In this week’s Founded and Funded, Madrona Partner, Chris Picardo, sits down with Terray Therapeutics founders (and brothers), Jacob and Eli Berlin as well as Terray’s lead data scientist, Narbe Mardirossian to talk about the power of bringing together transformational wet lab processes with ML and AI to speed drug development. Terray announced their $60 million Series A which Madrona led (and Chris wrote about here) in February of 2022. Terray Therapeutics brings together novel methods of creating vast amounts of data around small molecule disease targets, and then applies ML and AI to map the interactions between these molecules and the causes of disease. This is a company at the intersection of innovations between life and computer science and this was a great conversation. You can listen below or on any of the podcast platforms.
This transcript was automatically generated and edited for clarity.
Erika Shaffer: Welcome to Founded and Funded I’m Erika Shaffer from Madrona Venture Group. On this week’s Founded and Funded, Madrona Partner, Chris Picardo sits down with the team leading Terray Therapeutics. That is CEO and founder, Jacob Berlin, CFO, COO and founder, Eli Berlin, and Head of Computational and Data Sciences, Narbe Mardirossian.
Madrona first invested in Terray right before the pandemic hit and all business, especially wet labs, shut down. And we are excited to lead their 60 million Series A. Terray Therapeutics brings together novel methods of creating vast amounts of data around small molecule disease targets, and then applies ML and AI to map the interactions between these molecules and the causes of disease.
Based on research from founder, Dr. Jacob Berlin, the company was formed by brothers, Eli and Jacob to bring an interdisciplinary team, that works together to bring life saving new drugs to patients in need. This is one of Madrona’s intersections of innovation companies. And without further ado here is their conversation.
Chris Picardo: We’re thrilled to have the Terray Therapeutics team on the podcast today and also super excited to lead their Series A, having been a big participant in the seed, which all got announced recently and super excited to chat with Jacob, Eli, and Narbe on all things Terray Therapeutics and combining the wet lab with really cutting edge machine learning. So, before we jump into it, I’ll kick it over to Jacob, Eli, and Narbe to quickly introduce themselves.
Jacob Berlin: Thanks so much, Chris, it’s wonderful to be here today. It’s wonderful to be working with you and the team at Madrona and to be building a fabulous company here together. I’m Jacob, I’m the CEO and co-founder here. My background is all in science and chemistry. I started that all the way back in college at Harvard making small molecules and looking for applications to them, how we could make better drugs.
At Caltech, I also worked again on making new molecules. After a postdoc at MIT, I had done a tremendous amount of design and development of molecules, but also recognized that the way we were doing it as a field was slow and pretty hard. We’ll hit those themes a lot today.
I went to do a second postdoc at Rice University. Down there in Houston, I worked on nano materials and trying to ultra-miniaturized things and dramatically speed up how fast we could do things. That’s really where Terray started to come from, I really built it out here at City of Hope, where I was a professor for eight years. My lab there worked at the intersection of nanomaterials and synthetic chemistry. It’s there that we began the process of building the technology that became Terray. Over six and a half years, we built it before spinning out the company and I’m excited to tell you and the listeners all about it throughout the rest of this podcast.
When we launched the company in late 2018 and raised the seed round in early 2019, I left my tenured job to be here full time. Since then, it’s been a tremendous time with an amazing growth throughout the company. We’re seeing fantastic science and outcomes and excited to talk about it today and wonderful to be here.
I’ll turn it over to Narbe and we’ll close with Eli.
Narbe Mardirossian: Hey Chris, it’s great to be here as well. I’m excited to talk to your listeners. My name is Narbe Mardirossian, I’m Head of Computational and Data Sciences, here at Terray. My background is in machine learning and quantum mechanics, quantum chemistry, that’s what I got my PhD in. After I got my PhD, I moved to Amgen working in the therapeutics discovery organization and small molecule discovery on the computational side, of course, working on physics-based models, machine learning models, and moving all of our on-prem compute to the cloud. So I moved here in November of 2020 and have been here ever since.
