theCUBE + NYSE Wired: NYSE Rx | Viswa Colluru, Enveda

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More from theCUBE + NYSE Wired: NYSE Rx

Viswa Colluru

CEO & Founder

Enveda

play_circle_outline Chemistry-First vs Biology-First: Foundation Models, Life’s Chemical Code Database, and LLMs Interpreting Mass Spectrometry

play_circle_outline Platform results: 17 discovery candidates, 6 IND-enabling, 3 in clinical trials

play_circle_outline Vertical integration: running in-house clinical trials, avoiding CROs

play_circle_outline Founder’s Personal Loss Spurs $311M Series E to Accelerate Clinical Cures

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Viswa Colluru, Enveda

Viswa Colluru

CEO & Founder Enveda

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Gemma Allen

>> (INTRO)Welcome back to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen, co-host of NYSE Wired: NYSE Rx, where we talk to some of the builders and breakers defining the next wave of the biotech industry. Now we're going to have a conversation about what it would mean if the next blockbuster drug isn't sitting in a pharmaceutical company's database, but instead sitting in a plant, a fungus, a microbe, or somewhere else in nature that we simply haven't figured out how to read yet. That's the bet Viswa Colluru is making. He founded Enveda Biosciences on a pretty radical idea. That nature is a largely untapped pharmacy and that AI can finally give scientists the tool to search it. Now he's moving the question past whether AI can help discover drugs, to whether AI can actually help us discover medicines that work. Viswa, welcome to NYSE Wired.

Viswa Colluru

>> Gemma, thank you so much for having me. It's a pleasure to be here.

Gemma Allen

>> So I hope I did a good enough job there in setting out the premise of Enveda in that intro, but it's certainly a fascinating concept. This idea that there are so many medicines living in our natural habitat, some of which I think have been proven already, like metformin is one I believe as an example that I myself am familiar with. But maybe you can just help by giving us the backstory here, what made you wake up one day and say, you know what, I think we can look to nature for solutions for drug discovery and medical treatment.

Viswa Colluru

>> Look, I couldn't have done the intro better myself. And you're absolutely right that some of the most common, well-understood and well-known medicines in the world today that touch the lives of billions of people are actually a product of evolution rather than human ingenuity. You mentioned metformin, which I think about half a billion diabetics and pre-diabetics take worldwide. But it's true for everything from aspirin all the way to insulin and GLP-1. And all of those actually started not with an observation around a biological mechanism, which is how we tend to think about the industry and has certainly tended to occupy our activities over the last 30 years, but really around an idea that evolution has created its own pharmacy with molecules that actually have been pre-optimized for activity, for getting to the right tissue. And they've been created out of a very large set of possibilities. And this isn't a radical idea. Actually, it's where the industry started. The industry had medicines before we had biology labs. It's because our ancestors walked the Silk Road, drank the milk of the poppy, and realized that their feet don't hurt and they can sleep better. So yes, to answer your question, we think that the massive 4 billion year long, 10 to the power of 60 parameter optimization that evolution has run is probably the best place to look. Not only is it intuitive, but it's also validated and it's untapped. Only a few hundred thousand compounds from the living world have ever been identified. And we're setting out to find the millions and potentially billions of other ones. Wow.

Gemma Allen

>> So clearly something huge has happened in this industry and in society broadly since you founded this company in 2019, and that is AI, right? A word or two letters we say probably every 30 seconds here on this show, sadly. But help me understand the world of 2019 for Enveda and the world now, because in 2019 a lot of folks weren't really familiar with ChatGPT or LLMs or generative AI or any of the talk that we now have in our daily vocabulary. You perhaps, though, were, right? You kind of set out with this premise, or maybe help me understand that and help me understand what AI has actually done here. what sort of proof is there so far in actually helping humans discover drugs from nature?

