Lantern Pharma Inc. (LTRN) Earnings Call Transcript

Good afternoon, everybody. We're going to go ahead and get started on our call and webinar here in a few minutes. We're going to give everyone some time to log in. We're increasing the number of attendees. I've got my colleagues from Lantern and Open Medicine also on Dr. Kishor Bhatia and Reed Bender, they'll introduce themselves a little bit later in this call. So as we get everyone some time to join in, I'm going to remind everyone that we will be making forward-looking statements, and the presentation I'm making today will contain forward-looking statements and that I urge you guys to read our full disclosure that is in compliance with Section 27A of the Securities Act and that we may or may not update statements that we make today, and these statements will be -- will include potentially issues such as anticipation, revenue, product road maps, estimates, et cetera. You can also access our annual and quarterly reports under the Investor SEC Filings tab of our website and also at sec.gov. Forward-looking statements in this presentation represent our judgment as of today, the date September 23, and we disclaim any obligation to update any forward-looking statements to conform to actual results or changes in our expectations. So with that, I'm going to go ahead and get started. Thank you, guys, again for joining us. I've got my colleagues, Dr. Kishor Bhatia, and Reed Bender on as well, and they'll help me with questions. I urge you guys to type in your questions. We'll be doing a demo talking about Open Medicine, where we're headed, the feedback that we've been getting so far and also talking a little bit about how we see the road map evolving both in terms of usage and in terms of growth of this amazing platform. So again, urge you guys to type any of your questions or raise your hand when we get to the Q&A, and we'll try to work through as many questions as we can today. [Operator Instructions] Okay. Can everyone see the presentation?

Yes, we can see it.

Great. So we're going to talk about Open Medicine. It's a platform that's really been built internally by drug developers for the broader drug development community. Our vision for Open Medicine is really to become like a Bloomberg but for medicine, for everyone involved, whether they be developer, service provider, investigator, individual, power groups and large pharma companies. Our vision was initially to accelerate our own drug development out were a small emerging company. But what we found, which is really quite exciting and amazing is that the entire world realizes the power of AI and has moved to a very similar model. So we've spun out or spinning out Open Medicine into its own entity, which we accomplished earlier this year, and we've transferred certain assets and IP and capabilities into this new multi-agentc platform. So again, I've made the forward-looking statements earlier in the call, so I won't go through those. But let's talk a little bit about what we're going to cover. I'm going to give you a view of the market size and how viewe -- we view the market, which is a little bit different than how a lot of other companies and people approach the market. It's a large market, no doubt. But as a former analyst, myself and a consultant, I really try to size the opportunity bottom up. And in terms of what's really addressable, where is the revenue new or disruption coming from? And how will this actually shift to companies like ours? I'm going to give feedback from customers. I'm going to talk about the road map toward the end. Types of partnerships that we've got an interest in and seem to be pursuing and also a little bit on, of course, the demo because that is worth more than any webinar alone is actually seeing the differentiated capabilities in real time. So again, we're going to allot about 45 minutes. We're going to try to end our discussion and demo around 1:00 and then leave 10 to 15 minutes for Q&A. So like I said earlier in the discussion, when we first started Open Medicine, Back when I got to the company at the end of 2018, early 2019, we weren't setting to build an AI platform company. We're really building a drug development company that was using an AI platform and industrialized AI platform, not one that you take off the shelf and put away, but something that was always on, always evolving and really an AI-native drug development companies how we view ourselves. And our focus is really singular. Can we get cancer drugs faster and less capital. And we built the tools and we built the methodologies, and we built the approach because that's what we needed. That was the way that we do it. And we've built this on the back of clinical program, we had no clinical programs. They're all preclinical. In fact, some of these concepts didn't exist, the indications, the fast track designations, LP-284 as a molecule, all were ideas. Today, we have LP-184 that's going into Phase Ib, Phase II trials, multiple trials as well as has 4 pediatric disease designations and 2 fast track designations and multiple orphan disease designations. We have LP-300 that's in a Phase II trial that's seen some great results so far in a very targeted population called L858. Mutation in non-small cell lung cancer. And we've also developed a whole new drug, LP-284, which is a stereoisomer of 184, which was optimized using our platform, and we brought it to GMP quality, launched a Phase I, have 3 orphan drug designations for that drug. And just yesterday, we talked about a new patent for that as well, and we've seen some good responses in the trial. We also focused on CNS cancers and develop a new subsidiary to focus on these devastating cancers, both in pediatric and adult brain cancers. So we have, we think, a library of amazing work. But the