Eli Berlin: Hey, I’m Eli, the Chief Financial and Operating Officer here at Terray. My background is all in finance. I did 10 years of private equity and growth equity before joining Jacob to co-found this business back in 2018 and I’m super excited to spend the time with you today, Chris. So thanks for doing this.
Chris Picardo: That’s great to have all you guys on, and I’d say it’s been a pretty awesome time already working together. Narbe remember when we hired you and how thrilled everyone was. So that’s exciting now to all get together. Before we dive in, I think it’s just interesting to note that, Madrona first invested into Terray right before COVID hit.
And I remember that round closed and then the world went into lockdown for two years, in some cases it still is. So it’s been a great time working together and I know you guys have had a lot of creative solutions of how to work through COVID and we to hit a bunch of those today.
It’s been amazing to see the progress you’ve made even with a bunch of unforeseen headwinds from the world in the way. So it’s been fun to be along the journey. Before we jump into the detail, I think it’ll be fun, Jacob, just to talk about how you decided to form a company around the academic research.
You’ve been at City of Hope for awhile and you decided to formerly spin it out and bring Eli into the fold. I’d love to know the motivations behind that.
Jacob Berlin: Thanks, Chris. It has been a wonderful journey and it’s always an interesting question. When is something ready to be commercial? Personally I’ve always been really fascinated and driven to have my work have impact on people in their everyday lives. I’ve been fortunate, as I mentioned in my background, that I participated in the development of a catalyst that is used across the world and saw compounds that I made in my first post-doc become part of preclinical drug development, and then had a lot of my work in my second post translate into start ups. So I’ve always had an eye on having my work have a real impact. I think the way to deliver solutions to make people’s lives better is through commercialization.
This is a project that honestly on day one, when we wrote it on the proverbial napkin, we were like, “Wow. This one is obviously a company someday. We’re building a technology that allows us to screen hundreds of millions of molecules in minutes and record their interactions with the causes of disease. Of course this is a drug development technology. That’s what we should use it for. That’s where it goes. This obviously doesn’t belong in academia because academia is not the place to develop a bunch of proprietary IP protected secret items that have to be developed, commercialized, scaled up, manufactured and sold.”
So from day one, I called Eli and was like, “man, this idea is so cool. This is going to be a company.” Eli is my closest friend and also harshest critic. He was like, “that sounds fantastic, Jacob. But does it work?” And I was like, “nah, it doesn’t even exist, but it’s a really cool napkin and we’re going to get after it.” And so my other co-founder Kathleen, who is still leads a lot of our R and D and development here today got going on it. We started working on it at City of Hope. And, as I told Eli, and I’ll tell everyone, science is hard and it takes a lot to make the machine work that we run today.
We spent six and a half years painstakingly developing all of the technology for it. Honestly, all along the way, I’d call Eli to talk about something and he’d be like, “does it work yet?” and year one, it was like “a little bit” year two is “yeah, most of it is looking pretty good.” Year three is “yeah, we’re starting to apply it” and by year six and a half, it was, “yeah, it totally does. We’re ready to roll. it’s reading out your interesting solutions to problems in academia. It’s ready to go provide solutions in the commercial space.” At that moment, we put our heads together and we launched the company, and we haven’t looked back. I think it’s been a fabulous home for the technology.
We’ve scaled tremendously. We’re using it to develop medicines to treat immunology disorders and bring therapeutic benefit to patients. I’ll kick it over to Eli for his side of that journey, but that’s really how it went from idea to company.
Chris Picardo: Yeah. Eli, before you jump in, I’m interested that you and Jacob are brothers. Obviously being the harshest critic is a nice natural role to inhabit. Then, at some point you guys decided to get together and actually build the company and backgrounds are a little bit different from what you’ve done professionally.
So it would be fun just for everyone to hear your perspective on the story and what has been like building the company together with your brother.
Eli Berlin: Absolutely! Look, for me, Terray is an exercise in, if you can’t beat them, join them. I did a decade of private equity and investment banking before co-founding Terray as I mentioned and the sort of short story here is that Jacob’s the smartest person I’ve ever met, and he’s disarming in his humility, and he rarely shows excitement for any of his achievements.