Viswa Colluru

>> That's a great question, Gemma, and I think it is an important lesson for us and maybe for potentially other entrepreneurs that you should always keep your eye on the goal. And for us, those were medicines. And we were agnostic initially to what the technological stack would be. All we really wanted to do was to make a chemistry-first company rather than a biology-first company. And look to life's chemical code rather than life's genetic code. And for the first 2 and a half years, we used a suite of technologies in the digital world, including things like knowledge graphs or machine learning on those, until we actually realized in about 2021 that we can use what we now call generative AI or attention-based large language model type models. And the realization came when we started looking deeply into a type of data called mass spectrometry, which is, as the name suggests, the measurement of mass. But what a mass spectrometer does, it measures the mass of the molecule and the mass of all of the fragments that a molecule produces when it's accelerated through neutral gas and broken down. In other words, it's a fingerprint generation machine for chemicals. And this fingerprint is made of masses. And this fingerprint has historically been unreadable by humans, except a tiny, tiny proportion of them that have spent their whole life on that particular analytical technique. And we realized that this was a great match for how LLMs read and understand language the same way they can understand how these little fragments of masses are arranged next to each other. So that was the bet we took in 2021, 2, 3 years before the ChatGPT moment, that attention-based learning and generative AI could be a huge unlock in our quest to build a sequencer for life's chemistry that tells you what a molecule is and what it might do. So that was our foray into machine learning and AI and using it to build the world's largest foundation models for life's chemical code.

Gemma Allen

>> Wow.

Viswa Colluru

>> Now coming to the third part of your question, which is what has AI done, I think in general, AI is a little bit like electricity. And our entire process has been getting electrified as an industry over the last maybe nearly a decade in different ways using different machine learning architectures. And so it's really hard to point to, say, a specific stunning discovery the way that you would, say, a scientific theorem or proof, as opposed to a drug, which is very akin to assembling, say, something like a rocket. It takes a long time. There are a lot of steps. But specifically in the case of Enveda, we wouldn't be able to do what we're doing without AI. And in the last 5 years since our Series A, when we started applying this technology in 2021, we've delivered 17 candidates past the discovery phase, 6 molecules through IND enabling. So this is the years-long process that the FDA requires every molecule to go through before they allow you to dose a healthy human volunteer. And have 3 assets in clinical studies. And we've done this setting, I think, records for industry productivity while ensuring that each one of them is novel. Wouldn't have been possible without our platform and our frameworks that are enabled by our technology. So we're excited about where the future may ultimately go, and we're seeing transformative impact already, and hopefully patients can soon.

Gemma Allen

>> So I want to focus on those 3 drugs that are entering or maybe already in clinical trial stage. But first, I want to ask you a question, and I hope you can maybe just answer for me quickly because it interests me. The 14 that did not make it, what was the kind of key finding there? and how quickly did you realize, you know what, these are non-contenders? how quickly do scientists and folks like you come to those conclusions?

Viswa Colluru

>> Oh, I'm glad you asked that. So the 17 molecules we discovered are not killed. They have tremendous potential. It's just that at every stage as a company, we ask ourselves, what is the most impactful use of our time and our effort and our capital? And we decided that 6 of those were worth the effort of having them ready to dose a human or dosing a human. But the other 11 still have tremendous potential. In fact, if you want the right denominator, we started on 19 projects and delivered on 17 that gave us a molecule with potential. And that's about an 89% success rate, which is nearly 7 to 8 times higher than the industry in that phase. So that tells you already how much impact it's having. And out of the 6 molecules we decided to put through the gauntlet of studies required by the FDA, all 6 have cleared, which is double the industry's average. Wow. And so it looks like so far the real equation that you must solve as a company in drug discovery is making sure that things that work in the lab also work in the clinic. And so far it looks like we're on a good track to bend the curve of that equation, which has historically been 10%.

Gemma Allen

>> Talk about that process, right? That we know is quite an onerous and long process for clinical trials moving to, I guess, performance or efficacy and then moving to actual FDA approval. It interests me that a lot of money, a lot of energy, and a lot of hope, I think, is being poured into the biotech space, which is fantastic to see. But the output in terms of drugs approved to market hasn't really met the moment as of yet. So help us understand what's entailed— what does this entail? What's involved for you to get these drugs to market? And do you think AI can also accelerate or at least condense some of this process?

