Big Pharma Fails 50% of the Time in Phase Three. AI Can Fix That | Vin Singh, BullFrog AI

[00:00:00] [SPEAKER_01] The point of this is to increase likelihood that a clinical trial will succeed.

[00:00:06] [SPEAKER_00] Considering it takes 10 to 15 years and 1 to 2 billion dollars to develop a drug, it's quite shocking that Big Pharma fails 50% of the time in Phase Three.

[00:00:15] [SPEAKER_01] Are you concerned about competition from Big Pharma who may be developing?

[00:00:19] [SPEAKER_00] We discovered the biological drivers of those diseases. We believe we've discovered the genes that drive depression, bipolar, and schizophrenia. From a one-of-a-kind data set that does not exist anywhere in the world.

[00:00:32] [SPEAKER_01] It seems that you would be an acquisition target by Big Pharma.

[00:00:37] [SPEAKER_00] Let's see if we can get one of these drug discovery AI deals done, or target discovery deals done.

[00:00:45] [SPEAKER_01] Introduce yourself to listeners, how you got to Bullfrog.

[00:00:49] [SPEAKER_00] Craig, thanks for having me today. Great to be here. So, you know, my name is Vin Singh. I'm the Chairman, Founder, and CEO of Bullfrog AI. We are a publicly traded company under the symbol BFRG on NASDAQ. So, obviously there are certain things I won't be able to say or questions I might not be able to answer, but looking forward to it. So, a little bit about me. I have almost 30 years of industry experience.

[00:01:20] [SPEAKER_00] Life sciences, biotech. I'm a biomedical engineer by training. I'm a three-time founder of investor-backed companies. Two of them have gone public. The first company I co-founded is a company called Maxite, symbol as MXCT, as a cell therapy systems company. When they went public on NASDAQ several years ago, I think the market cap was around 1.5 billion.

[00:01:48] [SPEAKER_00] Obviously, biotech's been beaten down pretty hard over that period of time. After that, I founded a company called Next Healthcare, which was at the time the world's first adult skin cell and stem cell banking company for return to medicine applications. And that company's still operating and doing some different things today. And then, about eight years ago, I founded Bullfrog.

[00:02:13] [SPEAKER_00] And the reason I founded the company was I was, you know, very surprised by the high failure rates in late-stage clinical development. And, you know, considering it takes 10 to 15 years and $1 to $2 billion to develop a drug, it's quite shocking that big pharma fails 50% of the time in phase three. It didn't make sense to me.

[00:02:39] [SPEAKER_00] And I also realized if I could make a difference, there's a lot of people that will benefit from drugs that maybe otherwise wouldn't. And I think the trickle-down effect would be, you know, drugs would be more affordable in general if you have more success, right? Because somebody's paying for all those failures, right? And it's unfortunately, it's the healthcare system that's paying for it.

[00:03:05] [SPEAKER_00] So, you know, I started the company and the original focus was, why don't we try to rescue failed drugs? Because there's like an ocean of them out there. But, obviously, you know, it's still something that we're interested in. But when I had started first, I realized, wow, that's going to require a tremendous amount of capital. And the funding environment is not what it is today, right? Where many billions are just flying around, right?

[00:03:33] [SPEAKER_00] Back then, you know, eight years ago, AI, artificial intelligence was not really, you know, a well-known term. You know, now it's like every human being on Earth knows what it is. So it's just, we're in a different world. But, so I started the, you know, the company, I, you know, I realized like, you know, technology is the key here, right? Yeah.

[00:03:59] [SPEAKER_00] And I had relationships with Johns Hopkins University. I did my MBA there and I was a mentor in residence as well. And, you know, I was hunting around and it turned out that they had a subsidiary called the Applied Physics Lab or APL.

[00:04:17] [SPEAKER_00] And, you know, one thing led to another and, you know, discovered that, hey, this is a multi-billion dollar subsidiary that has incredible technology and does very sensitive defense work. And, like, you can literally, can't even get in any of the buildings there. I mean, it's a pretty impressive place.

[00:04:39] [SPEAKER_00] So they, they develop all kinds of amazing technologies, things that the average person has probably read about or seen on TV related to, like, space exploration and robotics and things like that. And, and, and they had spent years developing an AI platform, you know, before it was like the thing to do and had deployed it in different sectors. So we were able to secure a worldwide exclusive license for drug development applications.

[00:05:09] [SPEAKER_00] So right out of the gates, we had blue chip technology. This, an APL, they, you know, they have like 5,000 engineers and scientists there. This is a big place. And this tech won innovation of the year at APL. So, you know, so we had this tremendous technology. You know, it was originally designed for different applications.

