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Terray Therapeutics CEO Jacob Berlin returns to Founded & Funded after three years to share how the company has scaled from an ambitious startup to an industry leader in AI-driven biotech. Learn how Terray’s proprietary hardware, combined with the world’s largest chemistry data set, is powering new discoveries in small molecule drug development.
Jacob also discusses how the company’s $120M Series B fundraise will prepare their internal programs for clinical trials and further enhance their AI platform. He shares insights on where he sees the future of AI and drug design and dives into how founders can balance internal innovation and high-profile partnerships.
Don’t miss this deep dive into the intersection of AI, biotech, and innovation!
This transcript was automatically generated and edited for clarity.
Jacob: Thanks, Chris. Super fun to be back here. It’s pretty wild that it’s been three years and everything that’s gone on, and I’m super excited to be back and talk about Terray some more. One of my favorite topics.
Chris: So, since it’s been a while, can you give me the brief overview and maybe even the elevator pitch of what Terray does and what’s happened since then?
Jacob: Absolutely. Terray is a biotech company focused on autoimmune disorders and immunology down in Los Angeles. We bring our unique proprietary hardware and experimentation to enable AI-driven small molecule drug discovery in a way that’s impossible without it. We’re deploying it for that internal pipeline on autoimmune disorders and also for our partners across a range of indications.
Chris: That was a good elevator pitch. It’s succinct. Before we get into everything, you mentioned small molecules there a couple of times. Could you explain why small molecules and why that’s an important thing to be working on?
Jacob: Small molecules would be the medicines that you’re probably all most 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, so large molecules are typically made by biology or analogous to biology. Then there are, of course, also cellular therapies, 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. There are a lot of opportunities still to develop new and better medicines in that area.
Chris: 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.
Jacob: 100%. 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, and investment to do it. You can see it today, for example, in 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. Although that one in particular, I don’t know if you can make a small molecule analog to, 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 go out with friends, that’s obviously an advantage modality provided that’s safe and effective. I do think they remain the medicine of choice when you can make them.
Chris: 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 we were working through the pitch deck and talking about how the Series B was going to go. I remember this special appendix that you brought, and it was basically — here’s where we were, and here’s where we are. The “where we were page” had one chart with a couple of dots, and 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. That explains a lot of just the amount of scale that you’ve put into this company since then.
Jacob: Yes, it’s really incredible. When we came and chatted last time three years ago, by the way, we count zero measurements 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 Elison, was pushing buttons on the syringe pump. We were doing 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’s 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 arrays and measuring those data sets, following up and making molecules to put into downstream assays and following them through the drug development pipeline.
We were, at that moment, though, at the beginning of our pipeline journey and the beginning of our AI journey because we didn’t yet have the data set to drive those two. In the last three years, I say zero because we had made, of course, many, many measurements before that day, but that was the day that we locked to a consistent format, and there’s a bunch of needy technical details that nobody needs to know about, nor will I tell you to 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 mapped to 5 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.
In the intervening three years, we’ve made 5 billion unique measurements, which has led us to build then unique generative AI tools to design small molecules. That, most importantly, led us to then really move our pipeline and move our partnership work. We’ve realized a whole number of significant milestones between here and there. Looking back is always, 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 realization’s come in the last few years. It’s been exciting.
Chris: 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. 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 I want to jump into some of those. Since you mentioned, and we’ve talked about it a couple of times, one of the major milestones that you recently hit over the summer and announced in the early fall was this large $120 million series B fundraise. We’re all super excited about that. I think it’s a pivotal moment for the company, but I’d love for you to share a quick overview of what that is 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. I’m curious just what exactly will that enable?
Jacob: It’s really incredible. I think all the founders out there know the saying, “The first dollar you raise is the hardest.” I think that is still true, but the markets have certainly been, as I think everyone involved with biotech knows, a little bit bumpy. Maybe all times are interesting to start a company, but we started it right before COVID, ran into all their operational challenges like that you mentioned, and then maybe one of the greater boom markets for biotech funding and progress and then one of the greater devastatingly bear markets that followed on the heels of that. We’ve stayed really focused on execution from day one through all of that and into today. 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. Owing to the nature of the market, we’re probably not going to cure one person. We’ll probably cure many, many people hopefully.
