appropriately suppressing the target at this dose. Again, Salveen, if you go back to the data that we've shared to date in COPD, what you're seeing is a comparable reduction in the relevant biomarkers between the asthma and the COPD study, right. You're seeing roughly this 50% reduction in a number of these key biomarkers, less so on FeNO. You're seeing kind of the similar reductions. You're not seeing any improvement when you go up to 600 milligrams, importantly. That ultimately will be the conversation and that will be what the phase III study will be ultimately to elucidate. You're probably talking about saving two years if you go directly to phase III versus have to do a phase II/III trial and a substantial amount of cost. Yeah. Can you discuss the target population in COPD and your confidence on TSLP and the mechanism- Yeah In that particular indication, how it differs from the two approved antibodies, Dupixent and Nucala? Yeah. I think we were really encouraged by the TEZI phase II data in COPD. What that data was able to show was a significant reduction in the greater than 150 EOS cohort. It didn't show a therapeutic effect in the less than 150 cells per microliter. The percentage reduction in the greater than 150s was about 37%, in the greater than 300s was about 46%, albeit with a small N. What you've seen with Dupie and with Nucala has been, Dupie's been around a 30% blended exacerbation rate reduction. Nucala has been roughly about a 20% reduction. If you could replicate what TEZI has shown in the phase II trial, we think you'd be positioning toward a best-in-class efficacy profile. I think the one unknown is the tozorakimab data that AstraZeneca will show with their IL-33. What's been encouraging, I think, for COPD patients is it's the first agent that has shown an effect in that low EOS cohort. Yeah. What the magnitude of effect is, we'll have to wait and see. Obviously, we've had other IL-33s with slightly different approaches, not yield consistent results. I think it's a great thing for patients that the tozorakimab data will come out and we'll obviously have to look and see where that efficacy bar is set based on their presentation at ERS. Yeah. For your MMAE-targeting antibody, when do you plan to present data next year, and why do you see 50% reduction as the key window here? 50% reduction is important, and frankly, the FDA in our conversations with them even encouraged us to potentially go up to 80% reduction. The reason we're targeting 50% reduction is in our preclinical data, we've shown that at 50% reduction, you actually retain the tumor killing, but get decrease in skin toxicities in others, in mice, as an example, and in non-human primates. For us, that 50% level, we think is the right level with flexibility to go up if we need to. The trial design for phase I, which we dosed our first patient actually this morning, and all the sites are activated for that trial. The trial design is we'll first do a dose escalation in terms of what dose will give us that 50% reduction in free MMAE. Once we determine that, which we believe will be probably close to the end of this year, maybe it falls into 2027, we'll then open a cohort of patients who have Grade 1 peripheral neuropathy, on PADCEV KEYTRUDA, give them our MMAE neutralizer, and see if we can stop or slow the progression to Grade 2 or irreversible peripheral neuropathy. We haven't made a decision in terms of which data, if any, we would present first, whether it would come in steps or ultimately we'll present the whole package. We're pretty confident that we should be able to be in a position in 2027 to present all of those data, but we'll make that decision in terms of whether we present the dose before we actually move to the second cohort of the trial, or we present all at once. Salveen, just to one point, when you look at that 80% threshold that Jason alluded to, above 80%, you do disrupt the bystander effect. Yeah. Part of what you're trying to do is find how do you open up that therapeutic window in an optimal way so that you reduce the rates and the burden of neuropathy and some of the other negative side effects associated with enfortumab vedotin, while make sure you're optimizing for tumor killing. We think the ultimate path will require us to not only show the reduction in neuropathy, but also show a non-inferiority on overall survival. That is the blend, and that's why this window of 50%-80% becomes so important. How much of a reduction in neuropathy would you expect at about 50%? It's a really good question. I think there's two parts to that, right? There's the rates of neuropathy, we think you could expect somewhere upwards of 50% reduction. It's also the progression of neuropathy, right? Once you get to Grade 2 neuropathy, you have irreversible side effects. Part of what we're trying to figure out, especially as enfortumab vedotin continues to post really stellar data in a number of different tumor types, but also in healthier patients, so in the adjuvant and neoadjuvant setting. The risk-benefit of irreversible side effects actually changes as you go into earlier populations. For us, it's not only just the absolute reduction, but also the ability to slow the progression or stop the progression so that you can maintain on drug for longer with these sort of agents. In addition, the two other important pieces, one, in the community, you're seeing probably less than 50% of patients with urothelial cancer get treated because physicians are less comfortable dealing with the side effects, given all the different toxicities. That's point number one, then two, in the academic center with 65% peripheral neuropathy, it's not only reducing the peripheral neuropathy. Ultimately, as Mike mentioned, if we're looking at non-inferiority, there's been some recent data to suggest that patients on Padcev Keytruda for a longer period of time actually convert from PR to CR. If that's true, then by giving our reducing toxicity medicine, that could enable the window for patients to receive the drug for a longer period of time and open that therapeutic window, ultimately lead to a better efficacy for them. Right. You have your own ADC preclinically that would go into the clinic at some point. That's right. Do you have any updated timelines around that? This is based on the observation that our technology