most important thing is that all the molecules that we [ ought ] to develop not only showed themselves to be tolerable and got to a meaningful dosage that is therapeutically relevant in trials, but are actually seeing results, mechanistic results that were thought about using our AI and data-driven approach. We started this AI layer, this machine learning layer to support these programs. Initially, RADR, which was a machine learning platform, which has a lot of accolades, hundreds of ML algorithms, 200 billion-plus data points. But again, focus for us and for our collaborators who was internally focused and continues to be used internally. And it's a team effort. You've got to bring in people who are data scientists and molecular biologists and other multidisciplinary people. And it was really there to compress what traditionally takes place in early development that can take 2, 3, 4, 5 years and try to compress that to 1 or 2 years. As we saw that succeed, we thought, what could we do something even more aggressive? Could we build this into a natural language system that you can prompt without the use of a data engineer, without the use of a multidisciplinary team and still access a lot of the information and algorithms and set them off in an automated fashion. And so at the time, as natural language processing chat GPTs and LLMs were growing, we thought could we create this for rare cancers, could we create an AI for good where we take all of our knowledge, guardrail architecture and focus it on the entire litany of hundreds of rare cancers. initially, it actually started as an experiment internally with the team. We said would it be great if we could take a biomarker or mechanism and just fully address every single cancer and see where it made most sense and automatically rank it and do all the complicated pathway analysis that we do and then actually verify it both at the RNA and protein level, and then have it come back to us with the results. That's great. It would take us -- it's not impossible to do, but it could take days or weeks to do that, enrich for pathways that are both known and maybe not known enrich within proprietary data, look at results, look at different biofomatic analysis, guardrail with existing published literature and iterate. And we thought there's got to be a better way, a more automated way, and that's why we created withZeta. And we our learnings in withZeta were fantastic, and Reed will talk a lot about the architecture later on. And so each layer that we built was built to support and guide the programs. And actually, Kishor will talk about how we've also used theZeta to develop whole new programs. We have an entire library of new molecules that we haven't really talked about, which we'll be talking about later this year. And then eventually, we said the same architecture, we can use not just in rare cancers, but we can use it for other therapeutic development purposes. We can expand it to go downstream. We can expand to go into new disease categories. And we can actually now use a subscription and premium model. There's no reason we have to go door-to-door to every pharma and spend months to convincing them. Let's just open this platform up and let's disrupt the way drugs are being developed. And so again, this -- each layer for us was built to run and [indiscernible] and validate the work that we've already done in the past. And this is very important. We're not approaching this as some kind of data an AI agent building exercise. We're really approaching this as can we empower people to do drug development, the way we would do it faster, deeper, highly parallel and at a level and pace it hasn't been done before. So where do we sit with Open Medicine. We see Open Medicine as a wonderful component to our core drug discovery and development business in cancer. It allows our team to focus on a major new category and our investors to participate in the upside of this AI revolution. So Open Medicine does this not only just for us, but really for multiple categories. And some of the early users are not just biopharma researchers but investment funds, academic centers, clinician scientists globally and actually service providers and CROs. The way I view it is it's a platform that's serving not just us, not just cancer, not just small pharma companies. But really, the whole industry that's invested in the success of drug development and making drug development faster, more precise and more efficient. And that's ultimately the real power of this kind of platform and these tools is to introduce new innovations in not only hypothesis development, but validation allow us to evaluate and but also compete -- generate competing explanations in a way that just hasn't been possible before. And this, to us, is a complementary business, 1 that has a lot of legs beyond just our own capability which is the goal of allowing it to be spun out and grow independently and be valued as an AI business. So as we're developing this webinar, this is very timely. There was an article that came out, in fact, just I think it was dated just 2 days ago in [ Nature ], volume 657 in the -- it was a technology future. And again, this just came out. I just circulated this to my team -- the title of this article is AI tents are revolutionizing how research is done. It's pretty amazing. It's even more amazing because the -- when it was initially published, it was called our changing how research is done. And I look at it again this morning, it altered from changing to revolutionizing. The author [ Eli Dolgan ], sorry if I mispronounced her name, did a great job at highlighting one of the most important things is that humans still need to decide what makes sense. But our ability to get to that point of making sense can be so compressed, and I'll talk