Viswa Colluru

>> Yes, it's a really important question. And I think you're asking, AI can probably help accelerate the hypotheses we have early on. So create a potential medicine or a potential biological target that the industry can then chase. But what happens when all of those ultimately meet the reality of clinical trials? I think taking the first part of your question, I'd say the real reason you haven't seen, say, a deluge of drugs that have been discovered by AI is that, one, as I mentioned, it's really hard to pinpoint and answer the question, what exactly do you mean by a drug discovered by AI? Because it's used across the process now and accelerating everything. There are some exceptions to that, like, the discovery of a specific new chemical that has been made possible by AI, as in the case of Enveda and moving our molecules forward. The second reason, as you mentioned, is it takes a long time for a molecule to become a medicine. After the initial discovery work, which the industry averages about 3 to 5 years, you need to perform about a year or 2 years worth of studies to ensure that the molecule you put into humans is likely to be safe in the patient population, is more likely to cause benefit than harm, and can be consistently manufactured batch to batch. Those are the 3 things that the FDA requires for a molecule to begin clinical testing and ultimately meet the bar for clinical testing. And that takes the form of 3 essential stages or phases of clinical trials. You go into healthy volunteers that are at the lowest risk for a toxicity event or an off-target event, and you prove that your molecule actually can get into circulation, usually if it's an oral pill, and it's safe. In Phase 2 studies, you start going into human patients that have that disease at doses that were deemed safe in the Phase 1 to establish there are signs of biological activity, movement of efficacy endpoints. So is the disease getting better? And then you do more of that in Phase 3 and accumulate enough evidence such that the FDA feels comfortable with the balance of safety and efficacy. And this process can typically take somewhere between 7 and 12 years, depending on the precise disease or disease indication. And you're right that there is a lot of slack in this process where it's equal parts documentation, quality assurance, and good clinical and manufacturing practices as it is acquiring the right patients and making sure that they're going through the trial to give you the right endpoints. So at Enveda, we've taken the same philosophy in discovery, which is vertically integrated, rapid iterative loops of learning, and applied that to our own clinical operations. So Enveda does not use a contract CRO or a contract research organization to run the trials, as is the industry's best practice, Instead, we're learning how to assemble a clinical trial and execute it ourselves. And AI is a big part of that process. In the next few weeks, we'll be making some announcements on how we're massively cutting down time, risk, and ultimately cost of clinical trials across these various steps.

Gemma Allen

>> Wow. So let's move on to the economics of this, because I will say we talk to a lot of founders on this show in all sorts of spaces. Right. And I really admire the biotech founder because It isn't like the typical tech founder where you spin up an idea, you sell it, you have customers, you have money. It's also almost instant, right? It takes chops to build a business in this space and to really go the long haul, I think, on hoping for the economic investments to come home to roost. Talk to me about, you know, how you see that. And you guys have raised Series E. Help me kind of understand the commercial dynamics at play here. What does it take, I guess, Viswa, to really kind of stay true to these theses? And where do you continue to balance the books from the perspective of your own P&L and your own investments? We know trials are super expensive. How do you think about that?

Viswa Colluru

>> Yes, I think there's a lot baked into that. We just closed our $311 million Series E, and what that allows us to do is add to our already strong balance sheet and take multiple medicines beyond early-stage hypotheses in the lab, in computer simulations, cells or animals, and actually see if they work in multiple Phase 1, Phase 2, and even Phase 2b trials, which are pre-trials to Phase 3, in patient populations. And the real reason I think entrepreneurs like me do this is they have a personal calling to be able to make medicines. In my own case, I got into biotech instead of, your classic tier 1 South Indian occupation of being a computer scientist and moving to Silicon Valley after losing my mother to chronic myeloid leukemia. And not only was there no medicine when she was diagnosed and her standard of care was something that we couldn't afford as a family, but as a life scientist, I became aware of the tremendous advancements in chronic myeloid leukemia care and treatment, where today it's largely a disease that can be managed chronically. And if my mother had been diagnosed just 10 years later, she would probably be with us here today. So for me, the urgency is to create medicines and accelerate cures. Every single day matters because patients are waiting. And the fact that AI and new ideas that AI can enable can turn back the clock on that is the real reason we do this. But how does the economics of all of this work is a great question. And I'll simply point to the fact that if you can make molecules that improve lives, and we call that molecules that make markets at Enveda, you can create incredibly enduring businesses. I think even at the end of this year, the two GLP-1 drugs are set to outpace all generative AI revenue in the world. And we're hoping that investors have continued to see that there's a sector beyond generative AI.