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 computations 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, and 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 in autoimmune disorders and immunology, but also delivering for our partners.
When you think about the milestones from three years ago to today, not only did we industrialize the technology, generate the data, and build these AI tools, but we moved those first programs of our own toward the clinic, and now we’ll move into the clinic. We started to build the second wave and all the programs behind it, and we saw scale in partnerships with Calico and BMS. Now we 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. And then, as you know, just recently, we 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. We’re going to continue to invest in the platform and be 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.
Chris: It’s the partnership velocity since you started to sign these partners or go after them has been pretty incredible. We’ll get back to that because that is a hot topic for companies across the board. Something you mentioned I think is also a hot topic, which is this platform versus product debate that seems to rage on a cyclical basis and biotech investing, whether it’s coming from the investors or the companies and Terray 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 would be great for you to talk for a second about what being a platform means in biotech, why you have conviction in that approach, and why you think for Terray that’s the right path to take.
Jacob: It is 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, 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. Overall, it’s clearly a very, very hard problem. It has an urgent need 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 our 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 faced 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, and as you know, and probably many listeners know, the market has moved towards the asset world, move 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.
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 have been a huge topic of interest because they offer the 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.
Chris: One thing you mentioned in there, which involves this platform strategy, and you’ve mentioned to me really since day one, is creating a long-term company versus something that, “Oh, 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.” Either I made this up or you said it to me, but I remember asking you, “Hey, what are you going to be doing 10, or15 years from now?” Your answer was running Terray. I think that’s a great answer, but it’s a lot about how you’ve thought about the vision, what you’re building, and where this can go on a true long-term perspective.
Jacob: 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 decades, but I really think Terray can be that one. We’ve always been focused on returning, now I sound like a broken record, but 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 says yes to, but I think you’ve got a 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, and we’re part of the way along that journey now, which is really exciting, but we still have a long way to go. As in our industry, the timelines and capital costs and scientific risk and discovery and development remain large, but we put ourselves in a position to execute it now.
Chris: I’ll say for me, it’s super fun to be able to work with a team like Terray and you and Eli, your co-founder, because of that true long-term view. It’s really differentiating. We’ll get back to a couple of your thoughts on the business-building side of this, but I think it’s a good time to take a nice detour or deep dive into the AI and the science that’s going on here. 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. I’m curious how you think the small molecule AI world is different than the protein design world or the antibody world and what you’ve done internally to build out this AI infrastructure.
Jacob: Now you’ve wandered into my favorite topics, although I love talking about everything. I can’t resist my origin story, anything science, risk, sending me down the rabbit hole for the rest of the podcast. Come for the AI discussion, stay for the enantiomer discussion that follows in the organic chemistry section. In all seriousness, it really follows the data. I say this a lot, but I 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. 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 DALL-E or Sora or any of these tools or facial recognition, to digital photography. Now, there are millions and billions of images and you can train the models and retrain them and refine them. As people probably talk about other podcasts, you teach 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.” Now it knows, and it makes you a picture. The same problem exists in our space. That’s why I did my postdoctoral work. It’s why I ran the lab. It’s why I started Terray, which is that chemistry data is hard to get.
And traditionally it was me and people like me making molecules and putting them in a flask or putting them on a 96 well plate or 1536, yes, they know they’re different well formats, and measuring them and 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 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, and 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.” That data set has been smaller. The entirety of public data, there is maybe a hundred 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 work for humans that couldn’t before.
That’s what we’ve been after and that’s why the sequence we always knew our data would fit with, back then we called it ML, but now we call it ML AI. We always knew it would fit with these large computational approaches because we generate too much data. We generated 5 billion data points in the 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 COATI, 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.
Chris: Can you explain exactly what COATI unlocks, maybe what it is, and then what it unlocks for doing AI in this world?