about how we're doing that. There are a couple of great quotes that are on the screen here. And these are exactly the issues that we set to solve in doing Open Medicine. We've had years of thinking about it already. The bottleneck is in reliable validation exactly. That's by Anton at Northeastern. That's why we've built a curated knowledge basis, that's why we guardrailed architecture, that's why we created proprietary curated information, that's why we validated all the bio tools. Second, the most valuable part is now actually asking the question, 100% true Kishor talk about that. The quality of your answer is so much driven by the quality of your interaction, just like it is in any relationship. I don't believe in this 1 shot just got anything, "Hey, make me a presentation for Open Medicine. You're going to probably end up with junk. But if you go through and say, these are the things I'm thinking about. These are the must-haves. These are how slides should look. These are the library of content. This is the information that I have in my head. Same with any other thing, the most valuable part is in the asking of the questions, the guidance. Just like any coscientist relationship would be, whether it be human or in this case digital. And one of the other observations from the article is 1 of the protocols that are agentic are not very good. They're like repeats. And I agree. I've worked with a couple of other tools out there in the industry. I won't name names, some of the companies with like fancy French-sounding names and some with generic tech names. They do seem very [ cook bookish]. But again, the opportunity is we were there, too. The opportunity is to make these a lot more specific and less cook bookish, more like very, very tied to the nature of the inquiry and research. And that's what medicine has accomplished. So the field is really now at the area where Open Medicine is already built to solve a co scientist trained on very specific categories, a coscientists built in something from drug programs. And really understanding the prompts, the cost limits and also being able to tell you when your hypothesis makes no sense. So this is where Open Medicine is, and what we've accomplished so far. Let me tell you a little bit about since we've launched it and made this announcement in April. We've had excellent customer feedback with [indiscernible]. We've had multiple specific input from customers across multiple industries, ranging from hedge funds to CROs, to clinician scientists to chief medical officers. We're putting together a world-class advisory Board that we'll be announcing in the coming weeks. We've had several people already say yes. several new invites go out. And that's really critical because the Advisory Board is going to surface market insights that we may not even know about surface functionality, access to new talent give us access to new people, users. Advisory Board is really critical. And again, as a small company, you need those. You need the Advisory Boards to help you get to people, you can't get to help you miss and surface opportunities that you may not be thinking about. We've improved and strengthens the security and scalability of the core architecture. Reed and the team have been focused on expanding the functionality, but also very importantly, running benchmarks to actually improve cost and scalability, which is we believe is going to be absolutely critical. We've also gone fully mobile. That's one of the challenges that we had initially with the platform is how do we have that seamless experience to go from a desktop, which is perfect for but now to your phone or to a tablet where you may want to extend or do things quickly and follow up. And one of the largest recent updates of the platform was for mobile users. And we've seen significant increases in certain classes of users since launching the mobile module a few weeks ago. And we think it's going to be a key ingredient in that seamless experience, but also very importantly, in keeping people and making that experience sticky. And we also have some great ideas. We've started some discussions outside of cancer with institutions and pharma companies. where they will help us go well beyond oncology. We're in discussions and negotiations with an institution to go into neurodegenerative disease with some of their multimodal models, and we're also looking at inflammatory immune disease. And again, we're -- there's only so many of those that we can take on at a time. So we'll be very careful not to overextend ourselves, but to approach new diseases and new modalities where we think it absolutely makes sense and we have the right partners. So how do we see the market developing, which is very, very critical. Again, we see these $1 billion numbers all the time. And I want to talk about how this is a new category. And it's kind of purposeful, but if you look at what I estimate about a $4.5 billion to $5 billion market spend now currently. And the agentic piece, the Agentic AI is tiny. It's barely visible. But if you look at the other pieces, platform deals, about sub-$1 billion in actual real platform revenue. And today, there is a $2 billion deal or $1 billion deal announced. But how that actually reaches the company, the company is not getting $1 billion. It's all on the come later if the drugs maybe work as a result of their platform, tiered out over so many years. And out of that $1 billion, maybe they get $15 million upfront. Okay. I count that $15 million. I don't count the billion. In services and consulting, tons of companies offering services and consulting. We'll build your knowledge graph. We'll do bioinformatics for you. We'll take your data to show you how you can do a [ basin ] model. We'll do an of 1 analysis. We'll redo your trial, we'll find and