Terray’s Jacob Berlin on The AI-Powered Future of Medicine
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We generated a data set the world hadn't seen before. You know, in the early years of Ture, like we want to not invent stuff. We want to use stuff off the shelf that's cheap and works and does what we need and move on to other hard problems. Welcome to Founded and Funded. I'm Madrona partner, Chris Picardo. And today I'm thrilled to welcome back Jacob Berlin, co-founder and CEO of Ture Therapeutics. It's been three years since Jacob first joined the show to discuss Ture and what a journey it has been.

Back then, Ture was just beginning to reshape the landscape of drug discovery, and today they've firmly established themselves as a key leader in generative AI-driven small molecule drug design.

In this episode, we'll explore Ture's evolution in AI. The company has been harnessing the power of generative AI since long before CHAP GPT brought the technology into mainstream conversation. We'll also discuss how they built the world's largest chemistry dataset, offering a unique advantage in small molecule drug discovery, and how their latest $120 million Series B fundraise will prepare their internal programs into clinical trials

and further enhance their AI platform. Jacob will share insights on where he sees the future of AI and drug design, and we'll dive into how founders can balance internal innovation and high-profile partnerships. This episode is a must-listen for anyone interested in how AI is transforming the life sciences and what it takes to build a successful biotech company from the ground up. Jacob, welcome back to the podcast.

Thanks, Chris. Super fun to be back here. Pretty wild that it's been three years and everything that's gone on and super excited to be back and talk about Ture some more, one of my favorite topics. Since it's been a while, can you give me the brief overview and maybe even the elevator pitch of what Ture does and what's happened since then? Absolutely. Ture is a biotech company focused on autoimmune disorders and immunology down in Los Angeles. We bring our unique proprietary hardware and experimentation

enable AI-driven small molecule drug discovery in a way that's impossible without it. And so we're deploying it for that internal pipeline on autoimmune disorders and also for our partners across a range of indications. That was a good elevator pitch. It's succinct. But before, you know, we get into everything, you mentioned small molecules there a couple of times. Could you just explain why small molecules and why that's an important thing to be working on?

Yeah. Small molecules would be the medicines that you're probably almost familiar with, the pills in your bottle that you can take by mouth, you can carry them around when you go travel, and probably some of the oldest types of remedies available to humans.

At this point in time, there have been incredible advances such that there are other classes. We now call those small molecules because there are large molecules, which are typically antibody-type therapies or protein-type therapies. Large molecules typically made by biology or analogous to biology. Then there are, of course, also cellular therapies and genetic therapies and others on the scene. For us, we're exclusively focused on small molecule therapies, which remain the world's most abundant

most impactful medicines and there's a lot of opportunity still to develop new and better medicines in that area. Yeah, I think about it as if for most cases, if you could develop a small molecule therapy for something that worked equally well, not all cases, but for many, it's better for patients and very impactful to have your medicine delivered in that form factor. A 100 percent.

all of the different forms of therapy that bring relief are incredible, but they do have really different levels of complexity in terms of manufacturing, distribution, investment to do it. You can see it, I think today, for example, the genetic medicines, which are really amazing, like lifelong cures to previously uncurable diseases.

but they take many months per patient to do and millions of dollars in cost. And although that one in particular, I don't know if you can make a small molecule, you know, analactyl, you can see that as you move down to a pill that you can carry in your pocket and take for your disease while you travel the world and, you know, go out with friends, that

That's obviously an advantage modality provided it's safe and effective. And so I do think they remain the medicine of choice when you can make them. You know, there's a funny story that I wasn't planning on sharing, but I'm going to share, which is something that you shared with everybody when you last came and spoke to Madrona to give the update, which I think was close to a year ago now. And, you know, we were working through the pitch deck and talking about how the Series B was going to go. And I remember the special appendix that you brought.

And it was basically, here's where we were and here's where we are. And the where we were page, I think had one chart with a couple of dots.