[00:05:34] [SPEAKER_00] And as we know, biomedical data is very different than, you know, autonomous vehicle data or, you know, energy data. So we went through some R&D for a while and then ended up with the platform that we needed, you know, did some more work with them and then added our own secret sauce. And here we are today with, you know, very unique and powerful ensemble of technology and capabilities.

[00:06:06] [SPEAKER_00] And, you know, Craig, everybody says, oh, you know, we have the best technology. And, of course, I'll say that, too. But now we're getting, you know, feedback. We're hearing from, you know, Big Pharma, for example, that knows what's out there. They have their own teams. They've done deals. And they're, you know, they're telling us that nobody can do what you guys can do, which is fantastic. So, obviously, we want to, you know, next step is, well, let's do, let's work together then, if that's the case.

[00:06:35] [SPEAKER_00] So, you know, so that's where we are. Now we're, we kind of followed the market forces. And where those forces pushed us was toward drug target discovery, right? That's the very first step in drug discovery. What is the right target, okay? And then the last step, late stage clinical trials, phase three.

[00:07:01] [SPEAKER_00] So that's where we have really, you know, had our experiences and made a difference. But I, you know, I just want to be clear, though. Our technology is completely agnostic. It doesn't, we don't care what stage of development or what disease, you know. The key is the data.

[00:07:20] [SPEAKER_00] And, you know, we know that we can make discoveries and uncover insights and make predictions that are, you know, very few, if anybody, can do. So, so that's, that's the story. That's how we got where we are today. Okay.

[00:07:37] [SPEAKER_01] And when you talk about the technology of Bullfrog's technology, are you talking about on both ends, on drug discovery and the phase three trials? Are you, is it a platform or are they tools that you're making available to either big pharma or small pharma drugs?

[00:08:06] [SPEAKER_01] And yeah, just talk about the two ends. Is it a platform? Are they tools? What, what is the product?

[00:08:13] [SPEAKER_00] So right now it's a platform, but the, you know, customer, client, they have to send their data to us. Let me zoom out a little bit for you. I want to talk about the whole platform, all the solutions. So we are now in a situation where we can address end to end, right? Everything that needs to be done, right?

[00:08:40] [SPEAKER_00] Using AI in for drug discovery and development. And, you know, we've launched, we launched a new solution yesterday, actually. We put out, you know, press release on that. And then we had launched one several months ago, but I'll just walk you through them quickly. And then you can ask some questions. But so the first thing we realized is, you know, the companies that we were working with and we just know in general, there's a lot of messy data out there.

[00:09:09] [SPEAKER_00] There's a lot of data, but it's a mess. And with the like rapid adoption of AI, right? These, and these, you know, with chat GPT and Claude and all these companies, you know, we were like, wow, you know, that's great. But nobody really has data that's ready to be ingested by an AI, right? And so you're not going to extract the value that you should be out of the AI.

[00:09:38] [SPEAKER_00] And so through one of our experiences working with this phase three stage pancreatic cancer company. So we went through this exercise with them, you know, they presented their data to us and we were just like, wow, what is this? You had tens of thousands of pages of PDFs with handwritten notes. I mean, like it was a mess.

[00:10:01] [SPEAKER_00] So we developed an algorithm and pipe algorithms and pipelines to accurately convert all of that data, all the handwritten notes and everything into clean structured data.

[00:10:15] [SPEAKER_00] And going through that exercise, we realized that if we can do this with a gigantic mess of data like this, we can literally do this for anybody else, probably in any industry, to be honest, because other industries data is far simpler than biomedical data. And we said, all right, we're going to call this BF prep for data prep. So we launched this several months ago.

[00:10:40] [SPEAKER_00] And as we've done more homework, we realized even now more than 60% of companies out there don't have AI ready data. So, but they're, you know, integrating these AI solutions into their IT systems. But, you know, let's just see what happens. But they got to have, they got to prepare their data. So we can do that. Okay, that's a very powerful AI tool. Then the next step is the analytics. And that's something we call BF LEAP.

[00:11:10] [SPEAKER_00] And that's really what I've been telling you, you know, for the past 10, 15 minutes here. That's where we make the discoveries. But sometimes you'll make a number of discoveries. Like, let's say you discover a number of drug targets or a number of biomarkers, right? Or some other prediction. That's where we just launched BF Arenas, okay?

[00:11:32] [SPEAKER_00] And that says, the purpose of that is, look at all these options and help me figure out what is the best one for us to move forward with. And, you know, that's a really critical.

[00:11:45] [SPEAKER_01] Just in terms of molecule. Is that what?