rank combination agents, huge industry of services and consulting, whether it's data, all focused on drug development and then software, great software companies out there, whether it be [ Cortera or Schroding ] or others. But the big thing, and when I spent several days modeling this a couple of weeks ago. One of the things that struck me is that services, consulting and software actually not going to grow. It's going to be disrupted. The growth rate net of inflation is pretty marginal in those businesses. And that's where AI is disrupting not only drug development, but actually if you look at any industry. The agentic AI is eating consulting and traditional software. That's why SaaS is really affected. No one wants to pay $13,000 per seat for rigid docking maybe with some quasi-decent flexible docking and maybe some large-scale QSAR analysis. Who cares? To me that's table stakes in the future. No one's going to pay that per seat. We do it inside of Open Medicine. We do it inside of Zeta. Why not have it as part of a larger bundle for drug development. Same with consulting. Why do I need to go hire buying from a [indiscernible] or 20 of them when I can have my own buying from a [indiscernible] use theZeta bioinformatic toolkit and do the work of 3 or 4 or 5 people. So this is changing how we see the market developing. And specifically, agenetic AI will disrupt legacy software and services. About $3.8 billion, I estimate, of today's revenue that's eaten up by software and service companies will be actually flat to negative and close to $1.5 billion to $2 billion of it will move to agentic delivery. That's a big number, and it's pretty fast. And if you look at even the rates of usage, even the people using these platforms, it will be very fast. There was an analysis done by the economist that I think NVIDIA also is involved somehow. So obviously, there may be a bias. But I thought it was very good word. The analysis suggested that agenetic AI revenue in financial service institutions among the top users, of course, had grown from nearly 0 to 3 years ago to almost $7,000-plus per user on average. I think that's probably right. It probably is even higher. And consulting companies, you've seen this across a lot of consulting companies. I sit across the street from one of the big consulting companies named after a major city in Massachusetts. And down the street from another one that's kind of have the Scottish heritage name. But consulting is booming, but they're not hiring. And in fact, the word on the street is that it's flat, flat to negative because they're using agenetic AI to do a lot of that work. The same thing is going to happen in drug development. So I see that market in legacy software, legacy services crumbling and all going toward agenetic AI. The other thing that I see, and if you see this in the chart that I've shown. If you look at platform deals, explode. I do think that platforms and providers of platforms like ourselves will take more on the come. And someone says to me that, "Hey, we only want to pay you $1 million a year. for your neurodegenerative module. And if 1 of our 5 candidates comes out as a result of it, and we'll give you $0.02, 3%, 2%, I would take that. So we see that companies that have these Agentic platforms because they can scale on their own and they offer a level of scale that you've never seen in the software industry, people are going to be more willing to take that risk. The risk that a traditional software and SaaS provider has not been able to take. And so that's going to cause more drug candidates sooner and faster. The agenetic systems will hold a very important place and accelerating that because you can run multiple hypotheses in parallel, fail earlier, pivot faster and advance more candidates. So that's how I see this. And this is very important to how we're architecting and thinking about Open Medicine. So going into what this causes, and this is very important because what you're hearing from me, and I think for most people to [ Vanguard ] of agentic AI software is that you're seeing simultaneous disruption and enablement at the same time. And this is going to create a new generation of scientific work. Every prior computational wave made a single path cheaper. You don't have to go to the store and drive the Walmart to buy games. You can buy them online. Now they can be served online. Now they can stream online. But agentic changes the shape of that whole path itself and the execution, everything from the ideation and search to actually how it's accomplished. You can run multiple hypotheses in parallel at the cost of what it traditionally took only 1. The value is now going to be measured not just in hours provided or service or consulting fees, but in terms of the time to innovation and the encoded judgment. And very importantly, knowledge is going to be constantly created and encoded into the system. That has not been possible before. You can see these legacy tools, these legacy CRMs, these legacy software packages, these legacy [indiscernible]. You create stuff, but it doesn't get re-encapsulated into the knowledge base and the core workings of the software. That's changing now. And because of that, we can run these multiple programs fail earlier, pivot faster. And the systems will now be valued not only on the cost , but really it's speed and parallel enablement, not just selling seats or [ butts ] and seats and licenses, you're going to be selling your ability to execute and do things faster. And so with that, we're going to dive into actually seeing that work in a demo. And before I do that, I'm going to ask my colleagues, Reed and Kishor who are on with me to also introduce and give a little bit of background on what they do at the company and for Open Medicine as I pull up the live demo.