Jacob: Oh yeah, that’s an easy one because COATI is a South American raccoon, so I think that pretty much wraps it up. But no, 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 the real thing that you want at the end and convert it into math, if you will. That’s what COATI does for chemical structures. Chemical structures can be represented in 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. That’s a series of atoms and bonds that make a three-dimensional shape, but they can also be written down in 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 COATI 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. I think of it as like a chemistry map where it’s basically mapping how similar or different molecules are in a math space so that if you optimize within that space and move close by, the molecule looks similar, and 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-sourced the first version for people to work with it. It’s done tremendous things for how you can translate structures back and forth into math and then move around to optimize. That’s just the first building. If the data’s the foundation, the COATI large language model is the next piece that allows you to traverse, but then you need the AI module, if you will, that will combine those two and move around and solve the problem after solving.
Chris: What’s interesting to me is you haven’t been able to take off-the-shelf machine learning or AI tools. There’s some of them in the workflows and say, “Hey, go to work on our data. You’re going to get great molecules out of this.” You’ve built this whole AI infrastructure, including the data infrastructure from scratch alongside some partners like Snowflake and NVIDIA who have been part of this conversation. I’m curious how you think about the reasoning for doing that and why we’ve had to build all of the models internally and what that does for our scientists.
Jacob: It’s been 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. This part’s always stressful. 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. Our workflow is very custom. We obviously have invented proprietary hardware. The way we read it is with imaging, and so we generate 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. That was a whole process that we built from scratch because nobody else made exactly what we made, and nobody processed it like we needed to process it.
Obviously, we stand on the shoulders of giants like all scientists, and there was stuff we borrowed from, but we built our own because we needed to be able to do that really quickly and efficiently. We work with AWS and Snowflake because we generated a data set the world hadn’t seen before in the early years of Terray. 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, can we put them into your stuff?” They said, “Chemistry measurements?” And we’re like, “Yeah.” They’re like, “Ooh, no, that’s a lot.”
We worked with instead, on the flip side, like Snowflake, which is obviously a service built for data sets 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 use the power of our unique data set 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 they were answers that our medicinal chemistry teams were immediately like, no way. This is off. It’s a 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 has happened with the generative side of the AI problem. The team’s done incredible work building all the way from the ground up, from 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 built it all because we couldn’t find what we wanted out there.
Chris: It’s interesting. I joke and we’ve joked before, 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 size of the science that is going on. It’s a fascinating change in how companies are built.
Jacob: 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 be all one thing, that you’re at a huge disadvantage if you’re only compute or only traditional discovery. Our intersection is 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 in build and robotics automation, large-scale data with precision, the iterates 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, your biological assays, and everything else that goes into that cell assays. I think you need all of the teams working really closely together. The last piece is that we also essentially have a little mini manufacturing business in that we make our microarray, proprietary microarray technology by assembling a variety of different things and building our custom libraries in-house. We have four businesses under the hood at Terray, but they all go together to drive the one singular value driver, which is the outcomes. I don’t think you can do just one of them and be successful in the way that we are.
Chris: I tell people at Madrona all the time, and other people, if they find themselves in East LA, they should visit Terray because it’s just visually so striking, the amount of automation, hardware innovation, and robotics that it’s just there and required. Every time I go and take a peek, it blows me away. I know we’ve seen that with when the New York Times visited, for example, and other investors, you have to see it to believe it.
Jacob: 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 three years ago, we’ve really lived the startup physical footprint journey. It’s been incredible in that same look back, 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 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.
If you come to Terray, or our partners or the New York Times, or others, you 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 millions and billions and billions of measurements all in a way, like a whole field of them. It is strikingly different. 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. You can see and almost feel how the pieces fit together and work together, except perhaps 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 to reach out and let me know. We’d love to show people what we’re doing at Terray.
Chris: 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, circling back to the AI side, I think one of the things that Terray is really good at doing is predicting completely de novo structures. And so when I say that, I contrast that toward a bunch of other AI platforms, which are very good at predicting things, especially binding molecules, but they look 99% 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. Talk a little bit about why that’s hard and why you think that’s also the way forward.