[00:11:49] [SPEAKER_00] It could be the molecule. It doesn't really matter what it is. Think about any time you've done some quantitative analysis. Let's say you've done a risk analysis and you have all these risks. You arbitrarily assign weights. Like, okay, this risk is worth 20%. That's worth 10. This is worth 5. There's a lot of subjectivity in this, right? And it's like, okay, how severe is the risk? What's the likelihood? You know, you may have some data.

[00:12:18] [SPEAKER_00] But with Arenas, you take that subjectivity out of it, right? By using large language models and other tools and incorporating massive amounts of information so that you can, you know, truly make the right decision. You know, a lot of these AI tools are black boxes that spit out answers, right? How do you trust it? How do you know, oh, should I do this or not, right?

[00:12:44] [SPEAKER_00] So, we are adding that layer on top, which we think is going to be, you know, a fantastic piece of the entire continuum, right, of solutions that we're providing. So, you know, with all of this, what this speaks to is we are in the AI innovator category, Craig.

[00:13:05] [SPEAKER_00] We're not like these biotechs that have added AI and, you know, people, companies out there that are using the open source tools and putting their own wrapper around it. We are innovating, right? We're pushing the industry forward. We, the things that we are doing, you know, we're pretty sure in almost all cases nobody else can do them. But we're still this tiny company. We're under the radar, right? So, that's sort of the tug of war that's going on.

[00:13:35] [SPEAKER_00] But we're driving toward getting deals done now with pharma. Our goal, you know, we want to be a revenue-generating company. We're not a biotech. We're not developing drugs for the long term. We leave that to our customers.

[00:13:48] [SPEAKER_01] Right. And let me just ask about the middle part, big MBF leap. I thought that was specifically for precision oncology.

[00:14:04] [SPEAKER_00] That was one of the applications. So, I can tell you a little bit about what is BF leap. So, the core of it, the heart of it is something called scalable graph analytics, okay? That's what comes from the Johns Hopkins Applied Physics Lab. We've now, over time, added causal AI capabilities. And let me explain what that is. I think your audience will be really interested. So, typically with AI, you're looking for patterns and relationships that exist.

[00:14:33] [SPEAKER_00] That's how deep predictions are made. So, we've added something now called causal AI. We've developed this ourselves where we can generate 6 million models in 30 minutes. And with causal AI, you can not only identify the relationships and patterns, but you can determine the magnitude and direction of the relationship. So, why is that important for our industry? Biological systems are very complex.

[00:15:03] [SPEAKER_00] So, if you want to try to determine, say, hey, what gene is driving a disease, driving these disease pathways and ultimately leading to the disease, right? And having that capability is an advantage. And we've used that. And that's helped us do some of the things we've done. For example, our partnership with the Lieber Institute for Brain Development. We had access to all of their data.

[00:15:32] [SPEAKER_00] They have many thousands of post-mortem brains for neuropsychiatric patients. And the world of neuropsychiatric, you know, well, not only diagnosing, but developing drugs. It's a very challenging space. A lot of subjectivity there. It's about behavior and symptoms, right? Well, through our analysis of this incredible data, we had exclusive access to it.

[00:16:00] [SPEAKER_00] We discovered the biological drivers of those diseases. So, now you add that to behavior symptoms, you have a precision neuropsych approach to these. So, we believe we've discovered the genes that drive depression, bipolar, and schizophrenia. And that is, so those discoveries, now we've gone to market. Now we're talking to big pharma. Hey, let's work together here.

[00:16:28] [SPEAKER_00] We've discovered the drug targets. And like I said in the beginning, I didn't really say it, but getting, you know, the wrong drug target is the reason a very significant number of drugs fail. Okay? Because with these causal networks, we can show, it's like, it's like the root cause of the disease. We can actually illustrate it. Hey, this gene has no parents. Everything is downstream from it.

[00:16:58] [SPEAKER_00] This is the root cause, right? It's not the gene way at the end that then, you know, you know, these genes turning on and off causes that. It's this root cause. A lot of times when drug targets are discovered, it's downstream somewhere. It's not the root cause, which is why the drug fails, you know, somewhere in the middle of development.

[00:17:23] [SPEAKER_00] So being able to pick that right target from the beginning, it's going to save the industry lots of time and lots of money, right? Right? Because not only could you use it to discover new targets, but hey, if you're a company that's already, you think you've discovered the right target? Well, we can tell you if you did. Right? Right. And I, you know, so I think this is something the whole industry is going to eventually really appreciate and investors too.

[00:17:50] [SPEAKER_00] I mean, you know how high risk biotech investing is, right? Yeah, sure.

[00:17:55] [SPEAKER_01] Just a question about that. But diseases don't have necessarily a root genetic starting point. Not necessarily. Yeah, a matrix of combinations of different things. So how can, yeah, maybe take one example. One example. So that's why.