Go ahead, Kishor.

Thanks, Reed. Thanks, Panna. I'm Kishor Bhatia. I am the Chief Science Officer for Lantern Pharma. I've been with Lantern Pharma for about 5 years, began mostly with focusing on the preclinical aspects of our drug pipeline. It was very exciting, but slower. Here's the advent of RADR and things kind of jumped up several magnitudes. Perhaps what I would like to do in the next 5 minutes is just share with you some recent interactions with Open Medicine to kind of give you a sense of what this enhanced excitement of this [indiscernible] about. And in trying to have the dialogue with you, I thought perhaps what I'll do is share a couple of case studies, one that -- where we engaged Open Medicine to design first-class molecules, to the areas of uncovering synthetic little pathways or identifying rectal drug repurpose in combinations for [indiscernible] cancers. So the -- and so I understand that each of these interactions involves a different personnel within Open Medicine. As you probably understand Open Medicine platform has several personals, including medicinal chemists, transportational biology and so on and so forth. So one of the more recent challenges that are posed to Open Medicine came from a question that arose in my mind after a recent paper in Nature target -- talking about cheroptosis, which is a pathway that causes cancer cells to die, particularly certain specific cancer cells. And I wanted Open Medicine to synthesize a drug that uses this pathway. And to summarize, Open medison rapidly synthesized insights across several different parts of this puzzle across the theraptosis, across lysosomal targeting, across [indiscernible] chemistry and the proposed old design strategy that created a hybrid molecule which combines lysomotropic accumulation and iron mobilization in a single structure, like working with chemist Persona, it identified a scaffold or known scaffold [indiscernible] and added a [indiscernible], which resulted in a novel molecule. It turns out that based upon other analysis. This is a first-in-class iron activator, capable of triggering a synergistic pyroptosis cascade that bypasses conventional resistance mechanisms. And so within an hour, this platform delivered a fully synthetic route for the drug, which has physiochemical drug-like properties, a traditional development plan and identifying the right indications where such a drug could be used. Now clearly, all this needs still further validation, which we are doing. But I think the point I'm trying to make is that trying to put together all this data and putting together a hypothesis that can be validated or drug that can be synthesized within a short time is just not feasible if 1 were to do without such a platform. To give you another example, I think I'll take the example of understanding how I will try to extend indications, further indications for which LP-184, a drug can be used. So I was interacting with Open Medicine to ask similar questions. And it turns out that Open Medicine, identified very specific gene called STK19 as a potential pathway to use to expand the indications of LP-184. This to me was very, very surprising because my recollection was that STK19 is a kinase. And why would Open Medicine connect STK19 with LP-184, which is a DNA Imaging drug that requires a specific DNA repair pathway. It turns out that I had missed a critical flaw that was published very recently. The fact that Open Medicine could pull out the more recent information which showed that STK19 is also a DNA repair protein was quite surprising. Nonetheless, what STK19, what Open Medicine then was identify a convergent pathway that expanded the synthetic lethal mechanisms of LP-184 to cancers that we were not thinking about to cancers that had biomarkers that we were not thinking about. And clearly, I think both those case studies demonstrate how the utilization of the right questions and the right [indiscernible] of interrogating with Open Medicine allows to open doors that would be difficult, if not impossible, for a small team to get into.

