The Bioinformatics CRO Podcast

Transcript of Episode 86: Deniz Kavi

Disclaimer: Transcripts are automated and may contain errors.

Grant Belgard: Welcome to the Bioinformatics CRO Podcast. Today, I’m joined by Deniz Kavi, co-founder and CEO of Tamarind Bio. Tamarind is building software that helps scientists access computational biology tools at scale through a web platform and API across areas like protein design, structure prediction, and docking.

Grant Belgard: We’ll talk about what Deniz is building now, how he got there, and what advice he has for people trying to build at the intersection of biology software and AI. Welcome.

Deniz Kavi: Thanks for having me

Grant Belgard: Thanks for coming on. So tell us about Tamarind

Deniz Kavi: Yeah. Tamarind as you described, basically a aggregator of all the leading molecular design tools. So things like AlphaFold and RF diffusion and many others, we provide in a single place for all scientists to use, basically. So we try to do the work, like the boring, annoying, undifferentiated work is what we try to do our best in.

Deniz Kavi: So yeah, scaling up these tools, applying them to real problems, connecting them into multiple stage pipelines, so on. I got into this problem basically doing this as a human for my colleagues at Stanford. So in my undergrad lab, my job was to get an email, run AlphaFold 10,000 times, email it back to one of my colleagues, and at some point I felt there does not need to be a person doing this.

Deniz Kavi: You can just have a software solution from there. So basically trying to lower the accessibility gap, but also handling the infrastructure, the, software aspect of how you deploy computational tools like molecular design models

Grant Belgard: What are you most excited to build or learn about right now?

Deniz Kavi: Yeah, I think today we spending a lot of time on developer tooling to some extent. I think we have found that sort of– we work mostly with large pharma and biotech companies, and we found that these organizations have a lot of interest in how they do– how their R&D teams deploy AI to their use cases. And, ultimately these have served that demographic and like we are building a lot of tooling for that. So I think most exciting– most of what’s most exciting to me right now is a organization-wide, whatever, one to ten thousand person deployment of molecular AI tools for, serious drug discovery applications, things that will go to clinical trials or become tool proteins, what have you. Yeah, I think serving data scientists and AI folks to then deploy that to the whole organization is something I’m very excited about today.

Grant Belgard: What problem do you most want to solve for scientists?

Deniz Kavi: I think the general genesis of the company has this theme of accessibility. Historically, if you look at how software has worked in our industry, do a– you do a computational chemistry PhD, you’d take a four-month course on this one software product, and you become an expert on it, and then you are the person tasked with running all these workflows for your colleagues. I think we think there’s a spectacular amount of inefficiency in sort of the handoff in information or in convincing your computational colleague to do this work for you, like all the lobbying internal work. I think our general case is that of the increased capabilities of AI recently, it’ll be available for every scientist and not just specialists in, 20, 30-person data science teams, but the entire org will be the consumer answer of people applying the AI tools in our day-to-day.

Deniz Kavi: So I think very much the, having every scientist be empowered by computational tools as opposed to a realm of specialists doing it for them

Grant Belgard: What’s the backstory on the name?

Deniz Kavi: Yeah, unfortunately not a very exciting story, but we basically wanted it to be named after a living thing. We went through a list of trees, found tamarind as a, memorable fruit and a notable name, and then went from there

Grant Belgard: Where do you think the real bottleneck sits today? Is it model capabilities, infrastructure, workflow design, adoption, something else?

Deniz Kavi: Yeah, I guess I would be biased to say infrastructure. I think model capability is a credible thing to look at as well. I think the better the individual tools are, the more– the less certain-uncertainty you have, the more reliability you have on the individual models, the sort of more valuable the infrastructure becomes.

Deniz Kavi: Because if you have to tweak a bunch of knobs and find all the errors and evaluate against them, the model itself may not be very useful if you don’t know those exact details. But in our case, we’re seeing the molecular design tools especially become good enough in the space that, you can just point at a problem and apply it there and go from there, basically. Our general view would be having it be available to a scientist. I’m repeating myself over and over. So generally, very much the sort of focus of the company is how do you get somebody with an interesting idea for a target or a, drug program or a way to optimize a lead protein or lead sequence and how do you get them to convert that idea into, a molecule on the computer, basically.

Deniz Kavi: I think broadly the issue is not so much the hypothesis generation or having targets to go after. It’s more so like how do you– Once you have a problem to solve, how do you apply tooling to solve that problem? It’s like how we think about the, one of the gnarly-est problems in the space.

Grant Belgard: What does great software for life scientists look like for you?

Deniz Kavi: Yeah, I think we, from the start, focused on having an opinionated way to apply these tools in the first place. You– if you take a look at the papers of these there are a lot of focus on, like, how the model is trained, how their– how the inputs should look. Our view has always been basically, we built a sort of opinionated, structured way to consume those tools in a serious, drug discovery use case. So for structure prediction, complexes and docking is a big use case. For protein design a lot of papers come out and say just, we are creating a way to create new structures, and these are arbitrary, not really– these are not becoming drugs, these are not becoming antibodies or vaccines or what have you. And we build templates and recipes to make that work more effectively. That’s one avenue, is like you need to codify and build the best practices into your product.

Deniz Kavi: The other side is, I think, integration. So we have been very like the entire product from day one has been usable programmatically.