[00:18:24] [SPEAKER_01] I'll tell you, in taking one example, what caught my attention when I heard about you guys is BFLEAP has identified patient subgroups with a nearly threefold increase in mean overall survival of pancreatic cancer. And pancreatic cancer is like one of those boogie men that people might be terrified of.

[00:18:54] [SPEAKER_01] Yeah. Because, yeah. So could you use that as an example? Or is that a little different than?

[00:19:02] [SPEAKER_00] Well, it's like, I think we're talking about maybe two different things here. But let me first say, our platform can ingest any type of data. Yeah. Right. You know, we were just talking about gene expression or genomics data. But it's true multimodal data, imaging data, demographic data, environmental data, lab data, all the omics data you can imagine. So you're right, Craig.

[00:19:30] [SPEAKER_00] In a lot of cases, it's much more complicated than just a gene. Right. But that's why we're able to bring all that information in. And that's the precision medicine approach. Because then you can really identify the true genetic and non-genetic profile of a patient that will maybe, in this case, best respond to a pancreatic cancer drug. So, yeah, in that example, that's when we had this mountain of data.

[00:19:59] [SPEAKER_00] And we were able to identify a patient subgroup and biomarkers that increased the overall survival from, like, two months to six months, which is, you know, for pancreatic cancer, that's a big deal. But it also speaks to the fact that, hey, the answer is in there. Like, there is a, you know, that's a precision medicine approach. If you're a patient with this particular profile, this drug will benefit you, right?

[00:20:27] [SPEAKER_00] So the whole industry, especially with AI, it's almost being forced in that direction. I mean, we've been hearing about precision medicine for 20 plus years. But there's always that tension because pharma wants wide nets for the patient population, right? They want millions of patients.

[00:20:48] [SPEAKER_02] Yeah.

[00:20:49] [SPEAKER_00] Well, if you want drugs that actually work, you're not going to be able to do that, right? You've got to take a precision approach. So I think with mass adoption of AI, it's going to turbo boost the precision medicine initiative that's out there.

[00:21:04] [SPEAKER_01] Yeah. And also, you're talking about big pharma. But what's happening are there are smaller companies, labs that are developing molecules or drugs. And they bring them all the way up through phase one, phase two, and trial, and then sell them to big pharma. Is that affecting that strata of the market?

[00:21:32] [SPEAKER_01] Those companies, it's not big pharma, but they're focused on precision drug discovery.

[00:21:40] [SPEAKER_00] Yeah, it is. I think what I'm seeing is more and more biotechs now are trying to incorporate AI from the beginning, right? I mean, they realize it's only going to improve their chances for success, right? The question is by how much.

[00:21:58] [SPEAKER_00] But still, in this incredibly high-risk business of biotech, where your chances from start to finish are like low single-digit percentages, any improvement is a big deal. So, yeah, we are seeing that. That's becoming – and then, like, if you look at other countries, China, for example, I mean, they've bolted AI into their entire biotech industry.

[00:22:25] [SPEAKER_00] I mean, that's – it's like having electricity at this point, right? I think U.S. has been slower, probably, on the biotech end to adopt that. Pharma has understood the value for the past 10 years. That's why they've been doing all these deals, and now they're starting to make acquisitions in AI because they want to own their own models. They don't want to partner on them. They want to own them.

[00:22:52] [SPEAKER_00] So they're making acquisitions or they're doing what Eli Lilly did and said, hey, we're going to partner with big tech and build supercomputing capabilities and, you know, just develop our own models and so forth. So anyway, so that's sort of the – you know, some of the changes that are occurring. But it's all in the right direction for us. You know, it's all good.

[00:23:15] [SPEAKER_01] Yeah. And so you guys, you're saying you're disease or target agnostic.

[00:23:21] [SPEAKER_00] Yeah. We are disease and stage agnostic.

[00:23:25] [SPEAKER_01] Yeah. So how does a company use – and let's talk about the three platforms. How do they – what's the progression? I mean, sort of give me a use case. Maybe that neuropsychiatric use case is very interesting.

[00:23:45] [SPEAKER_00] Happy to do it. I mean, so the first thing is we talk to a company. We basically audit their data. Okay. What kind of condition is your data in? And we have BF prep. We can prepare that data. So this is a great – it's a great door opener for us. Even if they don't want to work with us beyond that, we know they got problems with their data. Right? Right. So that's the first step.

[00:24:11] [SPEAKER_00] Then, you know, then we can basically prepare, you know, clean and structure their data. So we can do what would take a team months. We can do it sometimes in hours. Right? But certainly in days. We can prepare that data. Then the next step is, all right, what kinds of – you know, what questions do you want answers to in this data? What can we help you find? And that's where BF Leap and the analytics comes into play. Right?