Jacob: It’s interesting. In my mind, this one connects back 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 almost work for something and making them better. There are 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 refineman 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 COATI’s chemical space mapping somewhere, but goodness knows. The key then is to be able to do your own measurement to get a starting toehold where there wasn’t data before. As I talked about, AI always needs data. I don’t think it’s any surprise that AI’s first impact on small molecule design has been 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. We work on a different approach, which is to 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, very, very broadly across chemical space, 75 million plus molecules, but we’re obviously chemical space, infinite. We’re very sparsely sampling, looking for a starting point, 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. 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 enrich the models with a lot of local knowledge around the area where we do know now that there’s an answer in there.
That sequential build lets 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 Medcam team where they’re able to ask it questions as they go about their work and think about, “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 nothing more exciting than finding a totally new answer to a intractable science and health problem. I think our approach really gets it done.
Chris: 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 on a regular basis. It’s impressive how that’s been able to occur with all the work that you’ve done. I have two more questions for you, both are more on the business side and the business philosophy. Now that it’s three years in, I would say you’re an experienced founder.
Jacob: Three years since the last podcast.
Chris: That’s true.
Jacob: Six plus total. I’m a deep expert now.
Chris: That’s right. You’re a very experienced founder. I mean, you’ve scaled the company a bunch since the last time we talked. I want to hit two things. On the business side, Terray’s always been about the partnerships as well as the pipeline. 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.
Jacob: This also goes to the platform build question. I was influenced by an article I read 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 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. The inverse of that, as we certainly know, that one singular bet actually is odds on to lose.
As you know, I’m a baseball fan and so I talk about this a lot is 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 boss, 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. 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 resource all of our own programs, but that just hasn’t worked out yet.
Chris: Someday.
Jacob: 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 to do the partnership, but the fact that they’re going to then carry the backend development of those molecules through the clinic and out to patients. We have an opportunity to realize value where we otherwise wouldn’t reach patients. The other piece is that it also realizes expertise. Internally, we were fully on immunology and autoimmune disorders, but with our partners, we touch a variety of other therapeutic areas that would’ve been a whole another build for the company to move into those types of indications.
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 coins from very negatively weighted in biotech, to an 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’d be incredible. That’d be the biggest home run ever. You might not get to do the last five if you only have the first two bets. 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 at the programs we do with them. So yeah, it’s always been both for us.
Chris: That’s really well put. Finally, I want to ask you about one of my favorite parts of Terray, which is the very unique and extremely high-performance culture you’ve built, and you’ve set an incredibly high bar just to get a job at Terray. I can think of one time, maybe in the last four years since I’ve been deeply involved, it is actually, I guess, closer to five now, since I’ve been deeply involved in the company when we’ve lost anybody, or we’ve lost someone, we’re even like, “Oh man, that was really terrible that we lost that person.” How have you done that?
Jacob: 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 in the investing ecosystem too. It’s really remarkable to me how mission-aligned everybody involved with Terray is. I’ve always been, as you probably can feel through this, super mission-driven, I’m here to make the world a better place and this is how I want to do it. I think that shines through as we hire people and build the team. It’s 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 four times smaller, and we’ve been through that growth and maintained, as you noted, the quality people we want, intensity.
It’ll sound cliche, but it’s because we hire for the person and the culture and the way we work together, not necessarily just for the skillset, 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 going to take if you hold to both bars. There are times when that’s really tough, and it’s like, we really need somebody, but overall, we’re always happier and more successful when we get both. We’ve built an interview for that since the beginning. As we’ve 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. 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 or 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. One of the new employees was there, and I was like, “Oh, how’d you find Terray?” They’re like, “Oh, well, my friend who used to work here. She left for a school opportunity.” That was awesome for her. Was like, “You got to work at Terray. 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 teamwork together. I don’t know what the secret is except not making compromises on that aspect. There’s never anybody who’s good enough that you’re willing to compromise how you want to do it.
Chris: Well, I can’t think of a better place to 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.
Jacob: 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. It means a lot to me and 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.
Chris: Thank you.