There’s this one time Jacob was in high school. So, I was in the middle school, and he tells my parents that there’s an award ceremony at school that we should go to. So we go, and he’s in like shorts and a t-shirt and it turns out it’s the Year End Academic Awards. And Jacob wins legitimately every single award that is given that night.
And, at one point, they tell him to stop going up to the podium and back to his seat. And, he had presented it to the family, like whatever, no big deal, I would have been like, I would have probably sent out invitations to my extended family. And, so for Jacob to show excitement about something is a really high bar and Terray, as you mentioned, was totally different from the start.
He was excited about it from day one. He used to say to me, we should make this a business. It took them a really long time to get it to a point of maturation where we could make it a business. I just always felt that Terray was an extraordinarily rare opportunity. If you believe that, you live and you die, this was one worth doing.
I believe that Terray deserved to exist as a commercial venture. And that for all the hard work in academia by the founding team and for all the promise, it deserved a chance. I’m convinced now four years in that the opportunity ahead of us is really extraordinary to transform drug discovery at an incredible scale.
So I think, it’s been there just aren’t that many opportunities in your life where you can work on a business with purpose where the end goal is advancing human health, do it with your brother and do it with novel technology. And so it’s been an extraordinary journey. As regards working with Jacob, there’s no, I just don’t think there’s any way to replicate the durable trust that we have being brothers.
It just drives us extraordinary efficiency. He talked about me being his harshest critic. That’s probably true. And I think the level of direct and honest communication that we can have because of that trust is totally unique. I also think, I’m not sure anybody listening to this would know, but we are very different people and have very complimentary skills and very separate responsibilities here at Terray.
That’s worked really nicely with a ton of mutual respect. And look, he’s my brother. So I know that before we go pitch investors in the morning, he should have breakfast and we make sure that’s on the calendar. So you know it runs the gamut.
Jacob Berlin: We are different. If Eli had won those awards in middle school, he probably would have rolled into the awards in a three-piece suit. But everything Eli has said right there is true. The durable trust, ability to build it together and work in complementary areas has tremendously accelerated the growth of Terray. We’re super excited to be here.
Eli Berlin: That’s just for listeners. That was like Jacob’s funniest joke ever. I’m also much funnier as the younger brother. And Chris, to your question, I don’t know, we’ve learned a lot over the years, but I think for me, this has been about confirming the thesis.
Jacob’s really, truly brilliant. He’s a tremendous listener. He’s a great strategic thinker. Terray is really novel and working together with purpose, is just a once in a lifetime opportunity. I can’t say enough good things about the opportunity to be on this journey.
Chris Picardo: I’ll say just for myself, it’s been fun working with everyone. I can second all of those things that you guys have both said minus the stories from high school. I want to ask you Eli one more thing before we jump into the platform, because I do want to talk a lot about the platform and the Teray difference and how we approach the world.
But I think one thing that’s really interesting for everybody in this kind of hybrid space between tech and software and traditional wetlab as teams are coming together, how do you join a company, or in your case, build a company where you might be the one with significantly less of the technical background. It obviously helps to have a brother who’s a world-class scientist and can talk to you about chemistry, but you and I have talked about this a bunch and I’ve seen your learning curve. I’ve had my own of right trying to just ramp up. How do you think about approaching that?
We talked to lots of more, less science-oriented people who’d love to go join a company like Terray, and they’re worried about the science side of it. So, what was your approach to moving up the learning journey there?
Eli Berlin: Yeah, I think there are two answers. I think it’s really hard to join a scientific organization from the outside without a scientific background if you’re trying to diligence the science, right? If you’d asked me four years ago, when we started this. How good was the science on a relative basis?
I would have said I spoke to a few folks, gray haired folks who spent a long time in drug discovery and development. They were excited about it, but I couldn’t underwrite the technology. I think that was a benefit because it gave me the leap of faith that, I just trust that Jacob is brilliant, the science is novel, the IP is there.
