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Good afternoon everybody. We're going to go ahead and get started on our call and webinar here in a few minutes. We are 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. Kishore Bhatia and Reid 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 roadmaps, estimates, etc. You can also access our annual and quarterly reports under the Investor SEC Filings tab of our website, and also at sec.gov. All forward-looking statements in this presentation represent our judgment as of today, the date, September 23rd, and we disclaim any obligation to update any forward-thinking 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. Kishore Bhatia and Reid 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 roadmap evolving both in terms of usage and in termsof growth of this amazing platform. So again, I 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. Okay. Can everyone see the presentation? Yeah, we can see it. Great. So we're going to talk about OpenMedicine. 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 was initially to accelerate our own drug development because we had to we were a small emerging company but we've what we've 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 agentic 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 have you 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 gonna talk about the roadmap 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 gonna a lot about 45 minutes we're going to try to end our discussion and demo around 1 o'clock 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 an 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 viewed ourselves and our focus was really singular can we get cancer drugs faster and less capital and we built the tools and we've built the methodologies and we built the approach because that's what we needed. That was the way that we could do it. And we built this on the back of clinical programs. At the time, we had no clinical programs. They were all preclinical. In fact, some of these concepts didn't exist. The indications, the fast-track designations, LP284 as a molecule, all were ideas. Today, we have LP184. That's going into phase 1b, phase 2 trials, multiple trials, as well as has four pediatric disease designations and two fast-track designations and multiple orphan disease designations. We have LP300 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, LP284, which is a stereoisomer of 184, which was optimized using our platform, and we brought it to GMP Quality, launched a phase one have three 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 developed a new subsidiary to focus on these devastating cancers and 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 set out 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, Radar, 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. It 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 two three four or five years and try to compress that to one or two 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, you know, wouldn't 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 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 with your own proprietary data look at results look at different bioinformatic 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 how we created with Zeta and we are learnings and with Zetta were fantastic and Reid 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 Kishore will talk about how we've now also used with ZETA to develop whole new programs we have an entire library of new molecules that we haven't even really talked about which we'll be talking about later this year and then And 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 go into new disease categories, and we can actually now use a subscription and freemium model. There's no reason we have to go door to door to every pharma and spend months convincing them. platform up, and let's disrupt the way drugs are being developed. And so again, each layer for us was built to run, influence, guide, 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 and 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, cheaper, highly parallel, and at a level and pace that hasn't been done before. So, where do we sit with open medicine? We see open medicine as a wonderful complement 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 allow us to produce new innovations in not only hypothesis development but validation allow us 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 one that has a lot of legs beyond just our own capabilities 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 two days ago in nature volume 657 in the it was a technology feature and again this just came out I just circulated this to my team the title of this article is AI co-scientists 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 are changing how researchers done and I looked at it again this morning it altered from changing to revolutionizing the author Eli Dolgan Ellie 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 screen here, you know, 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 built curated knowledge bases that's why we guard railed architecture that's why we created proprietary curated information that's how we validated all the bio tools second the most valuable part is now actually asking the question 100 true because sure we'll 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 these one-shot, just ask 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 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 co-scientist relationship would be, whether it be human or in this case digital. And one of the other observations from the article is once the protocols that are generated are not very good. They're like recipes. And I agree. I've worked a couple of other tools out there in the industry and I won't name names. Some with big companies with like fancy French sounding names and some with generic tech names. They do seem very cookbookish. But again, the opportunity is, you know, we were there too. The opportunity is to make these a lot more specific and less cookbookish and more like very, very targeted to the nature of the inquiry and research, and that's what open medicine has accomplished. So the field is really now at the area where open medicine was already built to solve. A co-scientist trained on very specific categories, a co- scientist built inside and from drug programs and really understanding the prompts 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 loops we've got 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 will 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 give us access to new talent give us access to new people users advisory boards are really critical and again as a small company you need those you need this advisory boards to help you get to people you can't get to to help you miss and surface opportunities that you may not be thinking about you've improved and strengthens the security and scalability of the core architecture Reid 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 you know how do we have that seamless experience to go from a desktop which is perfect for it 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 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 where they will help us go well beyond oncology when 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 