Deniz Kavi: So our API or MCP or agent offering is basically a way for any arbitrary code to call our product. So I think in a way, we can just go away in the background, just be done as infrastructure, and that just gives you flexibility to consume the tooling in wherever you want it. So it might be in your ELN, your LIMS system in your internal custom applications or your AI agents within Claude, what have you.

Deniz Kavi: I think those two are the main points of you want to build a opinionated, codified way, like a very practical-focused applications of the AI tools. And the other side is the how do you get it to– how do you get a scientist to use you where they want you to use them? That’s the two pillars, I would say.

Grant Belgard: What part of the scientist experience do people outside the field most often miss?

Deniz Kavi: I think amount of, uncertainty in everything you do, basically. Like I think some people think about the process as, I have an idea, I will do an experiment to fail or not fail that idea, and then that’ll be the end of it. And if it works, it’s gonna be a drug and it’s going into clinical trials.

Deniz Kavi: If it doesn’t work, I like toss it. But oftentimes, there’s uncertainty in the actual experiments you do and the computational tools you use. ‘Cause anything you do has some error bars associated with, and you have to believe and persist every time something is like going wrong where it might be a sign of it being interesting. Very rarely something in science is, immediately a useful result or like you, you even get data that like proves it in either direction. So I think that’s a very much something people not think about is you’re not really sure where you’re going when you’re in the middle of a process or like hypothesis in general.

Grant Belgard: What have conversations with users taught you that theory never would have?

Deniz Kavi: I think we don’t talk much about– People talk a lot about like where AI is interesting, where the applications are, but applications like are pretty broadly on what people think about as things that computers can do. I think if you think about the grand idea of a virtual cell, the sort of like a philosophical leaning of a research work there would be you want to create a thing that simulates the entire behavior of a cell in the computer. But realistically, that is like too big of a problem to be applied immediately today. So in some ways, I don’t have– I have a computer science background. I have a sort of, basic science background in, in relevance to that as well, but I’d never worked in a sort of specific R&D org before in a drug discovery context.

Deniz Kavi: And I have learned a lot about what the day-to-day problems are in a specific, in the context of a research program for a drug program what the use cases there are. So that’s things around what assays are most interesting, what sort of you can trust on, what you can trust what the sort of clinical or risks are before going into the later stages.

Deniz Kavi: I think that type of day-to-day experience is very hard to see. Certainly in academia, even if you’re a very strong scientist, you might not know the sort of how the applications go. And there is a meaningful difference in academic science and, commercial drug discovery-focused science, I would say. And, having not worked in that space, talking to– the privilege of talking to like pretty senior folks who manage these R&D programs and learning how they think about those use cases is very valuable for me.

Grant Belgard: How do you decide what to hide behind the interface and what scientists should still control directly?

Deniz Kavi: That’s always a changing structure for us for the most part. I would say people tell us that this point is a big value add for the platform, like maybe whatever tens of thousands of scientists, people will email us when they want a model added. I think we’ve always had a question of complexity versus abstraction, where, we have, say a few hundred person org in a company using us and also like some 30, 50 person data science team using us. And the interests of those groups are not necessarily the same. The scientist wants it to be easy to use. They can come in, use it, and then forget about it, but the data scientist might be, I want to check every single possible option. Generally, we’ve built our product in two distinct sort of ways.

Deniz Kavi: So the API, for example, is you can– you do anything you want programmatically, can write code to build on top of our product. And then nowadays, the API actually serves as a way to like abstract even further. So we found AI agents as a good way to guide the user to, you know… Most of our value from AI agents, like not even necessarily the reasoning, but just the hiding the complexity of how you consume a tool.

Deniz Kavi: That’s the one avenue. And the web interface is also there for the most complex way to Basically, anything you need for a given tool, you can use with another web interface. So we bifurcated our products in that way for different cases. And then generally just being close to the customer, and people tell us when they’re unhappy and, it’s a boring answer, but just people– you be very close to, your check-ins and regular meetings, what have you, where people will tell you what the, what problems they have and how– whether or not we’re able to solve them.

Deniz Kavi: Or if we can solve them, are they able to access that problem or access that solution to that problem are the main ways.

Grant Belgard: What does a trustworthy AI-assisted workflow look like to you?

Deniz Kavi: Yeah, I think generally the– I guess these are two different answers for, what we live in, the molecular domain, and also the sort of general knowledge work reasoning LLM tasks. Would say the molecular side, you just want wet lab data. I think we’ve seen a big explosion of the recent models doing very well in the sort of classical, one binder design or structure prediction problems, and much of that was launched by you can do experiment to see that this model works and like you can see the hit rates, what have you. I think that both added a lot of trust, but also despite not substantial changes in architecture, showed just that the models can be good and you can demonstrate that they’re good in that way. I think there’s still some questions there, like how much you can see from experiments. There’s– obviously, you can’t run a clinical trial to show that your model works every time.

Deniz Kavi: The molecular design side, I would say it’s the, just want to run experiment that can be as close to the use case as possible. Then surprisingly enough, like in some, most of the best performing models, they don’t really tend to correlate with a direct experiment. Like most of them are not simulations of an assay you would do.