And so I do think that’s a hurdle. How do you get around, underwriting the technology as you’re thinking about joining a company? And what I would say is, I think you can learn a lot talking to a handful of folks in industry and get yourself over the hump. I think the second piece of it is, at its core, Chris, the technology is extraordinarily complicated.
When you get down into the technical weeds, which you’ve suffered through in a few board meetings, it is deeply complex, but you don’t need to know that. And I think that’s true of any Company like ours, you don’t need to know much about those details. You need to know about what it can do, the why in the technology. And I think it’s been a steep learning curve, but one that I’ve been being grateful for where. I understand the differentiation. I understand the competitive positioning and I understand the opportunity that’s in front of us. And none of that relies on figuring out the linker strategy for our core platform. That all is just transferable from one industry to the next.
For folks who are first and foremost technologists thinking about crossing over into a biotech business. I think it’s an extremely exciting opportunity. I think it’s one where you can work with purpose to advance human health, which is totally unique and gives every day new meaning.
I think there just shouldn’t be that hurdle, because I think it’s all imminently learnable, even if you don’t have full command of every one of the details.
Chris Picardo: Yeah, that resonates a lot with me personally, certainly on my own learning journey. I’ve felt that in board meetings, as we dive into the details. I think everything you said there is relevant for thinking about these opportunities. It’s also a perfect segue into kind of back to Jacob and Narbe talking about Terray’s approach itself. I think that the first question I want to ask there is, Jacob, when you think about small molecule drug discovery as it’s been traditionally done, give us the 30 second version of that, and then the slightly longer version of why and how Terray really is changing the game on the core platform.
Jacob Berlin: Yeah, thanks, Chris. I think we’ll spare the listener today the full deep dive into the specific chemical reactions and linkers and the chips and all the various technical pieces that make our whole enterprise run. But it is, I think, really important to spend a moment and think through the current canonical, small molecule drug discovery.
Just for context, when we talk about small molecules, we’re really talking about medicines that can be put in pills, taken orally and are convenient. They’re the bulk of what people take as medicines today. When we use the term, we’re separating them from antibody therapies or things like that. But for small molecules, the first problem is always (and, really, for any drug discovery the first problem is) figuring out the problem. So the biologists work really diligently to find the cause of disease.
What could be a protein or an RNA molecule that plays a role in developing this disease or perpetuating this diseas? We call that ‘target ID’, and it’s a target because it’s the thing we want to then go solve and fix. So at that point, you go into the drug development process where you need to now start to find your starting points.
We call these ‘hits’, which are molecules that do something of what you want to do with that target. So your end goal may be to say fully turn that cause of disease off, not change anything else in the body. An example of this end goal might be a drug with very nice safety thresholds that you can take a big pill of. You can, in some ways, think of those as like antibiotics. We all are familiar with taking those giant pills. They kill the antibiotic, the bugs, they don’t hurt you. You get better and feel great.
The question is, how do you get there? And so we get there by starting with these starting points called ‘hits’, and they typically do one of the things you want to do.
They either interact with that cause of disease very strongly. So they go and stick to it. We call it binding. Or they impact some of its behavior, but they may be lacking in some other property that ultimately you need to be able to put it in a pill and take it orally and have it work. Then you hit the phase of drug development we call ‘hit to lead’, where we’re taking these starting points and we’re turning them all the way into basically nearly candidates for giving to people. So we’re optimizing all the other aspects the molecule needs to do. It needs to be able to be taken by mouth. It needs to dissolve, it needs to go into the bloodstream. It needs to get where it needs to go into the cell where it needs to go. It needs to interact with the target where it gets there. There all these layers that you work through as a drug development company. Then ultimately the last stage is of course, ready for clinical testing, which involves manufacturing, reproducibility, toxicity testing, and a larger scale and trying it out in humans.
Today that process is typically quite long. These are often, ten-year plus development timelines, and it’s also really hard. We’ve done great as a human kind improving human health and developing therapies, but there are also thousands and thousands of causes of disease that we don’t know how to fix.