That was it. The where we are now page had, I don't know, as many dots as you could fit in while still making a legible. And that was just a sub sample. And I keep thinking like basically that explains a lot of sort of just the amount of scale that you've put into this company since then. Yes, really incredible. You know, when we came and chatted last time, three years ago,

we had, by the way we count, zero measurements now, the way we count today. Because the first three and a half years of the company were about taking that experimental innovation, the proprietary hardware from

an academic invention where my co-founder Kathleen was pushing buttons on the syringe pump. We were doing like one microarray a week and it was all artisanal and we'd measure 32 million measurements maybe that week, which was a lot. It was huge. It was way more than I've ever done in my career, but nothing like what we do today. When I last came in, we were at that moment where we had industrialized it and we had this incredible array of automated systems, both making and using those arrayed

arrays and measuring those datasets, following up and making molecules to put into downstream assays and following them through the drug development.

pipeline. And we were really at that moment, though, at the beginning of our pipeline journey and at the beginning of our AI journey, because we didn't yet have the data set to drive those two. And so in the last three years, you know, I say zero because we had made, of course, many, many measurements before that day. But that was the day that we sort of like locked to a consistent format. And there's a bunch of nitty technical details

details that nobody needs to know about nor will I tell you. They go into what we decided to do with certain elements of the science on the chip. But once we locked it now, we've measured over 150 billion raw measurements on the interaction side, which map to five billion unique measurements because every data point that we use in our modeling, we measure about 30 times and replicate to make sure it's a high-quality precise data point.

And so in that intervening three years, we've made, you know, 5 billion unique measurements, which has let us build then unique generative AI tools to design small molecules. And that's most importantly, as we'll probably go next, you know, that's let us then really move our pipeline and move our partnership work. And we've realized a whole number of, you know, really significant milestones between here and there. And yeah, looking back is always, uh,

I don't know, shocking, exciting, terrifying for a founder. You look back at the old deck and you're like, I'm so glad somebody funded that. We're doing a lot better today. And that one's true. Looking back at what you all backed in the beginning, it was very much the vision and the

core of what we do, but the realizations come in the last few years. It's been exciting. I think it's a fun one for me to think back on because the first time we met in person was a couple of weeks before the COVID shutdown. And since then, even dealing with that

It's just a different company. And so I think it's fun to be able to have this conversation when you're now much more of a scaled up founder, leader of a company. You've learned a lot of these lessons. And so, you know, I want to jump into some of those. But since you mentioned it and since we've talked about it a couple of times, one of the major milestones that you recently hit sort of over the summer and announced, you know, in the early fall was this, you know, large $120 million Series B fundraise.

We're all super excited about that. I think it's a pivotal moment for the company. But, you know, I'd love for you to just sort of share the quick, you know, overview of like, what is that fundraise going to get for you? That's a lot of money. People think biotech companies raise a lot of money. You've got big plans for it. So I'm curious, you know, just what exactly will that enable? Yeah, it's really incredible, you know.

I think all the founders out there know, the saying, the first dollar you raise is the hardest is still, I think, true. But the markets have certainly been, as I think everyone involved with biotech knows, a little bit bumpy. I would say we started, maybe all times are interesting to start a company, but we started it right before COVID.

ran into all their operational challenges like that that you mentioned, and then maybe one of the greater boom markets for biotech funding in progress, and then one of the greater devastatingly bear markets that fall on the heels of that.

Yeah, we've stayed real focused on execution from day one through all of that and into today. And so we're really excited because my life's mission has been to cure somebody for decades now. And we're finally coming up on that, you know, owing to the nature of the market, we're probably not going to cure one person. We'll probably cure many, many people, hopefully.

But this money is so important to us because we'll be bringing our first programs into the clinic from the first wave of targets we worked on. One of the unique aspects of the scale of the integration of experimentation and computation is that we can work on far, far more targets than your average biotech company of our scale and then pick out the best opportunities and move those forward. We have the first couple of those headed into the clinic out of what we call our first wave of targets.

We have a second wave of targets behind that that we're also super excited about. We're continuing to invest in the platform as well, both for our own pipeline progression like those programs which I should note, everything internal is an autoimmune disorders and immunology, but also delivering for our partners. When you think about the milestones from three years ago to today, not only do we industrialize the technology and generate data and build these AI tools, but we move those first programs of our own towards the clinic and now we'll move into the clinic.