Thank you. I'll introduce myself real quick and then hand it back over to you, Panna, for the demo. But my name is Reed Bender. I'm the lead platform architect behind with data and Open Medicine. So my role has been in developing the infrastructure and the agent itself behind withZeta. And before that, I was working with [indiscernible] as a data engineer and my role within compiling all of the data sets within the RADR team that ultimately became the foundational knowledge base for with data. So I've really enjoyed working across the whole stack and beginning with the data itself, building a base for withZeta, the rare cancer knowledge base, our oncology of the rare cancers and then expanding it beyond literature search or simple lookup adding in really complex analytical tools like computational biology and pathway enrichment tools, those have all been fantastic as well as chemical structure tools like we have theZeta is integrating, integrated for generating real chemical structures and reasoning over [indiscernible] which normal LLMs are generally pretty bad about. So it has been quite an adventure and very fun to develop this platform with a great team that we have and it's calling it to see it in demo here.

Great. I've started the demo in parallel. I think Kishor mentioned paraptosis and we got a great answer ranking by created kind of a teriptosis therapeutic index. Not only did it think about a mechanism, but it felt like a scientist, it said, in order to do a ranking, it created sensitivity, times, clinical need, times, feasibility. So just the way the scientists or a consultant would think and then give us an answer to the top 5 cancers to think about. And then also the mechanistic convergence on the therapy-resistant [indiscernible] states that unify the why these cancers are being ranked. And very importantly, as it does this, it creates a knowledge graph, which is one of the areas of feedback that we got that people really love is the knowledge graph and I'll show you the acknowledge graph quickly, and I know we're running out of time. But just like any knowledge worker inside your head, you're going to create a knowledge graph that associates things. And we gave it a very strict oncology to think about disease, drug, gene molecule. These are the things that you as a drug developer or scientists think about. And of course, there might be things out there that aren't associated yet with your knowledge graph. So you can see there are concepts like this this specific gene [ FAMP ]8. It wants to connect it to something, but it hasn't yet. But that's always going to be important. It's always any part of knowledge. There's -- and as it grows, you'll see the knowledge graph grow as well. After we looked at ferotopis, we then asked Zeta to design, give us a design of some feraptosis inducers. And as you see, it's working on this right now. So it's working on this. into take a few more minutes. It gives itself a design strategy with key benchmarks. And it's going to work to try to optimize against those benchmarks and either is going to get there or it's not. And if it decides not to get there, it's going to ask you, can we sacrifice this? Just like a scientist would. It's not going to give you an hallucination and get there. And again, it starts by going out and using tools, you can see which tools it's using, like MBL, molecular descriptive tools, validating smile strings. And again, these are all tools given each of these personas access to. And so one of the important things to think about, and I know we have got a couple of questions piled in. And so I'll make sure we get to the questions, and then go back to sharing my screen in the presentation mode, if I can find my Zoom screen, give me a second. Okay. Hopefully, can you guys see the live screen properly?

Yes, we're looking at the PowerPoint.

Great. So like I said this, this new generation of using these tools, and I urge you guys to definitely use the tool because it's definitely can spend hours, again, use the code withZeta 14 for those of you on the call. And that way, you'll be able to access all the professional tools as well. And let's keep going through this. The key thing you'll see is that in using it, you have one question that's carried end to end, not a series of disjointed questions like you're seeing a lot of other tools. So you're going to have like a question, a drug developer would actually ask, the handoff goes through all the different personas automatically and to the levels of recursive thinking that are required. And then it's a biased action. It's in -- Zeta is all in to going to try to get you to go to the next stage to try to develop the drug. And also, as you see in action, it will actually refuse comparisons that are going to generate a false discovery. And that's very important. And it's also going to prioritize druggable biomarkers or things that are in high patient need and involve you in the cycle or the loop. And this is an important point of Open Medicine, what differentiates it from other platforms. it generates and proposes and reasons and then allows you to continue being in the decision loop. So let's talk about who's using the platform, what we've learned, which is critical. We've seen that biopharma R&D teams are asking questions largely about translational analysis. And their big feedback is they want to get ability to upload proprietary data. Academic and translational labs are using it to build cohorts, to do biothermatic analysis because that's, again, it's expensive and hard to get to. But they also want to do group sharing. They also want to get alerts and new research. Service providers are some of the most extensive users. There are early adopters actually. These are CROs, regulatory groups, bioinformatic groups, and