[00:24:40] [SPEAKER_00] So out of that, we're able to build these networks. These networks show the relationships that exist in the data. Or you can, you know, identify disease pathways and, you know, things like that. Then, let's say a number of predictions are made. Right? And like an example I gave earlier was drug targets. Right? Right. And let's say there's a series of genes that are – we think they're the drivers of a disease. Okay. Okay.

[00:25:08] [SPEAKER_00] Which one should we go after out of those 10 driver genes? And that's where we layer on BF Arenas. So BF Arenas effectively takes each of those, you know, selections and creates a competition between them, which is – that's really the secret sauce. That's the approach. Yeah. And brings in lots of other information.

[00:25:33] [SPEAKER_00] And then what you're presented with is a recommendation that you can have a lot more confidence in. Okay. BF Arenas is saying go for target one and then six and then nine, you know. So it's like a priority ranking. So anyway, and I think that's going to be critical in this world of AI. Right? Because you're going to – the AI is, you know, you want to – first of all, you have to trust what the AI is telling you.

[00:26:01] [SPEAKER_00] And that's where explainability is important. And our platform has always been explainable from the very beginning. It's not a black box. Right. But then even with the explainability, okay, help me make a decision. Anyway, I think we're uniquely positioned. You know, we're a small company but like super high on the innovative end of things.

[00:26:26] [SPEAKER_00] And so now we think we got like three really valuable pieces for the industry that are going to make a difference.

[00:26:34] [SPEAKER_01] Yeah. The – at the last stage there when you said then you have these different targets competing with each other, are you using evolutionary AI where you're coming – different models produce an output and then you take the best and combine those models?

[00:27:02] [SPEAKER_00] Yeah. Yeah. So we're looking at different models. And like I said, like with our causal AI capabilities, for example, we can generate millions of models, right, in a matter of minutes. But that's the beauty of it. Like you're bringing lots of information together, lots of models, lots of outputs. And then you're trying to find sort of where that common thread is to give you confidence in what selection to make.

[00:27:31] [SPEAKER_01] I mean, you said an interesting thing earlier that you were looking at – you were considering looking at failed drugs because clinical trials fail oftentimes for reasons that have nothing to do with the underlying drug. But the point of all this is to increase the likelihood that a clinical trial will succeed, right?

[00:28:00] [SPEAKER_01] I mean, ultimately –

[00:28:01] [SPEAKER_00] That's the goal. That is the goal. That's been the goal from the beginning to de-risk drug development, improve the odds of success, shrink the timelines and investment. That's always been the goal. And like I said, we've had opportunities to get involved at the beginning, at the end. Hopefully in the future we'll get more involved in the middle stages.

[00:28:26] [SPEAKER_00] But that – you know – and I think that's the goal of AI in general, right? I mean, that's – it doesn't matter what industry you're in. It's to improve efficiencies, right?

[00:28:37] [SPEAKER_01] Yeah. So these three platforms, BF Prep, that makes the data, cleans the data, prepares the data, and then the causal analysis, that's BF Leap. And then the sort of decisions on what to focus on, that's BF Arenas. Can you talk about Bullfrog Data Networks?

[00:29:04] [SPEAKER_01] From what I understand, that's sort of the – that ties them all together.

[00:29:09] [SPEAKER_00] Yeah, really. It's – well, it's really the outputs of BF Leap. So BF Leap is the analytics engine, but what comes out are the networks, right? And that's Bullfrog Data Networks. I see. And that's where you can actually, you know, visualize these relationships, you know, extract information, you know, from whatever's, you know, being illustrated there. So that's what the networks piece is.

[00:29:38] [SPEAKER_01] Yeah. And again, on the underlying AI, can you talk about the different models? I mean, you said it's an ensemble of models. But can you talk about the different architectures underlying of the three platforms?

[00:30:01] [SPEAKER_00] Yeah. I mean, I can't talk too much about the architecture. That's kind of a – but I can just tell you scalable graph analytics is the core. And that's, you know, that's an approach that's been around for a long time. Yeah. But what we have is proprietary, it's protected by patents, some of it. But that's really the heart of the technology.

[00:30:26] [SPEAKER_00] These other pieces that we've attached, you know, that's our innovation. But, yeah, I want to be careful about giving away too much of our secret sauce here. But, you know, at a high level, that's what we use to do what we do.

[00:30:44] [SPEAKER_01] Yeah. And do you have any case studies you can talk about?

[00:30:50] [SPEAKER_00] Yeah, we do. I mean, we – well, obviously, one is this phase three pancreatic cancer work we did. And we're actually – we'll put out a case study on that pretty soon so that everybody can read this whole story from start to finish. The other one is, you know, the work we did with Lieber where we discovered those driver genes, these drug targets.