We started to build the second wave and all the programs behind it. We signed scale partnerships with Calico and BMS. Now we also have a co-development deal with Odyssey, which is another exciting opportunity for us to use our technology advantage to really deliver medicines that matter. Then as you know, just recently, we also signed a deal with Gilead to bring the same approach forward and solve really challenging problems for them.

We're really excited to become the AI driven small molecule provider of choice for large pharma. And it's been incredibly gratifying to see that. And so we're going to continue to invest in the platform and being best in class at the intersection of large scale, precise, iterative experimentation and AI, but also super excited. The primary piece is moving that pipeline into the clinic.

Yeah, it's the partnership velocity really since you started to sign these partners or go after them has been pretty incredible. And we'll get back to that because that is a hot topic for companies across the board. But something you just mentioned, I think, is also a hot topic, which is this platform versus product debate that seems to rage on a cyclical basis in biotech investing, whether it's coming from the investors or the companies.

And Ture is very much building a platform. I mean, we certainly have lots of products, but there is a big platform vision there. I think it'd be great for you to just talk for a second about what being a platform means in biotech and, you know, why you have conviction in that approach and why you think for Ture, that's the right path to take. Yeah, it is, you know, probably one of the existential and repetitive questions in our industry. Are you platform? Are you asset?

And the market certainly moves back and forth with its own opinions about which one is in and out. But I think for us, you know, we've always been drawn to trying to solve the problems that are unsolvable and really transforming the cost, the speed, but most importantly, by far, the success rate of small molecules in development. I think probably

almost everybody out there knows drug discovery is really hard, that with all of the incredible expertise and all of the tools that are available today, the failure rate even after reaching the clinic is the vast majority of molecules. That doesn't count needing to go from the idea to the clinic. So overall, it's clearly a very, very hard problem. So it has an urgent need kind of always for

better approaches that give you a transformative opportunity to bend the whole curve and transform what can really be done out there. For us, we came at it from that macro, good for the world, good for value, good for science approach and have been a platform company since day one. The core innovation was transforming how you measure chemistry, which then let us transform what you can do on the AI compute side. We face the same tensions though, because our product is not

our microarray chips, it's not our AI model, it is the molecules. The back end of it is, of course, assets, the molecules themselves and as they move through. As you know, and probably many listeners know, the market has moved towards the asset world, moved towards the clinic. But that's why we do the partnership work, it's why we have a diversified internal pipeline. We feel very strongly that the right way to monetize, realize value, and deliver maximum impact from a platform,

is to basically translate it into as many assets as is possible, leveraging both private capital, but also partner capital and partner resources to move multiple programs across different opportunities. And so there's room for both. There's a lot of patience and need that you can address either by working off of

a singular item and finding a clever way to do it. But also I think there's a lot of room for transformative new approaches. I think you see that right now, obviously AI-driven small molecules and large molecules has been a huge topic of interest because it offers that opportunity to really transform success rates, which would be worth

millions of lives and billions and trillions of dollars goodness knows if you really can change the whole thing. One thing you mentioned in there which I think involves this platform strategy and you've mentioned to me really since day one is creating a long-term company.

versus something that you can build an asset maybe for three to five years and that's going to look really great for a pharma to go acquire. And either I made this up or you said it to me, but I remember you telling me asking you like, hey, what are you going to be doing 10, 15 years from now? And your answer is running to Ray. And I think that's a great answer, but it's a lot about how you've thought about the vision and what you're building and sort of where this can go on a true long-term perspective.

I guess this comes from the quintessential entrepreneur, too naive to know you're wrong type plan. I'm eyes wide open that the number of new biotech companies that transition to full-fledged commercial scale pharma companies is, I don't know, one a decade, one every couple of decades, but I really think Ture can be that one.