Gemma Allen

>> Wow. Well, as someone who has lost both my own parents to cancer, I want to say to you, Viswa, that I really commend you and I commend this industry and the work that you're doing. It certainly in my mind brings a lot of hope to some of the narratives that we hear about AI in our daily lives. So I certainly hope you're successful. Last question for you. What's ahead? you said you've obviously raised a lot of money. Does that give you some runway? You're at about 300 people. Will you continue to hire? And what does the map look like between now and I guess FDA approval for some of those drugs?

Viswa Colluru

>> That's a great question. In our view, I think answering the economics question more directly, we've turned the early stage process of drug discovery from 4 years and about $10 to $15 million to about a year and 1 million using our technology, our approach, and our operational capabilities that are vertically integrated. So it allows us to take the money we've raised very, very far, not just in the clinical trials that I just told you about, where we'll have over a dozen different clinical readouts in the next couple of years, or our ability to generate these candidates at a tenth of the cost and 4 times the speed, but really investing in the core AI engine that allows us to find life's chemistry and make a completely new database of the most powerful sources of medicines historically and going into the future.

Gemma Allen

>> Wow. Well, it is wonderful to have you on NYSE Wired Rx and to listen to this story. you certainly have our support and our hope that you're successful along with your team in this mission. Thank you so much for joining us on the show, and hopefully come visit us in New York from Boulder. Someday soon.

Viswa Colluru

>> Very, very soon. Thank you for having me.

Gemma Allen

>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired's Rx. We talked to some of the builders and breakers and visionaries defining this next wave of biotech. Thanks for watching.

Viswa Colluru, Enveda

search

Gemma Allen

>> (INTRO)Welcome back to theCUBE Studio here at the New York Stock Exchange. I'm Gemma Allen, co-host of NYSE Wired: NYSE Rx, where we talk to some of the builders and breakers defining the next wave of the biotech industry. Now we're going to have a conversation about what it would mean if the next blockbuster drug isn't sitting in a pharmaceutical company's database, but instead sitting in a plant, a fungus, a microbe, or somewhere else in nature that we simply haven't figured out how to read yet. That's the bet Viswa Colluru is making. He founded Enveda Biosciences on a pretty radical idea. That nature is a largely untapped pharmacy and that AI can finally give scientists the tool to search it. Now he's moving the question past whether AI can help discover drugs, to whether AI can actually help us discover medicines that work. Viswa, welcome to NYSE Wired.

Viswa Colluru

>> Gemma, thank you so much for having me. It's a pleasure to be here.

Gemma Allen

>> So I hope I did a good enough job there in setting out the premise of Enveda in that intro, but it's certainly a fascinating concept. This idea that there are so many medicines living in our natural habitat, some of which I think have been proven already, like metformin is one I believe as an example that I myself am familiar with. But maybe you can just help by giving us the backstory here, what made you wake up one day and say, you know what, I think we can look to nature for solutions for drug discovery and medical treatment.