billion dollar 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 to be about a four and a half to five billion dollar market spend now, currently. And the agentic piece, the agentik 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 was a $2 billion deal or billion dollar deal announced. But how that actually reaches the company, the company's not getting a billion dollars. It's all on come later if the drugs maybe work as a result of their platform tiered out over so many years and out of that billion dollars maybe they get 15 million up front okay i count that 15 million i don't count the billion in in services and consulting tons of companies offering services and consulting we'll build you a knowledge graph we'll do bioinformatics for you we'll take your data to show you how you can do a bayesian model we'll end of one analysis we'll redo your trial will find and rank, combination agents, huge industry of services and consulting, whether it's data, but all focused on drug development. And then software, great software companies out there, whether it be Surterra or Schrodinger or others. But the big thing, and I spent several days modeling this a couple of weeks ago, one of the things that struck me is that services, consulting and software are 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 gets really affected. No one wants to pay $13,000 per seat for a 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 gonna pay that per seat. We do it inside of Open Medicine. We do 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 a bioinformatician, or 20 of them, when I can have my own bioinformatician use the with Zeta bioinformatics toolkit, do the work of three or four or five people so this is changing how we see the market developing and specifically agentic AI will disrupt legacy software and services about three point eight billion dollars I estimate of today's revenue that's eaten up by software and service companies will be actually flat to negative and close to one and a half to two 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 of people using these platforms, it will be very fast. There was an analysis done by The Economist that I think Nvidia also was involved somehow so obviously there may be a bias, but I thought it was very good work. The analysis suggested that agentic AI revenue in financial service institutions among the top users of course had grown from nearly zero three years ago to almost $7,000 plus per user on average I think that's probably right probably is even higher and consulting companies have you seen this across a lot of consulting companies they sit across the street from one of the big big consulting companies named after a major city in Massachusetts and down the Street from another one that's kind of has a 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 a Gentic AI to do a lot of that work. The same thing's going to happen in drug development. So I see that market in legacy software, the legacy services crumbling and all going toward a Gentik 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. If someone says to me that, hey, we only want to pay you a million dollars a year for your neurodegenerative module, and if one of our five candidates comes out as a result of it, and we'll give you 10%, 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. And agentic systems will hold a very important place in 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 ist very important because what you're hearing from me, and I think for most of people, the 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 to Walmart to buy games. You can buy them online. Now they can be served online. Now they 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 one. 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 chemistry packages, you create stuff, but it doesn't get re-encapsulated into the knowledge base and into 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 the speed and parallel enablement. Not just selling seats or butts in seats and licenses. You're going to be selling on your ability to execute and do things faster. And so with that, we're going dive into actually seeing it at work in a demo. And before I do that, I'm going to ask my colleagues, Reid and Kishore, who are on with me, to also introduce themselves 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. Hey, thanks, Reid. Thanks, Manav. I'm Kishori Bhatia. I am the Chief Science Officer for Lantern Pharma. I've been with Lantern Perma for about five years. began mostly with focusing on the preclinical aspects of our drug pipeline. It was, you know, very exciting, but slow work. Here's the advent of radar and, you know, things kind of jumped up several magnitudes. Perhaps, you know, what I would like to do in the next five 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 discovery is about. In trying to have the dialogue with you, I thought perhaps what I'll do is share a couple of case studies, you know, one that where we engaged open medicine to design first-in-class molecules to the areas of uncovering synthetic lethal pathways or identifying rational drug repurposing combinations for rare cancers. So, and so understand that each of these interactions involves a different persona within open medicine you know as as as you you probably understand uh open medicine uh platform has several personas uh including medicinal chemists uh translational biologists so on and so forth so one of the uh more recent challenges that i posed to open medicine came uh from a question that arose in my mind after a recent paper in Nature, talking about ferroptosis, 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 And to summarize, Open Medicine rapidly synthesized insights across several different parts of this puzzle, across the theraptosis, across lysosomal targeting, across medicinal chemistry, and then proposed a bold design strategy that created a hybrid molecule which combines lysotropic accumulation and ion mobilization in a single structure. When working with this chemist persona, it identified a scaffold, a known scaffold, artemisinin, and added a tertiary amine, which resulted in a novel molecule. Turns out that, you know, based upon other analysis, this is a first-in-class ion activator capable of triggering a synergistic ferroptosis 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 translational development plan, and identified 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 pull together all this data and putting together a hypothesis that can be validated a drug that can been synthesized within a short time is just not feasible if one 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 or a drug can be used. So I was interacting with Open Medicine to ask similar questions, and it turns out that Open Medicine identified a very specific gene called STK19 as a potential pathway to use to expand the indications of LP184. This to me was very, very surprising because my recollection was that STK-19 is a kinase and, you know, why would open medicine connect STK19 with LP184, which is a DNA damaging drug that requires a specific DNA repair pathway. 