So we've always been focused on returning, now I sound like a broken record, but like maximal impact to patients and of course, maximum value that comes with that. That's always seemed to me to be realizing the inherent advantage of the platform at scale and bringing those medicines all the way through, which means building the whole thing. Obviously, in our industry along the way, sometimes people show up and make offers that everyone

as he has to, but I think you've got to plan for the stuff you can control and plan for the strategy that you can execute by yourself and plan for the strategy that you think overall is most valuable and most successful. For us, literally since day zero, that's been we're going to make and sell our own medicines one day, a whole bunch of them, and we're going to change the way the world does this. We're part of the way along that journey now, which is really exciting, but we still have a long way to go as you know in our industry, the timelines and capital costs.

scientific risk and discovery and development remains large, but we put ourselves in a position to execute on it now. I will say for me, it's super fun to be able to work with a team like Tarey and you and Eli, your co-founder.

because of that true long-term view. I think it's really differentiating. We'll get back to a couple of your sort of thoughts on the business building side of this, but I think it's a good time to take a nice detour, deep dive into the AI and the science that's going on here. And given I think it's the hottest topic in biotech right now, maybe besides the GLP-1 obesity drugs, we've got to talk a little bit about the AI that you've built. You said this before, but I think it's really interesting. The AI came...

a little bit after the data generation came. But since then, you've built a ton of it. And I'm curious how you think sort of the small molecule AI world is different than the protein design world or the antibody world, and what you've done internally to kind of build out this AI infrastructure. Now you've wandered into my favorite topics. Although I love talking about everything, I can't resist my origin story. Anything science, you know, risks sending me down the rabbit hole for the rest of the podcast. So, you know.

come for the AI discussion, stay for the enantiomer discussion that follows in the organic chemistry section. But in all seriousness, it really follows the data. I say this a lot, but I really think about the world as AI is transformative when it rests on top of the right type of data, which I think are those three pillars, large, precise, iterative. And I think in every case,

where that data comes about and is transformative, it rests on top of hardware innovation that compresses the cycle time, transforms the cost exponentially, and allows you to realize it. The one out there in the world that's easy to pattern match to is digital photography. You go from old photography where you probably never have enough images to build DALI or SOAR or any of these tools or facial recognition to digital photography. Now there's millions and billions of images and you can train

you know, the models and retrain them and refine them. And as probably people talk about other podcasts, you know, you teach you what a cat is and you teach you what a dog is and you need all the images to train it into which one's a cat, which one's a dog before you then go ask it for like, I'd love a picture of my kids cuddled up with a bunch of cats. And now it knows and it makes you a picture. And the same problem exists in our space. And that's why, you know, I did my postdoctoral work. It's why I ran the lab. It's why I started Trey, which is that chemistry data is hard to get.

Traditionally, it was me and people like me making molecules and put them in a flask or putting them in a well on a 96-volt plate or 1536. Yes, I know, they're different well formats and measuring them. It just is slow. It takes a lot of time to make those molecules.

There's some interesting automation chemistry approaches to it, but mostly that problems remain very stubborn. Making molecules at scale and putting them into assays is still pretty slow and still pretty expensive.

Where AI has come into our world, it's come into it where there have been curated high-quality data sets, like AlphaFold, of course, where the government, fortunately, curated a large crystallography database. But also, there's an enormous sequencing database that came about thanks to next-generation sequencing and the plunging price of sequencing and the time taken to do it.

That's done transformative things for AI around protein design, obviously, protein folding, large molecule design. I think that's why you've seen AI be most successful in biotech first in large molecules. The question we're tackling is, great, now I want to put a small molecule in there and that dataset has just been smaller. The entirety of public data there is maybe 100 million measurements spread across a variety of different assays. We're really convinced that the unlock for AI there

is the data, the measurement of small molecules interacting with proteins at a large enough set and across enough targets and enough molecules to build generalized models that can solve these problems quickly and go where humans couldn't before. That's what we've been after. That's why the sequence, we always knew

our data would fit with, well, back then we called it ML, but now we call it ML. We always knew it would fit with these large computational approaches because we just generate too much data. We generated five billion data points last three years. What human is going to flip through that and do anything?

But what to build, we needed to get the data in first. And now we've been able to build really transformative tools. The first of which was Kowati, which actually doesn't depend on the data. It's the large language model of chemistry that we built such that we can work with our data in a computational way and smoothly traverse chemical space to optimize molecules. Can you explain just on that, you know, exactly what Kowati on

unlocks, maybe what it is and then what it unlocks for just doing AI in this world? Yeah, that's an easy one because Kawade is a South American raccoon. I think that pretty much wraps it up. But now, in all seriousness, in addition to being a South American raccoon, it is our large language model of chemistry. For any of these AI applications, you need basically a mathematical space within which the optimization is taking place.