Deniz Kavi: Many of them are, but not probably the most successful aren’t. That’s one. For LLMs, I think the

Deniz Kavi: main

Deniz Kavi: question is around, You know, to some extent, the tools LLMs have access to are not particularly strong. I think if you give the tools a bioinformatician uses to an LLM, there’s still some uncertainty and problems around that because of the sort of inaccuracy of the specific tools you’re using or the value of the, the reliability and the accuracy of the tools it’s using in the first place. If you give an LLM a protein design model, you now have two things to worry about, which is a faulty tool using another faulty tool, and you have to make sure that you have to find out which one’s the wrong part there. View for there– for that has been basically being very transparent and clear about what the tools it’s using are looking like and evaluating those results.

Deniz Kavi: Our LLM agents basically call our same– Like, whatever humans would use, the LLM uses that tool, and you can interpret it in the same way. So all the sort of inter– the abstractions we built help there. other questions are how good is the LLM in scientific parsing and understanding what’s going on. at this moment in time, the, Claude Op– Claude 4.6 and as of recording is 5.4, Thinking for GPT are reasonably good at not hallucinating anymore. I think questions tend to be where do you take the result of a paper at face– like what the result you’re citing at face value versus is it applicable?

Deniz Kavi: So let’s say if a model setting use a tool, the only tool it has access to for, let’s say protein solubility prediction, it just says protein solubility predictor, and it does not have any error bars or caveats or any details there. The model has no choice but to just, “I have this. This is the one thing I have.

Deniz Kavi: Let me just call this and give it to my user.” But in fact, maybe refusing to do something or showing its limits is probably a most more valuable thing there. Somewhat long-winded answer, but I should say, and showing uncertainty and then maybe even rejecting tasks when it’s on not confident in its ability to do them are two major points, I would say. And so I think the RLHF efforts on the LLMs are doing this well for other– medical advice or what have you. I think these will apply for science because we’re not really– we’re not trying to adversarially convince the model to do something that’s bad because there’s no benefit to having it do a ineffective in silico experiment, I should say.

Grant Belgard: How do you think about simplicity without flattening the science?

Deniz Kavi: Yeah, definitely a difficult problem. I would say the honest answer is like education and support is the honest or best way to go about this. I think we, we try not to limit capabilities of the tools we serve. To some extent we do, honestly, like for the, again, the, say small molecule design tools, for example. Like so many custom options you could be doing that it becomes untenable to use them for anything in the first place. So we try to build recipes of best, best– most commonly used use cases. I think the, again, the sort of open-ended AI models, like things like, protein design or small molecule discovery or even the LLMs themselves, they have a lot of use cases that can be disc– discretized into buckets of, this is my one use case.

Deniz Kavi: I want to do, docking prediction for small molecules, and I have a big world model that does it for me. I think narrowing those down to different tasks is the current approach people take. I think this is like unfortunate because if you can have a single model do everything, it’s quite, quite a more interesting experience and more interesting results most likely. But for the most part, it’s carving out chunks of the capabilities and giving them as recipes you can use is one, one approach. So you don’t really use– lose the capability there. You just become a bit more constrained into a way of doing things, which can be good if the model is not that good, is the– not that performant is the other detail there.

Deniz Kavi: For the most part, I think focusing on the where the models are good and like you can know that by being close to your customers, but also being close to the results in the first place with a sure review and then carving out those places to be individual tasks you’re looking at are the things we try to do.

Grant Belgard: What things are easiest to overbuild in this space?

Deniz Kavi: I think you can make a lot of analogies to like horizontal software players. So I think in some ways we resemble something like a Databricks or Snowflake quite a bit. We do model inference, training onboarding our custom tools, app builders most of those hold, but those products are also used a lot in the life science industry, and they can’t quite apply to them directly. the over-index on verticalized solutions for the industry. You think of yourself as a very bespoke product. There’s lawyers who do life sciences, there’s, procurement teams who do life sciences. And in fact, some of that might just be if you just take the horizontal version, like up and modify and optimize, that might be a decent way to approach it in the first place. You don’t need to reinvent everything.

Deniz Kavi: You can probably use the, the workflow builder, the pipeline builder for generalized use cases probably works well for the bioinformatics use cases as well. I think we don’t need to overindulge in our specialness and difference from the software industry or whatever the AI applications are.

Deniz Kavi: I think that’s the one thing I see a lot of is, you want to reinvent the tool hosting and say the bio– bioinformatics is basically, computer science, and then you don’t– a lot of the tools there, you can apply to the general use cases. So I think we– both from a people who gets into the industry perspective, like we hire a lot of non-biotech folks on the software side and then also what tools you use to build products are the– they don’t all need to be from scratch.

Deniz Kavi: They can be building on the horizontal solutions.

Grant Belgard: When new tools or models appear, what makes you pay attention?

Deniz Kavi: Yeah, I think the– surprisingly enough, I think the, to some extent, like the social media hive mind is like a decent predictor of like how much interest there is in something. That doesn’t necessarily mean it’s like the most accurate or the most interesting. Like some of our models are probably very, like very well known.