allows you to optimize for internalization. There are a number of antibody-drug conjugate targets that have been materially overexpressed on cancer cells and not expressed on healthy cells. A number of these sort of first-generation attempts have struggled because they don't get enough of the payload into the tumor. Sometimes it's been kind of perceived to be, "Oh, well, that's just a poor internalizing target." What we've been able to show now in the preclinical setting with our technology is that by modifying the CDRs alone, you can drive logarithmic improvement in internalization. That opens up these kind of, in some ways, partially validated targets to a better internalizing antibody-drug conjugate. We have one target that is kind of approaching development candidate nomination. We have a second target that is in earlier stages of exploration. We think based on this technology of being able to optimize internalization, there are a whole array of not only antibody-drug conjugate targets, but also potentially other protein-based therapeutics where getting into the cell in an optimal way, they become more viable with the technology. Jason, perhaps speak to the partnership outlook here for your overall portfolio. Sure. We have, as you know, partnerships today with Amgen and Novartis on the corporate side. We have MD Anderson and Roswell Park Comprehensive Cancer Center on the academic center side. The Amgen and Novartis programs are really getting toward the tail end of those programs. We've made an enormous amount of progress, frankly, even in the last year in both of those programs, in the outstanding programs. We think by the end of this year or so, early 2027 or end of this year, we should be sunsetting those programs, having met the criteria that we believe will enable us to receive additional milestones in both those programs. Those, as you'll recall, are complex in terms of what we've been asked to solve. These are more than a decade in some cases, get to outcomes that are either measurable or being able to test biology just because traditional techniques are unable to get to the result they want. Our technology has enabled us to find those results, and frankly, it also gives us an opportunity to utilize those technologies in other areas outside of the specific target that we're working with on those two companies. We will and are continuing to explore additional platform-type collaborations like those. They'll be target-specific collaborations. Over the course of the next six months to 12 months or 18 months, we certainly will be in a position to announce at least one more of those, and we'll continue to pursue similar. Other collaborations that we're exploring and potentially would do are much larger collaborations that utilize either a specific technology with broader applications or technology with broad therapeutic applications, maybe in areas outside of our core focus, which right now we're focused, we've been on infectious disease, immunology, and inflammatory diseases. You could think about cardiometabolic or neurology as an example. Our technology that we're working on today, much of which we've learned through doing work with our existing collaborations will enable things in those domains, in particular like crossing barriers, pH-dependent binding, increased internalization, things like that. Other things, we have other programs that we have decided to deprioritize, just given capital constraints of the other existing clinical assets. We certainly would be in position and consider whether we would do licensing transactions or some kind of collaboration around those as well. Those are the core collaborations. The other thing, Salveen, though, if you step back and you think about the technology, why we are so excited about it is partially because drug discovery has been this artisanal craft. When you put the computer at the center of the discovery effort, all of a sudden you introduce scalability dynamics in discovery that have not existed before. What we're challenging ourselves on right now is we've been able to put a number of programs into the clinic, but we also know that the technology could actually produce much more than we could consume ourselves. The partnerships with Amgen and Novartis were the first step in terms of how do we realize additional value from the scalability of the technology. As we look forward, we think there are a number of other vehicles that we owe it, certainly to our employees, to our shareholders, to further explore. First is, are there opportunities to what we would call create modality intellectual property? Are there generalizable principles that govern certain biological functions? One common example is the YTE mutation for half-life extension, but are there similar sort of mutations that would govern barrier crossing, that would govern internalization, pH-dependent binding? If you can find those, you can prove them and then enable the field in a very capital-efficient way. The other thing that we spend some time thinking about is, could we use the technology to spin off independent companies? part of our, I think, responsibility is to sit there and say, well, given a unique level of productivity, how do we effectively monetize it in an optimal fashion without requiring all the capital in one entity? Yeah. Perhaps the last question here, how do you defend your algorithms from potential commoditization of AI over time? I think we've always believed that the algorithms will commoditize. Yeah. again, it doesn't mean to suggest that you don't need to keep pushing the frontiers of algorithm development, but much as I would look at the LLMs, one day Claude's the best, the next day Gemini is the best, the next day ChatGPT is the best. We think the techniques that lie behind the models will ultimately converge. What's really important is how you combine those techniques with the experimental interface in a virtuous cycle of learning. I think where we've been fortunate is that our computational scientists at Generate believe in the necessity of experimental verification. Our experimentalists believe in the potential of these sort of technologies. it's this intersection that I think is much more defensible, especially with unique and differentiated experimental capabilities. Great. Well, with that, thank you so much. Thank you.