But you need to take your sort of real thing that you want at the end and convert it into math, if you will. And that's what Kauai does for chemical structures. And so chemical structures, you know, can be represented a variety of ways. One is as a three-dimensional object, which is probably the closest to what's really going on out there in the body, in the world. And so that's a series of atoms and bonds that make a three-dimensional shape.

but they can also be written down and abbreviated notation like a word. You can write them down as both and people use them interchangeably in different applications in our industry, but neither of those is a math representation. What Kawadee did was it's a contrast optimization where you train on those two to build a common math language that can translate back and forth between either of those.

Importantly, it allows you to then, I think of it as like a chemistry map, where it's basically just mapping how similar or different molecules are in a mass space so that if you optimize within that space and move close by, the molecule looks similar. If you go far away, it looks dissimilar. Getting that right took a lot of work and the team did an incredible job. It was published recently. It was on the cover of JCIM and we

open source the first version for people to work with it. And we really think it's done tremendous, you know, things for how you can translate structures back and forth in math and then move around to optimize. But that's just the sort of first building. Like if the data is the, you know, foundation, the Kauai large language model is the next piece that allows you to traverse. But then you actually need the sort of AI module, if you will, that will

combine those two and move around and solve the problem you're after solving.

Yeah, I think from, you know, it's interesting to me is you haven't been able to just take off the shelf machine learning or AI tools. I mean, there's some of them in the workflows and just say, hey, go to work on our data. You're going to get great molecules out of this. You've really built this whole AI infrastructure, including the data infrastructure from scratch, you know, alongside some partners like Snowflake and NVIDIA who have been part of this conversation. But, you know, when you, I'm curious how you think about sort of

the reasoning for doing that and why we've had to build all of the models internally and what that does for our scientists. Yeah, it's been really an incredible journey and one that I don't know if when we started we knew how much of each of them we would do.

and this part's always stressful because there's so many people that have gone into making that possible. Narbe, who's our CTO, has been a driver. John and the entire ML team, Kevin, the whole data team, because as you mentioned,

you have to first be able to get at the data. And so our workflow is very custom. We obviously have inventor proprietary hardware. The way we read it is with imaging. And so we generate like over 50 terabytes of images a day that we need to convert into the numerical values that we're going to use to drive the models. And so that was a whole process that we built from scratch because nobody else really made exactly what we made and nobody processed it like we needed to process it.

And so there are obviously we stand on the shoulders of giants like all scientists and there were stuff we borrowed from, but we built our own because we needed to be able to do that really quickly, really efficiently. And then as you mentioned, you know, we work with AWS and Snowflake because we generated a data set the world hadn't seen before. You know, in the early years of trade, like we want to not invent stuff. We want to use stuff off the shelf that's cheap and works and does what we need and move on to other hard problems. But when we showed up to vendors and we're like, hey, we have 5 billion measurements coming up soon. Like, can we put them into your stuff?

They said, "Chemistry measurements?" We're like, "Yeah." They're like, "Ooh, no, that's a lot." So we worked with instead on the flip side like Snowflake, which is obviously a service built for datasets that large. We saw the same thing with the foundation model of chemistry. We tried every model that was available out there, and we found that when we applied them and used the power of our unique dataset to ask,

Are these models really then connecting molecules the way we want to connect them for optimization? We got some suggestions we didn't think were that reasonable. And I think we'll come to this, but it's one of the real keys of having expert humans in the loop when you build and use these models, because there were answers that our medicinal chemistry teams were immediately like, no way. Like, this is off its rocker. It's way off.

We had to go build something that constrained it and gave answers that made sense and really allowed us to optimize molecules. The same thing has happened with the generative side of the AI problem. The team has done incredible work building all the way from the ground up, the data processing through the foundation model of chemistry to the generative and predictive models that go into designing molecules to solve the problems. We've built it all because we couldn't find what we wanted out there.