they want to embed these co scientists as part of their client work. They want to create project codes. They want to save into different formats and starts going into what we think of as kind of the enterprise features. And then we have independent developers, which are very exciting, that are trying to run investigations end to end that previously where they needed a full department. They want to upload their documents and some of these developers, if they're part of larger teams, they want to actually do on-premise deployment. And we'll talk a little bit about some of the service deals that we're looking at doing as well. So power users, I took a couple of quotes from conversations I've had over the last few months. We've got users that want to bring their own methods, upload their -- some of their own models that they've been working on, but take advantage of the recursive thinking and the deployment that we have, others I want to bring their own data we have groups that want to do multiple scientists and agents, not just limited to one work stream. So when they ask a bioinformatic question, they want to launch a series of bioinformatic streams in parallel, multiple approaches, not just paraptosis induction, but multiple different mechanisms, which we're looking at deploying what we think of as a swarm technology. We've had people ask about, and this is recurrent. My team needs to share and build on work. I want to see what Reed is doing or Kishor and have a work folder. We want to take it further downstream and do regulatory-type work. And then we've had larger groups, including some bigger centers asked for on-site deployment. We would love to deploy this across all of our groups or work teams that are in Europe and in Cambridge and in San Diego, but can we do it on-prem. We don't want to do it in the cloud. So we've got over 200 users and it's growing each month. Super users are very, very sticky is what we're finding, several doses actions and questions every week. Most usage is an investigator mode, which gives us some thoughts in terms of how to make it more efficient. And one of the most surprising things is we've had hundreds of new molecules generated by a handful of customers, not just ourselves, but people who are really using the small molecule and are asking for other modalities as well. So what are some of the discussions that we're having. And then we'll get into some of the Q&A, I know that's piling up. Biopharma co-development. A lot of patient and disease groups actually have -- and we think that's a great way to get sticky new customers. We actually had a couple of funds and financial institutions. So I want to use it for decision-making, diligence and want to do it in a proprietary client manner. We've had service providers want to integrate it and embed it into their own workflow, and we've had academic groups that want to use it with their own proprietary literature or with their own models or share their own libraries that they have and more and more recently, technology groups, groups that are at the frontier models that want to use this to power their own specialized tools or incorporate their specialized tools, think of like a unique group working on peptides or unique group working on bispecific protein constructs that it wants to make available. And then interestingly enough, governmental and regulatory agencies. Because of the rare cancer focus, we've actually had a lot of interest from groups in rare disease, rare cancer to use it for fact checking knowledge base, proposal review and creation of knowledge perhaps. So a lot of excitement around where this is heading. And where it goes next. I want to talk about 4 specific categories that are really very important new disease categories, which we'll do with partners and knowledge groups, neurodegenerative, immune. And I think we'll talk about some of these in the coming weeks. We've got some deals that we're working on. Novel therapeutic modalities, trial into regulatory filings. These are new functionalities like trial benchmark and support for FDA filings, trial design analysis cohorts and then proactive portfolio intelligence. And this is important because -- all this is not just knowledge synopsis and knowledge briefing, it's actually creating new knowledge. And it's creating new forms of analysis or running analysis in parallel. And when the marginal cost of analysis approaches 0 and the constraint moves to hypothesis quality, decision judgment and time to innovation. And I think that's exactly what we traded Open Medicine for us to handle. And marginal cost is going to continue approaching 0. And then the tools and technologies that are going to win are going to be those that offer is massive improvement in time to innovation and decision judgment. So this is really important, we think, for the future. This is a great example for AI for good. And because every person on this call has the potential to be a patient at some point in the future. And that's the reason why this company exists. It's a reason why these tools were built in the first place. And I think Open Medicine AI is how we make this available to everyone, who wants to get a therapy to a patient. It's not just pharma companies and big pharma companies. It's clinicians, scientists, research groups, the empowered citizen scientists, the small biotech that wants to do more. We all want to get therapies and therapeutic programs across the next stage faster and with greater validation. And these are the kind of tools that will empower that we think this is going to be quiet, but very important revolution in AI and in medicine. So with that, I'm going to turn to a few of the questions.

I think we had a question from Michael King at Rodman, is that right? Michael, I think your line is open.