Also countless failures along that road, where only a small fraction of what we try to test on people actually works. There’s huge opportunity to deliver better solutions faster.
Chris Picardo: So that’s the traditional approach, Jacob, and you laid out the timeline and how long it takes. I’d be curious to hear how you reformulated this problem at Terray and how we’re thinking about solving drug discovery in a totally novel way.
Jacob Berlin: We sit at like an amazing moment in time with the revolution in the biology capabilities and the ability to discover these causes of disease. So things you may have seen or heard like Crispr, siRNA and Gene Knockout have just continued to unveil opportunities to create drugs, but the chemistry side hasn’t kept up.
There’s not an ability to look at enough molecules and diverse enough molecules, fast enough to really address that explosion of opportunities and bring those timelines down. And ultimately be able to go from discovery to therapeutic in a much shorter and much more reproducible fashion. That’s really at its core Terray’s bet.
We’ve built technology that lets us actually measure far more molecules than any other technology out there. We can actually go and build the loosely drawn map of chemical space quite quickly at enormous scale. Then I’ll kick it over to Narbe, because the next piece is that even with all of that throughput, even mapping and measuring hundreds of millions of compounds, there’s still an infinite amount of chemistry to go through.
So, filling in more of it and knowing where to go next and taking an efficient route through this infinite space to find the solutions you need, the proverbial needle in the haystack, we turn to AI and ML tools and computational tools that are the right fit for the scale of data we work with, to allow us to make the next set of compounds that go iteratively back and forth from large-scale chemical measurement to computational prediction and back to the wetlab measurement of that.
That’s what lets us really compress the timeline and deliver therapeutics where otherwise, we haven’t historically. So I’ll turn to Narbe to say a few words about that complimentary side, where we used the design to accelerate the wet lab throughput.
Chris Picardo: This is such an interesting point. You’ve told me before drug development, isn’t just an algorithm problem. It’s a data problem. So talk about what you mean by that and how that is actually executed on a day-to-day basis.
Narbe Mardirossian: Drug discovery is both an algorithm problem, and also a data problem. But I think first and foremost, it’s a data problem and probably the best way to motivate this is that, big pharma sits on tremendous sizes of data sets. But what you really need in drug discovery is the iterative capability which Terray has built and is expanding.
I think, a lot of these models that are very novel today, neural networks and beyond like they only work when they have the proper data, proper amount of data, proper amount of clean data to seed it, to feed it. Historically, it doesn’t really matter if you use traditional models such as partial least squares or random forest.
Like all of these are going to perform probably equivalently on datasets of the size of ten or a hundred or a thousand, which is really what you’re looking at for a specific target that you’re trying to drug therapeutically. The promise of Terray is really the ability to generate data quickly, iteratively, and at scale and at the quality that’s needed to power regressors.
That can truly optimize chemical space and to take a step back and hit on why this is such a tremendous problem. It’s truly a needle in a haystack; the size of chemical space is something like 1040 or 1060. It is a tremendously large space that you’re trying to explore. You really do need the help of these modern machine learning approaches, plus the data that’s fed into them to traverse that space efficiently. If you look at the traditional process in big pharma, it’s always going to be iterative, but you’re generating about 10 to 20 data points a week.
That’s just not enough to be able to power these models that are improving on a daily basis, and you’re improving because the data is being fed into it. So I think it’s certainly both, you need the data. Only with the proper amount high-quality data, will you be able to unleash the algorithms that are present in probably all aspects of our lives today.
Jacob Berlin: Yeah, I think Narbe is spot on there and we’ve seen this opportunity unfolded across so many other industries that I think everyone listening is probably familiar with. What you should get recommended to shop for on the internet, where are you going to go to dinner, and how to get there. What you’re going to watch a Netflix. All of those algorithms probably started out not that accurate, but they all drew on iterative data sources that are enormous, millions and millions of people clicking on things or driving places or buying things or watching things.