[00:31:14] [SPEAKER_00] I mean, Lieber has been around for like 15 years and they spent many years trying to figure those kinds of things out. And we did it in a matter of months.

[00:31:23] [SPEAKER_01] Yeah. We'll talk about that sort of start to finish.

[00:31:28] [SPEAKER_00] Yeah. Yeah. Yeah. So, you know, we – so Lieber has, I think, over 5,000 brains, okay, actual human brains. And the vast majority of them are for neuropsychiatric disease. They also have neurodegenerative, you know, and other related diseases. And, you know, we had talked to them about our technology. They are associated with Johns Hopkins Medicine.

[00:31:58] [SPEAKER_00] So they were kind of familiar with APL, the Applied Physics Lab. And they were like, okay, if that's your technology, like, we want to work with you because we know how good those guys are. And so – and we have – we had some other connections with them. Our senior director for CNS had worked there for many years in the past. And so, you know, there was a lot of trust there.

[00:32:26] [SPEAKER_00] Because for Lieber, look, they can work with anybody. They had to feel like they could trust whoever was going to be their partner and who, you know, was going to have access to this precious data. I mean, they spent a lot of money, you know, probably over $100 million to, you know, in many years collecting these brains and, you know, extracting tissue samples and then generating the data. I mean, a lot went into this.

[00:32:56] [SPEAKER_00] So – so anyway, so that's how it all started. And then, you know, we worked collaboratively with them. It wasn't like, all right, hey, Bullfrog, take the data and let us know what you find. We met with them on a weekly basis. I mean, they have – they have tremendous expertise there. Their CEO, by the way, Danny Weinberger, you know, he's a rock star. He's like Nobel Prize winning type guys. He's a phenomenal scientist, physician, and leader.

[00:33:27] [SPEAKER_00] And, you know, he was actively participating in these meetings with us. And as we – you know, each week went by and we're showing them what we're doing and you saw the eyes wide open like, you know, wow, that's pretty impressive. And then – anyway, so we got through that in a matter of months. And, you know, then the goal was, okay, these are important discoveries, okay? We know pharma is very interested in neuropsych, right?

[00:33:56] [SPEAKER_00] Also neurodegenerative, but that's for another day. They're interested in neuropsych. We know there's been a lot of failures in that space. And – but so let's, you know, let's start approaching pharma. Let's see if we can get one of these drug discovery AI deals done or target discovery deals done. And as you can imagine, it's a long sales cycle working with pharma. Sure. I mean, easily over a year.

[00:34:25] [SPEAKER_00] So, you know, we've been doing that, though. We've been doing the outreach. And then along the way, trying to prove ourselves in other ways, right? Because, you know, when you're this small company that no one's heard of, you know, even pharma will look at you like, you know, who are you guys? So, you know, we did some things along the way, like the phase three pancreatic. We had some partnerships. And those were forms of validation for us, right?

[00:34:54] [SPEAKER_00] They said people were saying, hey, okay, if you work with those guys, then, you know, you must have something. So, anyway, so fast forward to today. We've been in a number of these conversations. And, you know, we're zeroing in now on some real opportunities. And, you know, the way it works with pharma, you know, for those first opportunities, it's typically like a pilot. Like they have to kick the tires on Bullfrog, right?

[00:35:20] [SPEAKER_00] And if that goes well, then you get into the, you know, mega million dollar deals that we keep reading about. I think a lot of people don't realize that. They just see these big headlines and they, oh, wow, how'd they go from start to that so quickly? Well, you don't know what happened in between, right? In between is the pain and suffering and having to do these small projects, right, to prove yourselves.

[00:35:48] [SPEAKER_00] But so that, you know, that's how we got where we are today. We continue to have the relationship with Libra. And we look forward to, you know, continuing that into the future. Right.

[00:36:00] [SPEAKER_01] Right. But you identified a gene, the root cause of one of the...

[00:36:07] [SPEAKER_00] Yeah. In the analysis, we discovered the driver genes, we call them. Right. So, you know, these are the genes that appear to be drivers of the disease. Right.

[00:36:21] [SPEAKER_01] But which disease specifically are you talking about?

[00:36:24] [SPEAKER_00] We found it for major depressive disorder, bipolar, and schizophrenia. Okay.

[00:36:32] [SPEAKER_01] You take that to big pharma and you're looking for someone to take this up and develop a drug to address that. Exactly.