Deniz Kavi: People outside the industry know them, what have you, but they’re like basically not used. I think some level of interest tends to be a sign of in- of value. I think if something just goes out to bioRxiv and nobody talks about it, like it doesn’t tend to be the most valuable. I think that like in that case, like the most obvious thing is immediately valuable. The other side is these days, a lot of validation you can discretize and show for, especially in our use cases, in random molecular design use cases. I think if you show clear objective results there I think we’re doing a decent job of putting out benchmarks. There’s more to be done there for sure as well, like for other use cases. In my mind, it’s basically two, two avenues. One is, it can do something that was never doable before. I can make a fully– a protein completely from scratch and put it out and express in the real world.

Deniz Kavi: That’s meaningful scientific results. The other is it does something that was s- theoretically doable, but it was so much better than that, say 100 times better than that, that it can reliably get there.

Deniz Kavi: So again, going back to the protein design example, we’ve had a renaissance of binder design tools recently, and those are mostly because they’ve had, 100 times the hit rate of the previous generation of tools, and now people can actually use them and, instead of, they don’t need to have a huge V display set up where they spend a million bucks on the assay.

Deniz Kavi: They can just spend, whatever, $10,000 and show the results there. So I think it’s either increased accuracy and then the other side is just a net new use cases like in-invented by this protocol.

Grant Belgard: What trade-offs do you face between frontier capability and everyday reliability?

Deniz Kavi: I think you don’t want to be overambitious in applying tools to a given use case. Theoretically, there’s a model that claims to do everything you need to be doing for a drug discovery program. If you read the Wikipedia or you said the GitHub repo the definitions, and you just did them, you say you gave them to an LLM agent and then told them, “Don’t question these.

Deniz Kavi: These can do exactly what they claim to be doing.” And then probably like it will fail in the first step, but it’ll fail in the second step and the third step and fourth, con– successively. I think there’s a– incentive is always to publish like a decent result, and you just claim a… Maybe the most humble people say towards this task or towards, towards even a Jesse prediction on the computer. You do need to build the muscle of identifying what is there to publish a paper or what is there to show an actually or what shows an interesting result. I think that’s one question we have. Something that might be at the frontier might not be good enough for it to be actually practically useful.

Deniz Kavi: GPT-2 was on the frontier for a while, and it was basically useless for a lot of tasks. And similar things apply for our world, which is basically the incentive is to provide the most audacious result you can. But even– That might be true in relative terms. It might just be, it might be 100 times better than the previous stage, but that might not be enough from– If it never worked and it’s zero is zero.

Deniz Kavi: So just making sure that the individual applications are– I think restraints are, I should say in that note, restraints are a big thing we think a lot about where can a model be applied, what the actual sort of error bars around the results are, how can we codify that into a product where you can understand what the limitations might be. I think it’s just that there’s a lot of things that claim to be the best, and then the best is not good enough for a lot of use cases too.

Grant Belgard: What do outsiders misunderstand about making advanced computational biology tools genuinely usable?

Deniz Kavi: I think the view or the understanding is basically that everybody’s aware of what the protocols they want to use are and like what the best, what the hit rates are, what the– People think about, computational modeling tools as the same as they would think about like assays, where there’s some clearly defined set of worlds.

Deniz Kavi: You can buy a recipe or have a recipe built internally and just apply that, and like it’s, 90% of the time it’s gonna be the same thing, except some modifications you have to make, what have you. I think the– both the world is changing so quickly that like very few scientists are actually on top of what’s the frontier.

Deniz Kavi: I think like we do that role informally by just giving guidance on that and providing educational materials there. For the most part, yeah, the limitations are just, people don’t really know what works as a community, basically. So like I think it’s very much in flux, and that might maybe be stabilized like every couple of weeks and then we go off the rails again. The question basically is: scientists know what they want to do? Do scientists know what works well? And, the criticism of the science community is a sort of general observation that the world is moving too quickly and the incentives are, or incentives are such that everybody claims state-of-the-art for everything, and it’s hard to be on top of what the direct applications are.

Grant Belgard: What first pulled you toward the overlap of software and biology?

Deniz Kavi: Yeah, I actually– so coming into Stanford, I had done some work for doing natural language processing work. I very wrongly predicted that sort of transformers would not be a good application for life sciences. And so I was doing some older text translation and summarization, sentiment analysis type work, and I just assumed, “Oh, these are not gonna be useful tasks,” and, I’m gonna look at another application. And I got into biology from trying to find cool applications of AI tools as I was going into a pre-science degree. And in reaction to that, I got a job in a drug discovery context at a lab at Stanford. Yeah, I think it was very much I was trying to find an interesting application for computer science, which sort of led me to biology.

Deniz Kavi: I’ve heard this from a lot of mathematicians as well, where the modeling of a complex system tends to be an interesting problem as a mathematician. You become a bio-focused mathematician or what have you.

Grant Belgard: Was there an early moment when you saw a gap between what scientists needed and what the tooling actually allowed?

Deniz Kavi: Yeah, I think the most obvious for me personally, the earliest sign was basically that I was hired for a full-time or full-time job doing this for my lab mates. Somebody– I think to this day we see a lot of this as well, where even large pharma– A director of data science at a big pharma company who has 15 years of experience, half their job is just somebody sends them an email and says, “Can you run this in silico workflow for me?”