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 OpenMedicine did was identify a convergent pathway that expanded the synthetic lethal mechanisms of LP184 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 the utilization of the of right questions and the the right becomes of interrogating with uh open medicine allows to open doors that would be difficult if not impossible for, you know, a small theme to get into. Thank you. I'll introduce myself real quick and then hand it back over to you, Pana, for the demo. But my name is Reid Bender. I'm the lead platform architect behind WithSata and OpenMedicine. So my role has been in developing the infrastructure and the agent itself behind WithStata. And before that, I was working as a data engineer and my role was in compiling all of the data sets within the radar team that ultimately became the foundational knowledge base for with zeta so i've really enjoyed working across the whole stack and beginning with the data itself building out the core knowledge bases for zeta the rare cancers knowledge base our ontology of the rare cancers and then expanding it beyond you know a literature search or simple look up but 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 ether zero integrating integrated for generating real chemical structures and reasoning over smile strings which normal lms are generally pretty bad about so it has been quite an adventure and very fun to develop this platform with the great team that we have and it's cool to get to see it in demo here great i've started the demo in parallel um i think kashore mentioned ferruptosis and And we got a great answer ranking by creating kind of a theraptosis therapeutic index. Yeah. Not only did it think about a mechanism, but it thought like a scientist. It said in order to do a ranking, it created sensitivity times clinical need times feasibility. So just the way a scientist or consultant would think. And then gave us an answer of the top five cancers to think about. and then also the mechanistic convergence on the therapy resistant mesenchymal 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 knowledge 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 ontology to think about disease, drug, gene, molecule. These are the things that you as a drug developer or scientist think about. And of course, there may 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, VAMP8. 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. And as it grows, you'll see the knowledge graph grow as well. After we looked at ferritopsis, we then asked Zeta to design, give us a design of some ferritosis inducers. And as you see, it's working on this right now. So it's work on this. It's gonna take a few more minutes. It gives itself a design strategy with key benchmarks. And it's gonna work to try to optimize against those benchmarks. And either it's going to get there or it's not. And if it decides not to get, it gonna 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 ChemBL, molecular descriptive tools, validating smile strings. And again these are all tools that we've given each of these personas access to. And so one of the important things to think about, and I know we've 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 you guys can, can you guys see my screen properly? Yep. Yeah, 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 you know definitely you can spend hours again use the code with zeta 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 that 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'll see in 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 the levels of recursive thinking that are required and then it's going to bias toward action it's going to zeta is always going to try to get you to go to the next stage to try and develop the drug and also as you see in action it'll 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 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 do bioinformatic analysis because that's Again, it's expensive and hard to get to, but they also want to do group sharing. They also want alerts and new research. Service providers are some of the most extensive users. They're early adopters, actually. These are CROs, regulatory groups, bioinformatics groups, and they want to embed these co-scientists as part of their client work. They want to create project codes. They want save into different formats, and it starts going into what we think of as kind of like 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 own documents, and some of these developers, if they're part of larger teams, they want to actually do on-premise deployment. 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 had users that want to bring their own methods, upload 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 that want bring their data. We have groups that want to do multiple scientists and agents, not just be limited to one work stream. So when they ask a bioinformatic question, they want to launch a series of bioinformatics streams in parallel, multiple approaches, not just a ferruptosis induction, but multiple different mechanisms, which we're looking at deploying in 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 Kishore 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, ask 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 dozen interactions and questions every week. Most usage is an investigator mode, which gives us some thoughts in terms of how to 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 this for a lot of small molecule and are asking for now other modalities as well so what are some of the discussions that we're having and then we'll get into some of 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 that want to use it for decision-making, diligence, and want to do it in a proprietary compliant 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 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 by specific 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 and rare cancer to use it for fact-checking knowledge base proposal review and creation of knowledge graphs so a lot of excitement around where this is heading and where it goes next I want to talk about four 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 and regulatory filings. These are new functionalities like trial benchmarking, 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 zero and the constraint moves to hypothesis quality decision judgment and time to innovation and I think that's exactly what we created open medicine for is to handle the and I marginal cost is going to continue approaching zero and then the tools and technologies that are going to win are going to be those that offer this 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 kind of company exists it's a reason why these tools were built in the first place and I think open at 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 a quiet but very important revolution in AI and in medicine. So 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. I'll read it because it's in chat. Oh, go ahead. Go ahead, Michael. I was going to ask, you know, since most everybody would be developing, you know, pharmaceutical and biotech AI LLMs or sourcing their data from, you know, public sources, what do you say open medicine differs? How does it differ from those clients that clients can access from other AI providers? And what would you say are the top one or two advantages that you would highlight for open medicine? Yeah, I think the biggest one is harnessing insights from the disease. That is not trivial to generate, so that's a key advantage. Also having tools that are guardrailed from producing false