[00:36:44] [SPEAKER_00] Exactly. Exactly. What we're saying is, hey, guys, we've discovered very valuable targets here, novel targets from a one-of-a-kind data set that does not exist anywhere in the world. Okay? It's not public data. Nobody has access, you know, had access to that data the way we did. So, yeah, so that's the pitch. We're not going to identify the drug, do drug screening, or design a drug. That's pharma's job.

[00:37:14] [SPEAKER_00] We've done our job. That said, our plan is, you know, once we get a deal or a couple of these deals done, obviously there's three disorders, so we can do at least three deals, presumably, probably more. Once we get cash flowing from those, then maybe we'll advance these targets one or two stages ourselves.

[00:37:37] [SPEAKER_00] We don't want to become a drug development company, but we can take that target, we can then validate it in the lab, and then maybe we do a drug screening. Then we do the deal with pharma, right? Right. Because that's not a super long timeline there. It's a few years, right, and it's not a massive investment, but it de-risks the asset and makes it much more valuable so we can do bigger deals. So we're trying to do things one step at a time.

[00:38:06] [SPEAKER_00] It's like we've made these discoveries.

[00:38:09] [SPEAKER_01] Well, I was going to say, as I said before, there's this strata below big pharma that is developing molecules and taking them through, you know, phase one or two clinical trials and then selling them to big pharma because phase three is much more complicated and expensive. Are there any of that level of companies that you're interested in?

[00:38:38] [SPEAKER_00] That's not our focus. I mean, we're happy to talk to them. Do they have resources, right, to do a deal with us? And that's where I think their investors have to get a lot smarter and say, hey, we got to add in an AI budget, an AI partnership budget to these investments that we're making, right, so that they can take advantage of some of these opportunities.

[00:39:03] [SPEAKER_00] But, you know, I think I'd say like a year or two ago, we were talking a lot of biotechs. But what we realized is, you know, they didn't really have the money. They didn't really understand the value of AI. They didn't know what they were really looking for. You know, that's the thing. They kind of had tunnel vision where big pharma is the opposite across the board, right? So we said let's focus, you know, we're a small company. We have limited resource.

[00:39:32] [SPEAKER_00] Let's focus on big pharma because that's the customer we want. So we're not opposed to dealing with biotechs. But that's, you know, we just kind of made that strategic decision a while back. Yeah.

[00:39:49] [SPEAKER_01] So where are you now? I mean, you sort of brought us up to date. We know about the three principal platforms. And you've had, you're working on this neuropsychiatric target or targets. Where do you as a company go from here? And where do you see the industry going from here?

[00:40:18] [SPEAKER_01] As more and more of these platforms come out that improve chances of trial success.

[00:40:29] [SPEAKER_00] Yeah. I mean, so, I mean, we're in discussions with a number of big pharmas. So over the course of 2026, I expect us to get a number of these deals done. And, you know, once we get one done, others are going to come. Right. And the sales cycle will get compressed. It's not going to be 12 to 18 months anymore. It might be six months or maybe even less. So we're going to get those opportunities.

[00:40:59] [SPEAKER_00] You know, we're going to see more and more adoption of AI. But there's like a one key thing. This is the key. You know, more than 90% of these deals that get done, they're not hitting their milestones. And that's one thing we're super careful about. When we go into a proposal, if we don't, if we're not very sure that we can deliver, we're not proposing it.

[00:41:28] [SPEAKER_00] And I think that over promising thing sort of cut across all industries. It's kind of, I mean, IBM Watson. Right. That's where it all started. So, but that's how you separate yourself from the pack. Like you said in the beginning, you're like, hey, have you heard of this company and that company? So, yeah, there's a bunch of AI companies working in life sciences, broadly speaking. Right. So how many of them have real technology?

[00:41:58] [SPEAKER_00] Well, I'll say most of them don't. Okay. Most of them are using the tools out there and putting a wrapper. The next, you know, the way you move up the pyramid is then you get a deal done of some sort with a name brand. Right. A reputable company. Then to get to the top of the pyramid, which is what we're aiming to do, is you actually deliver what you promised.

[00:42:23] [SPEAKER_00] And that's where, Craig, you're going to see a very small number of companies because that means they got real technology and they delivered on their promises. So, they're going to be the winners. So, that's where we're going to separate the players from the pretenders. And I think you're going to see, just broadly speaking, this sort of fallout from the whole AI world.

[00:42:48] [SPEAKER_00] You're already seeing story, reading stories about like these big AI companies. They add some new capability. And then all of a sudden, it makes like 100, you know, med tech AI startups obsolete. Like they're going to, they're just done. So, you're going to see, I think you're going to continue to see that. So, that's why being in the AI innovator category for us is critical.