Viswa Colluru

>> Look, I couldn't have done the intro better myself. And you're absolutely right that some of the most common, well-understood and well-known medicines in the world today that touch the lives of billions of people are actually a product of evolution rather than human ingenuity. You mentioned metformin, which I think about half a billion diabetics and pre-diabetics take worldwide. But it's true for everything from aspirin all the way to insulin and GLP-1. And all of those actually started not with an observation around a biological mechanism, which is how we tend to think about the industry and has certainly tended to occupy our activities over the last 30 years, but really around an idea that evolution has created its own pharmacy with molecules that actually have been pre-optimized for activity, for getting to the right tissue. And they've been created out of a very large set of possibilities. And this isn't a radical idea. Actually, it's where the industry started. The industry had medicines before we had biology labs. It's because our ancestors walked the Silk Road, drank the milk of the poppy, and realized that their feet don't hurt and they can sleep better. So yes, to answer your question, we think that the massive 4 billion year long, 10 to the power of 60 parameter optimization that evolution has run is probably the best place to look. Not only is it intuitive, but it's also validated and it's untapped. Only a few hundred thousand compounds from the living world have ever been identified. And we're setting out to find the millions and potentially billions of other ones. Wow.

Gemma Allen

>> So clearly something huge has happened in this industry and in society broadly since you founded this company in 2019, and that is AI, right? A word or two letters we say probably every 30 seconds here on this show, sadly. But help me understand the world of 2019 for Enveda and the world now, because in 2019 a lot of folks weren't really familiar with ChatGPT or LLMs or generative AI or any of the talk that we now have in our daily vocabulary. You perhaps, though, were, right? You kind of set out with this premise, or maybe help me understand that and help me understand what AI has actually done here. what sort of proof is there so far in actually helping humans discover drugs from nature?

Viswa Colluru

>> That's a great question, Gemma, and I think it is an important lesson for us and maybe for potentially other entrepreneurs that you should always keep your eye on the goal. And for us, those were medicines. And we were agnostic initially to what the technological stack would be. All we really wanted to do was to make a chemistry-first company rather than a biology-first company. And look to life's chemical code rather than life's genetic code. And for the first 2 and a half years, we used a suite of technologies in the digital world, including things like knowledge graphs or machine learning on those, until we actually realized in about 2021 that we can use what we now call generative AI or attention-based large language model type models. And the realization came when we started looking deeply into a type of data called mass spectrometry, which is, as the name suggests, the measurement of mass. But what a mass spectrometer does, it measures the mass of the molecule and the mass of all of the fragments that a molecule produces when it's accelerated through neutral gas and broken down. In other words, it's a fingerprint generation machine for chemicals. And this fingerprint is made of masses. And this fingerprint has historically been unreadable by humans, except a tiny, tiny proportion of them that have spent their whole life on that particular analytical technique. And we realized that this was a great match for how LLMs read and understand language the same way they can understand how these little fragments of masses are arranged next to each other. So that was the bet we took in 2021, 2, 3 years before the ChatGPT moment, that attention-based learning and generative AI could be a huge unlock in our quest to build a sequencer for life's chemistry that tells you what a molecule is and what it might do. So that was our foray into machine learning and AI and using it to build the world's largest foundation models for life's chemical code.

Gemma Allen

>> Wow.

Viswa Colluru

>> Now coming to the third part of your question, which is what has AI done, I think in general, AI is a little bit like electricity. And our entire process has been getting electrified as an industry over the last maybe nearly a decade in different ways using different machine learning architectures. And so it's really hard to point to, say, a specific stunning discovery the way that you would, say, a scientific theorem or proof, as opposed to a drug, which is very akin to assembling, say, something like a rocket. It takes a long time. There are a lot of steps. But specifically in the case of Enveda, we wouldn't be able to do what we're doing without AI. And in the last 5 years since our Series A, when we started applying this technology in 2021, we've delivered 17 candidates past the discovery phase, 6 molecules through IND enabling. So this is the years-long process that the FDA requires every molecule to go through before they allow you to dose a healthy human volunteer. And have 3 assets in clinical studies. And we've done this setting, I think, records for industry productivity while ensuring that each one of them is novel. Wouldn't have been possible without our platform and our frameworks that are enabled by our technology. So we're excited about where the future may ultimately go, and we're seeing transformative impact already, and hopefully patients can soon.