It's interesting. I think I joke and we've joked before, you know, biotech companies are obviously not software companies in many senses. But on the other hand, you've pretty much built an entire software company from the bare metal infrastructure up within a biotech company. And it's about equal to the size of the science, right, that is going on. I just think it's a fascinating change in how companies are built.

Yeah, I mean, we use as our slogan, everything small molecule discovery should be. And we picked it intentionally because we feel really strongly that you can't anymore just be all one thing, that you're at a huge disadvantage if you're only compute or like only traditional discovery. And so like our intersection is at that intersection of compute. So AI, ML, software, so that's a huge piece of the business, building all of that.

but also the experimental side. We have a huge investment and build in robotics, automation, large-scale data with precision. They iterate. Now, like I said, I repeat myself a bunch, but it goes then in the service of the pipeline and the preclinical development. We still have the teams that you would identify anywhere else, MedCam, biological assays, and everything else that goes into that cell assays. I think you need all of the teams working really closely together

And the last piece is that we also essentially have a little mini manufacturing business in that we make our microarray, you know, proprietary microarray technology by assembling a variety of different things and building our custom libraries in-house. And so we kind of have like four businesses under the hood at Teray. But they all obviously go together to provide the one, you know, singular value driver, which is the outcomes. But I don't think you can do just one of them and be successful in the way that...

we are. I tell people in Madrona all the time if they find themselves and other people, if they find themselves in East LA, they should go for a visit to Ture because it's just visually so striking the amount of automation and hardware innovation and robotics that are just there and required. You know, every time I go and take a peek into what's going on, it blows me away. And I know we've seen that with when, you know, the New York Times visited, for example, and other investors. I mean, just

You kind of have to see it to believe it. Yeah, it is really different. As my brother Eli and co-founder advertises it, it's not just a lab tour. Although it is just a lab tour, but it's an awesome lab. And it's one of the other milestones. Since we were here three years ago, we've really lived the startup life

physical footprint journey has been incredible. In that same like Lookback Deck, we started the company in a local incubator at a shared bench and a shared essentially closet that we did our imaging in. Last time we did the podcast, we had moved from there and matured into a step-up space and we were working in a couple of suites in a shared building.

But we've been really fortunate since then that we moved into a 50,000 square foot headquarters in Northeast LA, Monrovia for those in the know, great spot. And we've really then been able to build our workflows the way we wanted into the physical footprint of the building. So if you come to Trey, you will see like our partners or the New York Times or others have seen this whole first floor where the automated

imaging and liquid handling systems are running to use these little microwave chips and make them billions and billions and billions of measurements, just all humming away like a whole field of them. And it is strikingly different. And the interesting thing is that Upstairs, then, looks in many ways like a canonical biotech drug discovery company, although with a lot more robots in the hoods than on average.

and you can sort of see, you can almost feel how the pieces fit together and work together.

Except perhaps maybe as we talked about for the AI piece where you just see really smart people working at computers. But you see the impact as you move upstairs and downstairs and see the molecules that are being made and tested. It's pretty incredible watching it all come together. I encourage anyone who's interested ever, reach out, let me know. We love to show people what we're doing at Ture. It's a pretty great tour. I'm lucky I get to go all the time, but it's pretty fun.

I want to get into a couple of your business theses and lessons you have to share. But before that, you know, just circling back to the AI side, I think one of the things that Tarek is really good at doing is predicting completely de novo structures. And so when I say that, you know, I contrast that towards a bunch of other AI platforms which are very good at predicting things, especially binding molecules.

but they look 99 percent similar to known binding molecules. That's impressive in itself, but it's very different than how you've approached the problem and how you think about this pure de novo or unrelated structural prediction. Maybe just talk a little bit about why that's hard and why you think that's also the way forward. Yes, interesting. In my mind, this one connects back almost to the platform versus asset question.