Deniz Kavi: And then they do it for them, but it like takes a few weeks, what have you. I was basically doing that for my job. So we had a protocol for finding targets for peptide candidates in a, in silico way, and that was interesting for applications of my lab, and that sort of led to some other separate work that was a cell systems publication. And that was out there in the world for people to use, and then nobody in the world could use it, basically. They would have to have me do it for them. And that was the genesis of the company in general, was just solving that problem of, I have an idea to apply this, but I have no idea how to actually, use the tool to apply them to a real use case. And that’s how the company came to be too.

Grant Belgard: What did being close to research environments teach you about how science really gets done?

Deniz Kavi: I think that there’s just, the most part, being close to scientists makes you appreciate both how valuable their work is, like how much how hard they’re working for these use cases. But the other side is, I guess I’ll– going back to my previous point, like a lot of uncertainty around how well do you think your idea will work. The experiment you do might fail, the computational tool you use might fail. There, there might be some, side effect you’ve never known about and, that sort of– you have to embrace that as a researcher. And I think researchers also think about the world in this way. I think it’s more, more importantly than typical day-to-day experience is a very strong skepticism of everything that comes their way.

Deniz Kavi: Like anything that could be interesting or useful, people are, just by training of the job, I think very much skeptical and often negative on these things, which I think is an interesting habit to have, and I think it has its pros and cons. I think the temperamental approach is like what the temperament of a scientist is, or life sciences, I should say, is for what the future holds, quite interesting, where they simultaneously have a job that is much, I’m in the frontier.

Deniz Kavi: I’m trying to invent the next thing, but also I am also very skeptical and generally negative on everything that might be interesting. And you have to fuse these ways to do the process of science basically which is– I thought was quite interesting.

Grant Belgard: What made the founder path feel right for you?

Deniz Kavi: Yeah, I think in our case, we built the first version of Tamarind for my lab as like a side project with my co-founder basically, and gave it to my colleagues. And then I posted on some random forum about, we have this use case, these alpha fold on the web, and web, and that led to several hundred users.

Deniz Kavi: So there was a lot of demand for academic users to– who like felt this in– problem of how they do computational tooling. that sort of felt strong enough as a pool of interest from users that we felt we, we should do this full-time and not spend our times doing this as a side project in school. And I think that was the primary reason. Like for the most part, in my mind, always not really thought about being– Like I always wanted to be a scientist or a researcher, and the approach in my mind was: What is most valuable to the world right now of the things I could be doing? What is the fastest path I could get there? And I also was very much pulled towards this idea of, I can move faster in a quick, quicker way than typical timelines you would take.

Deniz Kavi: Instead of doing a– four-year undergrad and a PhD and another, postdoc and many years of working at a biotech company, I now get to basically do the job of a executive at a relatively serious, bio-software company and just just by having the virtue of having built this thing as opposed to getting approval from everybody else on top of me is the sort of primary motivation.

Deniz Kavi: So combination. We had a thing that was working that we wanted to make it more serious. Just the waiting and the having to play the games of getting, accredited and approved and going better and better over time by some other institution was not very appealing to me. And the other side was basically for us to realize this idea of how we get this to– these tools to be used by scientists more effectively and at more practical use cases. It do- it has to be a company as opposed to a nonprofit or academic research project because the incentives are not strong enough for those use cases or for those methods of applying this problem that we had to make it a company, basically.

Grant Belgard: How did you learn to tell the difference between an interesting technical problem and an important user problem?

Deniz Kavi: Yeah, I think I was somewhat commercially minded which I didn’t realize at the time, but I’m basically a salesperson now, so I’m more aware of it today. I think many interesting technical problems can be commercial or user problems as well. The question is, we’ve been asking for people to pay us from day one, basically, as the one side. I think if you keep it as a free tool that everybody can use, it’s a– have too many nice-to-have type problems. I think you want a investment from the user of their time or their money or their feedback, what have you, where the problem has to be severe enough in their day that they would take some risk, basically, is how we think about that. I think I have a decent way to emulate that in my head today. I can understand what the workflow looks like from just talking to somebody about their day-to-day.

Deniz Kavi: But for the most part, do inference scaling for AI models. The condition for that to be useful for users is that the AI models are useful for them, and we had to validate that for that use case. my finding was, if you are willing to put something of yourself on the table, like risk something of yourself or time or money, what have you, is the main beneficiary of user solutions. And then technical products are often, you know– I think engineers are very much drawn to this case of “I think this is interesting. It’s a thing to solve,” but there’s no real thought about what it means to solve a problem as opposed to research curiosity. Yeah, that’s my general approach is getting commitment from the user is the main thing to look at.

Grant Belgard: What surprised you most about turning research-driven insight into a company?

Deniz Kavi: I think I was broadly surprised by how disconnected academia is to some extent from industry. Like theoretically, all the IP from these companies, all the biotechs comes from industry. It gets spun out of labs, it’s the professor’s research, what have you. But in many ways also, there are dedicated industry research conferences.

Deniz Kavi: The problems you tackle are quite different. Yeah, I think a lot of the basic science is sort of– you know, obviously, there’s some actual practical R&D work also happening in, in academic settings. I think very much the context was, biotech and pharma think about the world in a, series of drug programs or like potential assets you can acquire versus, research will be more platform style.

Deniz Kavi: You want to get a– I want to make the machine that makes drugs as opposed to I just want to make a drug and get– create a company around that or, provide a way to create that drug in the first place. Generally, I would say industry is much more open to investing resources to make things more convenient and efficient versus, academia or research in general can be, I will have an undergrad do this for five, 10 years and just keep going through and churning through them.