Gemma Allen

>> So I want to focus on those 3 drugs that are entering or maybe already in clinical trial stage. But first, I want to ask you a question, and I hope you can maybe just answer for me quickly because it interests me. The 14 that did not make it, what was the kind of key finding there? and how quickly did you realize, you know what, these are non-contenders? how quickly do scientists and folks like you come to those conclusions?

Viswa Colluru

>> Oh, I'm glad you asked that. So the 17 molecules we discovered are not killed. They have tremendous potential. It's just that at every stage as a company, we ask ourselves, what is the most impactful use of our time and our effort and our capital? And we decided that 6 of those were worth the effort of having them ready to dose a human or dosing a human. But the other 11 still have tremendous potential. In fact, if you want the right denominator, we started on 19 projects and delivered on 17 that gave us a molecule with potential. And that's about an 89% success rate, which is nearly 7 to 8 times higher than the industry in that phase. So that tells you already how much impact it's having. And out of the 6 molecules we decided to put through the gauntlet of studies required by the FDA, all 6 have cleared, which is double the industry's average. Wow. And so it looks like so far the real equation that you must solve as a company in drug discovery is making sure that things that work in the lab also work in the clinic. And so far it looks like we're on a good track to bend the curve of that equation, which has historically been 10%.

Gemma Allen

>> Talk about that process, right? That we know is quite an onerous and long process for clinical trials moving to, I guess, performance or efficacy and then moving to actual FDA approval. It interests me that a lot of money, a lot of energy, and a lot of hope, I think, is being poured into the biotech space, which is fantastic to see. But the output in terms of drugs approved to market hasn't really met the moment as of yet. So help us understand what's entailed— what does this entail? What's involved for you to get these drugs to market? And do you think AI can also accelerate or at least condense some of this process?

Viswa Colluru

>> Yes, it's a really important question. And I think you're asking, AI can probably help accelerate the hypotheses we have early on. So create a potential medicine or a potential biological target that the industry can then chase. But what happens when all of those ultimately meet the reality of clinical trials? I think taking the first part of your question, I'd say the real reason you haven't seen, say, a deluge of drugs that have been discovered by AI is that, one, as I mentioned, it's really hard to pinpoint and answer the question, what exactly do you mean by a drug discovered by AI? Because it's used across the process now and accelerating everything. There are some exceptions to that, like, the discovery of a specific new chemical that has been made possible by AI, as in the case of Enveda and moving our molecules forward. The second reason, as you mentioned, is it takes a long time for a molecule to become a medicine. After the initial discovery work, which the industry averages about 3 to 5 years, you need to perform about a year or 2 years worth of studies to ensure that the molecule you put into humans is likely to be safe in the patient population, is more likely to cause benefit than harm, and can be consistently manufactured batch to batch. Those are the 3 things that the FDA requires for a molecule to begin clinical testing and ultimately meet the bar for clinical testing. And that takes the form of 3 essential stages or phases of clinical trials. You go into healthy volunteers that are at the lowest risk for a toxicity event or an off-target event, and you prove that your molecule actually can get into circulation, usually if it's an oral pill, and it's safe. In Phase 2 studies, you start going into human patients that have that disease at doses that were deemed safe in the Phase 1 to establish there are signs of biological activity, movement of efficacy endpoints. So is the disease getting better? And then you do more of that in Phase 3 and accumulate enough evidence such that the FDA feels comfortable with the balance of safety and efficacy. And this process can typically take somewhere between 7 and 12 years, depending on the precise disease or disease indication. And you're right that there is a lot of slack in this process where it's equal parts documentation, quality assurance, and good clinical and manufacturing practices as it is acquiring the right patients and making sure that they're going through the trial to give you the right endpoints. So at Enveda, we've taken the same philosophy in discovery, which is vertically integrated, rapid iterative loops of learning, and applied that to our own clinical operations. So Enveda does not use a contract CRO or a contract research organization to run the trials, as is the industry's best practice, Instead, we're learning how to assemble a clinical trial and execute it ourselves. And AI is a big part of that process. In the next few weeks, we'll be making some announcements on how we're massively cutting down time, risk, and ultimately cost of clinical trials across these various steps.