In the same way that there's value to a company being wholly invested in one medicine and bringing it through and being successful, there's value to patients and to the ecosystems to taking previously known molecules that either do work or sort of almost work for something and making them better.

innumerable examples of that, including the statins everyone knows and we're taking. It wasn't the very first one that became the most ubiquitous. There was refinement and the most ubiquitous one was an optimized version thereof. Those are exciting problems and problems that we can tackle,

But I think the most exciting and the biggest benefit for both human health as well as value is solving the problems that just can't be solved out there. That's like, as you mentioned, would be what we call de novo, where nobody knows where the molecule is or what it looks like. It's out there in Kauai's chemical space mapping somewhere, but goodness knows,

And the key then is to be able to do your own measurement to get a starting toehold where there wasn't data before. Because as I talked about, AI always needs data. And so I don't think it's any surprise that AI's first sort of

impact on small molecule design is predominantly working in areas where there was already data, working around known molecules, patents, things that were out there, and making better versions of those, which again are very valuable, have impact, and are also honestly often much quicker to bring to the clinic because of the path that's been trod before you.

But we work on a different approach, which is bring us your hardest thing. I think this is why you see the partnerships with large pharma because they're bringing us, of course, the things that they can't do themselves, otherwise they do them. We're out there working on very hard things where often there is no known starting point. We do this for our internal programs as well. For us, that's why we use our sequential iterative process where we use our platform to measure

very broadly across chemical space, 75 million plus molecules. But obviously, chemical space is infinite, so we're very sparsely sampling, looking for a starting point, like where can we possibly get going on this?

And that gets you going on the de novo, but it doesn't possibly give you enough data for the model to be impactful. And so we followed that with a design and test cycle where we then build a new library of millions of molecules around that area of interest, such that we massively enriched the models with a lot of local knowledge around the area where we do know now that there's an answer in there. And that sequential...

build that's the model both broadly understand chemical space, what doesn't work mostly, and then enrich into what does work and become essentially like a AI co-pilot for the MedChem team where they're able to ask it questions as they go about their work and think about like, "Hey, I need this molecule with these improved properties. Where should we go?" I'm super excited about it as you can tell,

There's like nothing more exciting than finding a totally new answer to a, you know, intractable science and health problem. And I think our approach really gets it done. Yeah. I mean, I know that you've now found many of those molecules because I get to see the outputs of that, not in real time, but, you know, on a regular basis. So it's impressive how that's been able to, you know, occur with all the work that you've done.

I have two more questions for you. I think both are more on your kind of business side and the business philosophy. Now that it's three years in, I would say you're an experienced founder. Three years since the last podcast. That's true. Six plus total. That's right. I'm a deep expert now. You're a very experienced founder. I think we've got a few years to go. Scaled the company a bunch since the last time we talked. So, you know, I want to hit two things first.

On the business side, you know, Trey's always been about the partnerships as well as the pipeline. And I am curious how you think about this strategy, because there are a lot of companies that will only focus on their internal pipeline, and that's not how you approach the business strategy. Yeah, I think this also goes to the platform build question. Yeah.

I was influenced by an article I read a long time ago looking at expected returns across numbers of assets. Back then, I think the conclusion of that particular analysis was like, well, if you have 20 programs in the clinic that are appropriately sized to their market and whatnot, you'll positively return over them.

Of course, you see the real-world version of this. Large Pharma is a successful, profitable industry that makes many, many bets, but in part also does it through acquisition and letting the bets play out outside of their ecosystem. If you can resource enough thoughtful bets, you're likely to be overall successful. But the inverse of that is we certainly know that one singular bet actually has odds on to lose.

And so trying to resolve that, as you know, I'm a baseball fan, and so I talk about this a lot as sequence luck. One team, all their hits come together, they score a bunch of runs. The other team only gets one every inning, they lose. And then one, I'm a Boston fan, I just like to point out one team sometimes also drops the ball for an entire inning and blows the World Series, but that happens. But coming back to what we're talking about, we work with partners because

I mean, it would be great if you guys would give us a few billion dollars and then we would just resource all of our own programs, but that just hasn't worked out yet. And so... Someday. Partners give us both. They give us the opportunity to resource more programs than we otherwise could through their capital commitment, not only in what they pay us, right, to do the partnership, but the fact that they are going to then carry the back-end development of those molecules through the clinic and out to patients. And so we have an opportunity to realize value

where we otherwise wouldn't reach patients we otherwise wouldn't. The other piece is that also realize expertise. Internally, we were fully on immunology and autoimmune disorders, but with our partners, we touch a variety of other therapeutic areas that would have been a whole another build for the company to move into those types of indications.