So we have that opportunity in drug development as well, if we can provide the data sets. Drug development is an even harder problem than all of those probably. The key is to be able to be a little bit or even a lot bit wrong the first time, but have a dataset then that gets you going back on track fast and to get better each and every round and run those rounds fast enough.
So that’s really where the compression in development is. It’s where the Terray differentiation is that we build and measure large enough that we can get our algorithm going, get the model going, and then rapidly refine it to really incredible accuracy and precision. And that’s the Terray difference.
Chris Picardo: Yeah, this is a point that I’d love to go one layer deeper on, which is that I think another way to frame that is in these broader machine learning problems, Terray closes the loop on the data side. Not only do we create a ton of data, we then test that and model that, and then we can validate it again with actual physical chemical data.
And I go back to Narbe for a second. When you think about that versus maybe the pure algorithmic approaches are purely in silico approaches out there, I looked at, get your perspective on the importance of being able to close the loops on the models and move as quickly as we can on the data side.
Narbe Mardirossian: Yeah, closing the loop is absolutely essential. Honestly it would be any computational chemists or machine learning scientists dream to be able to develop these models, make predictions, and actually see those predictions tested in real life. That is something that’s different from the traditional process, because, you only have so many shots on goal a week and you need to make, 10 or 20 compounds and you don’t really have that opportunity to make millions of compounds literally in a week and test them.
I think absolutely one of the benefits of Terray is the ability to iteratively benchmark and improve the models that we’re building. I’m not talking about only about machine learning models. Machine learning models are great for learning from experimental data, but even physics based models that are very popular these days in computational chemistry and other realms, these can also be improved by learning about how the predictions are right and wrong. So the ability to have this feedback loop is absolutely essential. Truly, I believe that Terray is probably one of the only places where you can actually test and hypothesize and, validate your hypotheses iteratively within weeks or even days.
Chris Picardo: Yeah, the power of the platform is pretty immense. I think one thing I wanted to ask too, and this goes back to the question I asked Eli earlier, say I’m a computational scientist or machine learning engineer, and I’m really curious about either these types of problems or joining a company like Terray.
What makes this data so special, and why would I get so excited about working at Terray and building the models that we’re building?
Narbe Mardirossian: Yeah, I think Terray is an exciting place for a variety of scientists and people with technical backgrounds. I guess, let me start with computational chemists and machine learning engineers — one, I would say the molecular data, we have at Terray both in terms of the quality and the scale is unparalleled.
Nowhere will you find datasets of size 10 million, hundred million. Where you actually have high quality, believable data that you can model. I’d say from, in those disciplines, the ability to model data and use the feedback to improve algorithms consistently, whether they be machine learning based or physics based is it doesn’t exist anywhere else, but, molecular data, isn’t the only type of data that Terray has.
We have tons of opportunities for data scientists. All of our readouts for molecules come essentially from images. Just the path from going from raw images to processed photometries to the output that is then used for hit discovery, and machine learning models and compchem, is full of custom algorithms that we’re developing every day at Terray.
And I think, beyond that for data engineers and software engineers, the amount of data we generate is tremendous. This year we’re gearing up to hit 20 to 30 petabytes of raw image data, and that doesn’t even include the processed data. So there’s tons of opportunities, whether from the domain, domain specific fields, such as compchem or machine learning, all the way to data engineers and software engineers that Terray offers.
Chris Picardo: Yeah, we talk a lot in Madrona about the combination of machine learning and life science and the wet lab. And I think what’s amazing about Terray is not just that you guys have actually built that and are running it on a daily basis. It’s that pretty much inside to raise. You’ve talked about Narbe.
Absolutely. Incredible data science software in engine engineering, machine learning challenge going on daily, that itself could be right, like a data science focused company. I think when we talk about integrating those two it’s pretty awesome to see how Terray like really fully, is as a data science and wet lab company and that you can’t really pull the two pieces apart.