[00:43:16] [SPEAKER_00] We know we can still do things that the big guys cannot do, but we can't sit still. Like we have to keep innovating, keep, you know, cranking. So, you know, my hope is though in the end, we're not just left with just two or three, you know, players out there that dominate. It kind of, it sort of looks like that's the direction things are going and we're going to push back on that. That's for sure.

[00:43:46] [SPEAKER_00] But, you know, anyway, I think the future is exciting, but it's going to come down to solid use cases, right?

[00:43:53] [SPEAKER_01] So, let me ask, when you say deliver, you know, we want to make sure that we deliver. You're talking about, ultimately, about delivering a target that then, when a drug is developed to address that target, that the outcome is successful. I mean, that's what you mean, that you're picking the right target, not just a target. That's right.

[00:44:22] [SPEAKER_00] But it could be, you know, for other applications in life sciences and other, you know, other stages. You know, you put together a proposal. Here are the deliverables to the company. Make sure you deliver those, right? You don't want to over-promise and under-deliver because that's where doubt creeps in the minds of the market, right?

[00:44:46] [SPEAKER_00] And then nobody starts believing anything, and then they're suspicious of AI. But, you know, the more use cases we get, right? I mean, look, AI can't do everything. I mean, I'm sure you've played around with, you know, the name brands that we hear about, and it's kind of surprising some of the things they can't do. But the key is use it selectively, right? You got to use it.

[00:45:12] [SPEAKER_00] They still hallucinate, like in document review, at about a 10% clip. I mean, that's significant. That means you got to go back in there and proofread, right? You can't just, you know, have it, you know, summarize a document or, you know, review a document and just trust it. You might be making a catastrophic mistake in there. But anyway, I think, you know, there are good things coming out of it.

[00:45:42] [SPEAKER_00] You got to sort of pierce through the hype, though, and be selective about how you want to use it. We don't believe that, you know, we don't have this thinking, philosophy that AI is replacing people. We think it's a tool to help scientists and physicians be better at their job, right? It's not ready to replace them anytime soon.

[00:46:09] [SPEAKER_01] Yeah, and the platforms, particularly BF Prep, you were saying that it's really industry agnostic. Eric, have you considered spinning that out as a general product? There is a massive market right now for people.

[00:46:30] [SPEAKER_00] Yeah, we have advisors from big tech that, you know, we're talking to. But, you know, Craig, at the end of the day, we're this very lean organization, and everybody always says focus, right? So let us, you know, we're going to get some wins with pharma, get things really flowing for us. And then I think finding strategic partners that can apply our tools across other industries, that's on the table.

[00:46:58] [SPEAKER_00] You know, it's because they truly can be used by these other industries, and we know it's a problem that they have for sure. So, you know, let's just see what happens. But right now, I got to keep myself and my team laser focused on pharma. But it's a nice to have, let's just put it that way.

[00:47:21] [SPEAKER_01] Yeah. What am I missing? Is there anything I'm not asking about that I should be asking about?

[00:47:26] [SPEAKER_00] No, I mean, I think you covered all the important points here.

[00:47:31] [SPEAKER_01] Well, I know what I'm going to ask you. You mentioned before that a lot of big pharma wants to own the models. It doesn't want to work with an outside vendor. Are you concerned about competition from big pharma who may be developing?

[00:47:51] [SPEAKER_00] Not right now. Well, so, you know, there's a movement toward that, wanting to own their own models. But I'm not, you know, and then, you know, look, for the past several years, big pharma has been building up, building their own AI teams. But then I'm hearing that they weren't really getting what they wanted out of them. And so, you know, and then we started seeing, you know, at least the one acquisition we saw a couple months ago by AstraZeneca.

[00:48:18] [SPEAKER_00] And then we see Lilly partnering with NVIDIA. So that sort of the model is changing a little bit. But the direction is owning your own models. That appears to be the direction things are headed. Not really worried about competition only because we know we have good technology. They don't have what we have. They can't possibly have what we have, right? Yeah.

[00:48:46] [SPEAKER_01] So I know this is kind of a sort of question for a public company, but it seems that you would be an acquisition target by big pharma.

[00:48:59] [SPEAKER_00] Look, yeah. And that's a fair point, fair question. You know, you become an invaluable partner to them. I mean, look, what we want to do is get in the door with a pharma and then we want to run wild, right? And they've already said, hey, get in the door. And then we want you to do A, B, C, D. Like we need help in a lot of different areas. At some point, they're going to be like, well, it makes more sense for us to buy these guys, right?

[00:49:27] [SPEAKER_00] So, you know, we'll cross that bridge when we get there. And if the price is right, then we'll take it seriously. But right now we're like, we feel like we're a very undervalued company and there's so much opportunity for us. And we want to grab that opportunity. And then we'll see, you know, about that.