I think it's a way to realize the promise and value of the platform while you're still a smaller company and be capital efficient as you build and grow. Tilt the odds of overall success in your favor from a singular coin flip, if you will, although the coin's very negatively weighted in biotech, to a ensemble approach that starts to give you a leg up on sequence luck. If you do seven programs,

and the first two fail and the last five succeed. That would be incredible. That would be the biggest home run ever. But you might not get to do the last five if you only have the first two bets. And so this is a way to do them all at the same time, do them with really expert, wonderful partners who are

just well-resourced and well-experienced to be successful with the programs we do with them. So yeah, it's always been both for us. I think that's really well put. Finally, I want to ask you about one of my favorite parts of Ture, which is the very unique and extremely high-performance culture you've built.

You've set an incredibly high bar just to get a job at Ture. And I can think of one time, maybe, in the last four years since I've been sort of deeply involved, which is actually I guess closer to five now, since I've been deeply involved in the company when we've lost anybody. And, you know, or, you know, we've lost someone or even like, oh man, that was really terrible that we lost that person. How have you done that? Yeah, it's really remarkable to me because...

As opposed to some of my friends and colleagues in other markets, it's not one of the things I worry about when I go to work very much. It's like, oh, we're unexpectedly going to have a large churn in the company. We've been really fortunate to work with wonderful people, including yourselves and the rest of the investing ecosystem too. It's really remarkable to me how mission aligned everybody involved with Trey is.

I've always been, as you probably can feel through this, like super mission driven. Like I'm here to make the world a better place and this is how I want to do it. And I think that shines through as we hire people and build the team. And so it's actually been one of the most incredible parts of this last journey because the other piece of the milestones is last time we talked, the company must have been like four times smaller. And we've been through that growth.

And maintain, as you noted, the quality of people we want, intensity. And so it'll sound kind of cliche, but I think it's because we hire for the person and the culture and the way we work together, not necessarily just for the skill set, which does make our searches take forever. We talk about this all the time. The trade is always time. Because you can find the person who not only does what you want,

but also does it how you want to do it. It's just going to take longer if you hold to both bars. And there are times where that's really tough and it's like, we really need somebody. But like overall, we're always happier and more successful when we get both. And so...

We've built an interview for that since the beginning. As we talked about, I'm not a huge fan of just canonical words for values. Like, oh, we're about excellence. Of course we are. So is everybody else, I hope. Otherwise, I don't know what you're doing. But we're really focused on how we work with each other and the operating principles, how we communicate with each other, how we make decisions, how we treat each other.

It's been just a real joy to watch that cascade down through the teams. I have a little rotating lunch I do across the company, like three, four people every week just to say hi. It's explicitly non-work. They just get to hear my awesome baseball jokes and thoughts about movies and TV and whatnot.

But one of the new employees was there and I was like, oh, how'd you find Trey? They're like, oh, well, my friend who used to work here, she left for a school opportunity that was awesome for her. It was like, you gotta work at Trey. It's awesome. And nothing makes me happier. The science part obviously motivates me. I love science still. I'll go back and tell you more about organic chemistry if you'd like. But

the building and the people side is every bit and maybe even more gratifying to see such a wonderful team work together. I don't know what the secret is except not making compromises on that aspect. There's just never anybody who's good enough that you're willing to compromise how you want to do it.

Well, I can't think of a better place to sort of end the discussion on that note about amazing people. You are one of them. It's been really fun to work together and I really appreciate you joining me three years later for this discussion. Well, I appreciate it, Chris. Not only the awesome conversation today, but as you know, you guys have been...

convicted supporters of our work from the beginning. And it's not that easy to find people who want to take the big, big bet and go for the whole journey. And so it means a lot to me. And, you know, the conversation we just had, you guys have been mission aligned and how we want to work together aligned from the beginning. So appreciate it. So excited to be back. And thank you so much. Thank you.