Narbe Mardirossian: Yeah, absolutely. I just want to add that one of the, to me one of the beautiful parts of Terray is the fact that the wet lab and the computational side are fully integrated. That is also not something you see very frequently in in drug discovery companies. Typically the computational team is viewed as like a support function where they’re contributing maybe 10-15% to various requests or projects. But here, without the computational side, the wet lab would not be able to function and vice versa. So I think this 50/50 integration is truly what makes Terray an exciting place to work for both computational people and wet lab people.
Jacob Berlin: This one will probably make the listeners chuckle if they are in the field at all. We built this from the wet lab side, initially. We wanted to see if the technology could be built and we could make the core of our technology, which is these little chips, the size of a nickel – the world’s most ultra-dense microarray. If we can make them and we can put the compounds on them and we can measure these interactions, which is where our raw data comes from, as Narbe said, it allows us to measure hundreds of millions of compounds.
In the academic lab, that’s where we started and we wanted to see if we could get the chemistry to work. Could we get the microscope to work? Could we get the chips to work? Can we get all the parts of this interdisciplinary process to work? The first time we made it work, we had no data people working with us at all. We were like, oh man, we just measured like a hundred million things, what should we do with that? Maybe we’ll put it in a Excel and filter for the top hundred and then see what we can do with that. Then the next day we went out and looked for someone to help us on the data side.
Now, of course, there’s been many years of working at the intersection of data science and experimentation. It’s staggering, and it’s a cliche that’s true. Working with an interdisciplinary team makes all the difference. We see stuff go back and forth all the time where the data team makes predictions out of the data or identifies things in the data that changed the way we do the wet lab side and vice versa.
Terray wouldn’t run the way it does without the data team, the chemistry team, the biology team, the automation team, the production team. All basically sitting together and talking together each and every single day, and it’s what makes it so special here.
Chris Picardo: That’s awesome. I get to witness it pretty regularly and have been down to Pasadena many times and seen the lab myself. It’s pretty great to just see it in person and see it all come together.
Eli, why don’t you also briefly tell us about the Series A and our big fundraising milestone that we just achieved and who’s been part of that journey.
Eli Berlin: Yeah, I appreciate it, Chris. So we’re super excited. We just announced our $60 million Series A which brings our equity capital raised to date to just over $80 million, including our seed financing. One of the things that’s been tremendous about this journey has been the partners we’ve had in the venture community.
That includes you guys at Madrona. It includes Two Sigma Ventures, Digitalis Ventures, KdT Ventures, Goldcrest Capital, XTX Ventures, Sahsen Ventures, Greentrail Capital, and the folks at Alexandria. As well as a whole host of other folks who’ve supported us along the way. We’re super excited about this moment and the opportunity been supported by the Capitol to massively parallelize our processes and throughput to deliver for patients in need.
Chris Picardo: I want to throw it back now, as we’re starting to wrap up to a couple of broader questions that I’ll pose to everyone, so feel free to jump in and take them as you see fit.
I think the first one is, and this is one that will probably resonate with most people listening, building companies it’s really hard. I think building companies that have complexity on both sides, the wet lab side and the machine learning side and trying to do both of those things is potentially even harder or at least more complicated.
What’s been the biggest challenge so far the biggest set of challenges, maybe Eli and Jacob that you guys have faced, and how have you thought about those?
Jacob Berlin: Chris, that’s always the question that keeps you up at night and everyone asks you what’s the hardest either retrospective or what’s coming next, that’s the hardest. We think about it a lot. I spend a lot of time on it, I don’t know if the answers will be exciting, cause they’re probably the same ones thematically that everyone who starts a business that builds at this interface faces, which is hiring the team. Building the expertise around the table, just always takes a lot. It’s always a tremendous lift to find people who are mission aligned, vision aligned and passionate and, perform at an excellent level.
We’ve been really lucky now to build a team of 50 with a seasoned management team with biotech expertise, as well as ML computational experience. We’re joined by a wealth of expertise now on the business development side, the drug development side, the computation side, but it took a lot to bring that team together and be at this remarkable moment.