Gemma Allen

>> Wow. So let's move on to the economics of this, because I will say we talk to a lot of founders on this show in all sorts of spaces. Right. And I really admire the biotech founder because It isn't like the typical tech founder where you spin up an idea, you sell it, you have customers, you have money. It's also almost instant, right? It takes chops to build a business in this space and to really go the long haul, I think, on hoping for the economic investments to come home to roost. Talk to me about, you know, how you see that. And you guys have raised Series E. Help me kind of understand the commercial dynamics at play here. What does it take, I guess, Viswa, to really kind of stay true to these theses? And where do you continue to balance the books from the perspective of your own P&L and your own investments? We know trials are super expensive. How do you think about that?

Viswa Colluru

>> Yes, I think there's a lot baked into that. We just closed our $311 million Series E, and what that allows us to do is add to our already strong balance sheet and take multiple medicines beyond early-stage hypotheses in the lab, in computer simulations, cells or animals, and actually see if they work in multiple Phase 1, Phase 2, and even Phase 2b trials, which are pre-trials to Phase 3, in patient populations. And the real reason I think entrepreneurs like me do this is they have a personal calling to be able to make medicines. In my own case, I got into biotech instead of, your classic tier 1 South Indian occupation of being a computer scientist and moving to Silicon Valley after losing my mother to chronic myeloid leukemia. And not only was there no medicine when she was diagnosed and her standard of care was something that we couldn't afford as a family, but as a life scientist, I became aware of the tremendous advancements in chronic myeloid leukemia care and treatment, where today it's largely a disease that can be managed chronically. And if my mother had been diagnosed just 10 years later, she would probably be with us here today. So for me, the urgency is to create medicines and accelerate cures. Every single day matters because patients are waiting. And the fact that AI and new ideas that AI can enable can turn back the clock on that is the real reason we do this. But how does the economics of all of this work is a great question. And I'll simply point to the fact that if you can make molecules that improve lives, and we call that molecules that make markets at Enveda, you can create incredibly enduring businesses. I think even at the end of this year, the two GLP-1 drugs are set to outpace all generative AI revenue in the world. And we're hoping that investors have continued to see that there's a sector beyond generative AI.

Gemma Allen

>> Wow. Well, as someone who has lost both my own parents to cancer, I want to say to you, Viswa, that I really commend you and I commend this industry and the work that you're doing. It certainly in my mind brings a lot of hope to some of the narratives that we hear about AI in our daily lives. So I certainly hope you're successful. Last question for you. What's ahead? you said you've obviously raised a lot of money. Does that give you some runway? You're at about 300 people. Will you continue to hire? And what does the map look like between now and I guess FDA approval for some of those drugs?

Viswa Colluru

>> That's a great question. In our view, I think answering the economics question more directly, we've turned the early stage process of drug discovery from 4 years and about $10 to $15 million to about a year and 1 million using our technology, our approach, and our operational capabilities that are vertically integrated. So it allows us to take the money we've raised very, very far, not just in the clinical trials that I just told you about, where we'll have over a dozen different clinical readouts in the next couple of years, or our ability to generate these candidates at a tenth of the cost and 4 times the speed, but really investing in the core AI engine that allows us to find life's chemistry and make a completely new database of the most powerful sources of medicines historically and going into the future.

Gemma Allen

>> Wow. Well, it is wonderful to have you on NYSE Wired Rx and to listen to this story. you certainly have our support and our hope that you're successful along with your team in this mission. Thank you so much for joining us on the show, and hopefully come visit us in New York from Boulder. Someday soon.

Viswa Colluru

>> Very, very soon. Thank you for having me.

Gemma Allen

>> I'm Gemma Allen here at theCUBE Studio at the New York Stock Exchange. This is NYSE Wired's Rx. We talked to some of the builders and breakers and visionaries defining this next wave of biotech. Thanks for watching.