results, and that requires a lot of disease specialization and looking at competing explanations which we've worked through. The models that sit on top of that to enhance the answer, again, those are things that you have to come from inside the drug development, not just gather data. Gathering data alone is not going to make you a better scientist. In fact, it would probably create a worse scientist. It's generating knowledge from judgment, and that's what we've trained our models and open medicines models to do, and that's differentiated. The next one, which is very important, which you've asked about, is its recursive ability to think and generate new knowledge that it puts back into its thinking process. That kind of recursion is very expensive using a lot of the frontier models, and we've made it a log or two different in terms of cost, which is critical. If you compare the speed of our platform to other platforms, there's a big difference as well. I think there are a lot of technical deployment differences, and I'll let Reid expand on that, but also I think going beyond just summarizing existing literature to actually generating new and competing innovative thought and structuring that back into the ontology of reasoning is something that's unique. Reid, do you want to talk a little bit about the technology aspects as well? Yeah. I mean, I would just mention the cost of it, first of all. We've done a lot of work to make sure, you know, if you use an open-ended agent, we've done benchmarking on this as well. These tools like open claw, open code, clawed code, these types of things, oftentimes you will get to a half decent answer, but it's going to take a lot of time and a lot of tokens churning through that and searching through the internet, searching through just open ended sources. Whereas largely our goal with Zeta was to, for the disease that we're particularly interested in this case which being rare cancers having all of that pre-indexed pre-curated in postgres accessible databases to the agent and then building the computational biology tools on top of that not just so that you know any agent can go out to the internet download a data set and start doing work on it but having all of that data right there with the methods for processing it immediately available to it that is something that none of the other platforms right now have available to it great thanks no problem another question i'll uh i think this is from mac is how will open medicine ai's roadmap be funded we are pursuing a separate round of financing at the oma i level and proceeds from that would obviously flow some through lantern for the ip license and and some ongoing services support and agreement. And we may sell part of Lantern's stake as the company grows. We think it can be very, very lucrative, especially as the AI companies and AI revolution continues. We would be very open to selling the stake. It's 100% owned by Lantern today, so the first round of funding, I'm sure we'll sell a piece of that, but still be a pretty significant owner and then continue selling as we need to. Second part, we do not have an ATM that's active right now, so I'm not sure if that's kind of separate, but yes, thank you for that. Any other questions, feel free to type them, or if you want to raise your hand, I can also. I think there is a question. Go ahead. Question from Michael at Rodman. Yeah. Hey, thanks for taking the follow up. The question we have over here is how does Lantern benefit from all of this other than the equity stake? Are there you know, will this economically benefit Lantern as well as open medicine or how do you envision the economic split? Yeah, we do. You know, again, I don't want to talk in front of some of the agreements that are being made, but we do expect to have downstream access to a revenue share, revenue split. and services to do some ongoing improvement to the models, especially in our area of expertise, which is cancer. But we expect part of Open Medicine's unique business model will be that it'll enter into agreements with experts in certain disease categories so that its focus can be on the deployment and engineering and making all these tools available to the entire industry. So we see revenue sharing, licensing, and then most importantly, we do see that we'll sell our stakes as we need to. The other thing that's been very important for us is that we have a whole new generation of molecules. We haven't talked about it, but we'll talk about it in the coming months. But as Kishore pointed to, we have an number of very unique first-in-class mechanisms. and we think a lot of those will have a lot of interest in the pharma community and of course those belong to Lantern we'll develop them and sell them off and we've it took us a couple of years to take our molecules into trials and obviously to validate that they made sense I think the next wave of our molecules will be highly compressed we can do it faster and cheaper and we're already down that path we're actually already validating a couple of these molecules to make sure that they're actually synthesizable in a very doable way. So making sure they're synthetically feasible, deployable, and we've got some exciting new molecules that we'll be putting on into our pipeline as a result of the work with OpenMedicine. And it also helps us expand on newer indications that we would never have thought about for existing molecules too. Yeah. And another question, I think this is kind of in separate formats. We do expect to announce additional deals around open medicine. Again, our focus has largely been on getting this up and deployed and scalable, usable in the mobile setting, ready to do, you know, to pass scrutiny for security and all the traceability and stuff of that nature. and our next generation of focus is on larger kind of pharma and enterprise type deals but we do have a base of users that is continuing to grow on the subscription basis we do expect subscription prices to be increased to in the next quarter so but yes we are pursuing actively now additional sources of revenue generation with larger groups and institutions so that I'm getting flag that we've gone over by about 10 minutes. Any other questions? Okay with that I'm gonna stop sharing but again as I mentioned we do have you know if anyone is interested in open medicine we're happy to take on additional calls or conferences we are out and beginning to have discussions with not only institutions but institutions that both would be users and financial partners so larger pharma companies and technology companies that see this as a critical unique and differentiated position in kind of the landscape of where medicine is going using agentic architecture again we're at the forefront of this and you know everything that we're we've built is actually deployed scalable this is not kind of we're looking at deploying this I urge you guys to go look and use the tool to really understand how different it is and how our agents divide up these complex drug development problems and are able to give meaningful and useful answers that we think will be very valued by the pharma and drug development community so thank you everyone for joining me sorry for running 10 minutes over but I think this was very useful and again urge you guys to contact us for more questions more discussions or most importantly looking at the platform even closer thank you very much for your time today