I think alongside that, personally going from academia, or I guess it could come from anyone. Back to the napkin sketch; the appreciation for what scaling and industrializing that discovery is like. It is, I think, harder than you would guess on day one, to be able to run the exact same process and a high number of replicates at incredible velocity and scale and know it’s right every single time. We’ve done that, but it took a number of years to really dial that all in. And so I don’t think that part should ever be underappreciated. Eli, what would you say?
Eli Berlin: It’s funny, Chris your comment really resonates. It is so hard to build a business. I think back to my days in Private Equity, where, I’d come into the board meeting and have a bunch of thoughts on what needs to get done and I used to walk out of the meeting and go back to San Francisco and it’s so hard. Execution is so hard, but it’s also got enormous joy to it, because you get to work with people day in and day out, you get to create work that is worth doing, and it’s all worth it in the end.
I think for me, the two are recruiting and for us, the recruiting piece is about, attracting candidates and helping separate signal from noise. There’s so many AI drug discovery companies out there, and we’re really different, right? If you believe that the data unlocks the opportunity, we’re the only ones with that capability in the whole landscape. It’s a competitive ecosystem out there, and it takes a lot to recruit folks and get them interested in our technology. We’ve been quiet up until a few days ago. And we have a lot of teaching to do, when we meet folks who are interested in Terray.
The second piece is, Terray is a massively interdisciplinary Company so we’ve got chemistry, biology, machine learning, and computational chemistry with robotics and automation that go to make the engine deliver for us and ensuring cross-functional communication and collaboration is done with excellence and precision to deliver is really hard. It’s taken a lot of work to get us to where we are, and we’ve got a lot of work to go from here, and those are the two for me.
Chris Picardo: That all resonates with me. I t’s been fun to watch you guys solve those challenges and we’ve been along for the journey for part of the way, and we’ll continue to be along for the rest of the way. It will be good to keep working through these together and I’ll go right to my last question.
I like to ask the couple of people that I’ve done these podcasts with this question. If you roll the clock forward 10 years and you’re looking at what we’ve achieved, at Terray, what does the big vision look like? What is success, and what will that look like when Terray is at scale and started to execute on a bunch of this stuff that you guys set out to do?
Jacob Berlin: Yeah, I picked my career, Chris, because the big vision is making people’s lives better. It’s allowing everyone to live healthier, enjoy more. For us, what does it look like for Terray? It means Terray is a drug development company at scale, working across multiple different types of diseases and delivering therapeutics to patients faster.
It is unlocking all of the opportunity in that biology revolution with a chemistry revolution, where we can really go from identifying causes of disease, to people enjoying medicines that make them better reproducibly, reliably, and quickly, so that’s what we’re building.
Eli Berlin: That resonates. I think about the opportunity to build a company is a tremendous opportunity, but the opportunity to build a company where the end result, if we’re successful, and when we’re successful is more therapies to patients in need, faster. It’s an extraordinary vision to be a part of.
It really resonates across the company. Everybody who works at Terray does so with purpose and with mission as their number one. It’s an opportunity to work with world-class science and, deliver the next generation of therapies to patients in need. It’s really a unique opportunity and a tremendous goal as we push everything forward here over the next handful of years.
Chris Picardo: I don’t think I could end our conversation on a better note than that. So I wanted to say that, for us at Madrona, it’s been really amazing to be part of the journey and we’re super excited to continue to be part of the journey. I know it’s a busy time, so I appreciate you guys taking the time to chat with me today and share about Terray for really one of the first times ever. This has been a real pleasure.
Jacob Berlin: It’s a delight, Chris. There are two things along the journey that really make it wonderful. One is the science and seeing what we can achieve and move human knowledge forward. And the second is the people. And so we’re, privileged to work with the people we work with here, you, and the rest of Madrona ecosystem supporting us and the rest of our investor ecosystem. We just want to thank you again for having us today and delighted to tell everyone about Terray.
Erika Shaffer: Thanks for joining us for Founded and Funded. If you want to learn more about Terray, they can be found on the web at www.terraytx.com. So that is, T E R R A Y T X.com. Thanks so much for joining us and tune in, in a couple of weeks for another episode of Founded and Funded.