Deniz Kavi: I think there’s a lot of benefits to some problems that can just be like thoughtfully thought of and then ground through for many years or many long amounts of time. But it also means the timeline for everything you do in academia is very much shortened by what you can do. Like academia or research in general can be a source of commercialization. But if you approach the world from a perspective like I am doing something that’s very hard, it’s impossible to do, and I’m just like seeing how close to impossible I can get, that sort of somewhat grounds you in your ability to do interesting sort of groundbreaking work in some way versus if you think the thing you’re doing is very hard, but it’s like possible and like you have a timeline for that.

Deniz Kavi: I think, projects that are set up in a way where it’s gonna be I’m gonna do this in the next two years versus I’m gonna do this however long it takes tends to be an interesting dichotomy there.

Grant Belgard: Where did you underestimate the human side of the job?

Deniz Kavi: I think for the most part for us it was adoption. So there’s, one– even once you like c-close a contract or a customer signs up, like you use it within their organization there needs to be a deployment process. You need to be able– can you do basically lobby and convince functional heads, get their teams to use us? More than I… And think we have a more rational sales process than we most other industries do. Like we don’t do these steak dinners or whatever. Like it’s mostly a case of proving product value and then scientific interest and value there, and getting people to trust you and be the source of expertise, I think pays a lot of dividends.

Deniz Kavi: And that is to some extent bounded by knowledge, but also the cases, you want to be writing a lot, talking to people a lot, and that sort of tends to be the way you become a brand or a known entity in both attracting talent or customers or what have you, is very much driven by these human social problems. And then I would say in the deployment side, it’s very much driven by like political problems, like how you get a team to adopt you and how do you get a larger team to adopt you and how do you get them to use more or pay more and what have you are… ideally, you’re convincing that they’re– you’re spending more on Tamarind is a sign of getting better science, but also more adoption means more people have access to these tools that are more practically useful for their day-to-day. I think I’d never really done any of these tasks.

Deniz Kavi: I think I’ve become a salesperson, as I mentioned, and learning that on the job was very exciting.

Grant Belgard: Looking back, what moments feel like the real turning points?

Deniz Kavi: I think we– So the company’s about two and a half years old today, or two years and a few months. And the first year, we basically didn’t really know what we wanted to build. We had this idea of a, web interface for all the models, and you can use them easily. But the models weren’t good enough yet.

Deniz Kavi: We weren’t sure about what the applications would look like. What the– is the use case, production agents? Is it experimentation? Is it like proteins or small molecules or, how much of the scale part matter? And we had to invent that with our customers, and that was a pretty meaningful thing for us to go through over time. In that sense to invent a category basically. It was the most surprising experience for us was to figure that out. And unlike, if you’re starting a CRM company or like a pretty typical software company, you can just check what’s happening in the world and you do it– you could do it again, but better. In our case, it was very much a case of how does this product happen ever and how does it become actually applied? I think for the most part, there’s been a lot of efforts in this direction, but none of them have gotten that much actual usage.

Deniz Kavi: So we had to invent what the of that product would be.

Grant Belgard: What skills compound fastest at the biology software interface?

Deniz Kavi: I think I would put just like being an information sponge as a very valuable skill to have. I think people underappreciate how much– if you just know a lot about the use case you’re interested in, if you know a lot about the customer you get to or just what the product you’re building is. Oftentimes, again, the world is changing very quickly. You want to be on top of everything. My view has always been I’m gonna spend all the time I have in the world passively learning what I can and being as close, curious, and interested in people as much as I can be. This can be, whatever, social media posts, YouTube lectures, or, it could just be a scientist you want to talk to. think a benefit of being in the world is that you get to talk to experts all the time.

Deniz Kavi: And I think if you’re doing a computational science PhD, and all you do is like you re- you write papers and you just publish them and nothing happens afterwards. Talking– like having a person that uses and is depending on you is a very strong incentive to learn in general. And the ability to learn is a very powerful thing to have. And putting yourself in a structural position where you can learn a lot is just quite valuable. The PhD obviously like you’ll learn a lot there, but the things you learn are very much how do I invest– advance my general PhD research theme versus, people are relying on me that are quite important in their organizations.

Deniz Kavi: What can I do to just solve their problems or go another avenue on the learning side?

Grant Belgard: What’s worth learning deeply even though the tools keep changing?

Deniz Kavi: I think honestly, the– one of the strongest– one of the biggest gaps we have is like very strong core AI folks. I think you can see the incentives here, like if you’re a really good AI person, why would you want to work in biology? Basically, like you can do the core foundation model research, you can do some interesting robotics work or do computer vision, what have you.

Deniz Kavi: You’ll probably be paid more. There’s a couple of companies I think like bucking this trend. There’s a few like good research organizations, but I think the core foundational computational skills are quite important to learn right now. And there’s a lot of these getting replaced by the old LLM coding agents, but I think having that technical fluency is quite valuable in that, I think many bioinformaticians have become basically people in the interface of software and techno– software and biotech, is they become masters of none. And that is not a great place to be for most.

Deniz Kavi: You’re being bought– you’re being hired for a specific skill, or you’re starting a company for a specific skill, and if you’re okay at everything you might be a decent cog in a machine, and you might just be helpful in an organization, but you’re probably not gonna move the frontier of that field forward very much beyond that. I think founders become a lot of generalists. I think you do need to be a generalist. I fundraise and sell, and also you write papers about like our benchmarking results, what have you. And to some extent, being an expert in something that is to computer science and then applying that to life science problems can be a very valuable way to approach this.

Grant Belgard: How should early career people think about breadth versus specialization?

Deniz Kavi: I think my general concern about breadth, especially in our field, is that you are putting together a lot of things where I’ve taken some classes at Stanford that were basically surveys of the field because you like want to simultaneously appeal to computer science folks. You want to learn about bioinformatics.

Deniz Kavi: to apply t- to apply the computational scientists, learn about more computer science. And those courses are like useful, but they also become basically surveys or like very high-level walkthroughs of the field. And then those don’t tend to create a thoughtful scientist in and of themselves. I think my view in the world in general, like not even beyond this field, is if you want to be an expert in something, and then if you are an expert in one thing, you can then become an expert in another thing and go from there, as opposed to trying to do those things simultaneously. To some extent, I have some concerns about the… I have skepticism of inter-interdisciplinary degrees, for example, in that direction where like classes or majors that prefer to be doing these two, two things simultaneously.

Deniz Kavi: Because if you’re trying to learn two things at a time and then combine them together, the degree program like makes a theme or makes up a way to connect those together, but not necessarily a way to actually be a good credentialed expert for that use case. My view would basically be, become the smartest person you can be for one field. I would probably start with computer science, honestly, and then see where you can apply the science from there, is my general view.

Grant Belgard: How can people get close to real scientific pain points instead of building in the abstract?

Deniz Kavi: I think honestly, talk to people or put yourself in a place where you have to do it. I think in some ways one of the best ways to learn about the economics of a therapeutics company might be to start a therapeutics company. I don’t know that I would recommend doing that for everybody just to learn about that stuff. But I think I really believe in the power of being forced by your environment to do something hard and learning those things on the fly. I think if you’re reading papers about how the drug discovery, this process is done, like it’s just hard to be in that mindset. Like it’s even hard to motivate yourself to read those things, right?

Deniz Kavi: ‘Cause like it’s not gonna be immediately useful to you. So I think put yourself in an uncomfortable situation. I guess in the like general case, I have friends who have applied for jobs in places that they’re, they were definitely not qualified to do, and they offered geo service for free for them, and if somebody responded, they would, they have to make that work in the next one or two weeks, and they respond in that way. I think putting yourself in an uncomfortable spot like forces you to learn something, and like that might be a bit embarrassing also if you fail. this is how I think about those things.

Grant Belgard: How should scientists decide among various career paths?

Deniz Kavi: I think it’s just, it’s a question of, in my mind, I optimize on what I think is gonna be most impactful. The most people or has the most meaningful scientific sort of core that grows into something else, is how I think about that. Part of the reason I became a founder was very much in this context of if I did a research route, might be a good scientist like five or 10 years from now, but if I’m the median scientist, I’m not gonna be very important to the whole of science. And I’ll have a lot of papers with have 20, 20 citations, don’t really go that far was what I was concerned about personally. You can be a great scientist. I think academic research or, re- general research focus is very important, and some people will do very powerful, impactful jobs there work, work there, I should say. My view has always been what are you optimizing life? In my mind, that was impact.

Deniz Kavi: So however many people I can impact, I would like to optimize for that, was how I thought about that. So if your belief is that, the s-science in– of the next generation of, say virtual cells will be in academia, you should go all into that and then become a PhD and a professor and doing virtual cell work. If you think it’s gonna be applying some AI tools to therapeutics work, you can start a biotech company or start your own company to some extent. Or the other side might be, I think the most important bottleneck is clinical trials, even though I have this life science background, let me go try to find out what the clinical trials can be solved with. Yeah, I think so find the thing to optimize that you think is most valuable and go for that.

Grant Belgard: What mistakes do smart, ambitious people make when they enter this space?

Deniz Kavi: I think the… I think the mistakes smart people make are always almost the same in independent of the field. I think oftentimes the questions are you try to learn from somebody else, become a clone of what you think is a good thing to be doing. I think it’s a– for the most part, it’s like a decent way to live your life is to copy someone who is better than you and pretty close proximity to you. I think that’s reasonable, but to some extent, you do want to take a leap at some point. You do want to take a risk as much as possible, and that doesn’t necessarily have to be like, dropping out of school and starting a company, but it can also be something like, going beyond replicating somebody else’s work, I should say.

Deniz Kavi: I think we set ourselves like heroes and people to admire and work to replicate and to be similar to, but that is– that gets you only so far in what you can accomplish. It’s helpful to have knowledge of history and what things happened before, but you probably don’t want to be living in the shadow of somebody else or someone else’s work as much as you would in an empirical context.

Grant Belgard: What habits help you stay grounded when the hype cycle gets loud?

Deniz Kavi: I think I just might say talking to customers again. I think sometimes I’ll– people will tell me their negative results, so I think it’s quite go– There’s a lot of ex accounts who just talk about how biology will be solved next week. And like I am quite like– I think I’m very much exposed to that technology world when I’m quite excited by the technology in the first place. But once you know what’s happening in a field, it becomes much less exciting for the most part. I think, I suspect, people who do the core AI research for LLMs are probably much more conservative than the people making products on top of them. They’re probably both very excited and ambitious right now. I think we’re actually yeah, I think ke-keeping yourself in a place where, you know, people whose job it is not to sell AI for their company, but people whose job it is to apply it to their use case.

Deniz Kavi: This is very AI-specific, but also can go through most everything else is, when you open news-newspaper, you see the headlines about science, and you think they’re all bad, and you read all the, headlines about politics, and you think they’re all credible.

Deniz Kavi: I think it’s one of these things where you just want to be close to the source as much as possible if you care about the results from a given category.

Grant Belgard: What question do you wish more people asked you?

Deniz Kavi: I think a lot about why there have not been great software companies in the life sciences over the, say past 50, 20, 30 years. I guess to answer that, I think the value of software was somewhat limited. most of R&D spend in biopharma goes to development, like it’s, assays, experiments, headcount and then like a bunch more of that money goes to clinical trials and all the commercialization efforts for like obvious reasons, because it’s much more expensive. And I think a lot about like why we are different in this world where, basically our belief would be AI is sufficiently new that– or powerful that compute will be a more meaningful spend on the biotech side, and this company can be a whatever billion dollar revenue company in the next five, 10 years. And, I get asked this from like VCs a lot, but not so much by products and practicioners a lot.

Deniz Kavi: And I am curious how folks think about the tools they use, like how viable and sustainable the companies themselves will be. Like in many ways, a lot of these companies get bought by private equity, they get merged into some other thing, like the product does not improve over five, 10 years. And, if you’re scientists who use these tools, don’t think too much about what happens. It feels like whatever happens to the company happens to them also, and like they don’t really get to control what happens. And I’d be curious about how scientists think about what software tools they use exist as sustainable, long-living companies versus, a thing they bought for a year that might disappear next year.

Grant Belgard: What belief do you hold more strongly now than you did a few years ago?

Deniz Kavi: I think I’m a believer that AI will be valuable drug discovery. I think like this is a– I don’t see the starting company as a more open-ended question, because of the bottlenecks in clinical trials. Again is AI the right place or the– is drug discovery the right problem to apply this even in the life sciences world where– and what if you’re optimizing clinical trials, you’re like shaving one year off a clinical trial? I’ve become very convinced that at the current rate of getting better, the models improving, will have, net new assets added to every biopharma company’s portfolio of assets that will be, driving revenue for them. I think like we think a lot about, science outcomes like approvals and clinical trials, but I think, AI will make more money for pharma companies is on the drug discovery side by making more drugs for you to get to clinical trials and then approvals eventually.

Deniz Kavi: And I think this will almost inevitably will happen in the next of years or very soon, basically. I think, the adoption is there, there’s interest, but also I think the results will be speaking for themselves very soon.

Grant Belgard: What about the inverse? What have you changed your mind about in the last few years?

Deniz Kavi: Yeah, I think the– I am generally actually averse to science for like, goodness sake. I think you do want to think more about what the outcomes will be. Yeah, I think revenue is a core metric when you’re trying to make medicine, think about the commercial results. But to some extent you are serving tooling for an organization, and I have not thought about, I think about science as an interesting, valuable way to impact humanity and be good for the world, what have you, and I believe that still. But what you do day-to-day on– at your job is basically the same, whatever job you do. Like some– or between the roles I can do anyway are very much the same. And you want to pick your battles more carefully what you apply yourself to. I think I used to think that, you want in a field that is good for the world necessarily has to be strictly a very clear value add.

Deniz Kavi: if I went back to my, yeah, to three years ago and I start– just starting a company, I might just tell him, think more clearly about do you want to work in science forever?

Deniz Kavi: Do you want to make a bioinformatics software company? To some extent, you don’t necessarily want to be making a plan for yourself without knowing the requirements for that plan. So in this case, it would be, let me rephrase what I was trying to say, which is basically is the sort of aligning core thing you’re motivating your acts by?

Deniz Kavi: I mentioned previously you want to pick a job or a thing– or one singular thing that you’re optimizing for, but be more thoughtful about what that thing is and not just which fields are is good or perceived to be good by from a fuzzy, open-ended sense. And I think there’s a lot of value to be added for clinical trials, like a lot more boring even like payroll or like better health insurance, what have you.

Deniz Kavi: That type of thing is still quite valuable, and I would not– I would have looked down on those companies beforehand, where I’m now much more agnostic to humanity. This is a singular sort of effort to make the species better, and whatever you dare can be quite valuable.

Grant Belgard: What boring looking skill turns out to matter far more than people think?

Deniz Kavi: I think generally remembering things is quite important. I think like making structures for yourself to remember people’s names and where they work and what their, use cases are. I think it pays a lot of dividends in getting to know people effectively. If you keep seeing the same person over and over again, you can talk to them more effectively.

Deniz Kavi: Yeah, I think taking a lot of time to remember other people and also the specifics of their work or like what they might be related to you is quite valuable. We’ve seen this a lot in hiring, where like keeping track of somebody for many months, like just eventually converting them to a part of the team or a customer in our case.

Deniz Kavi: Or it might just be scientific collaboration or working with a partner on a specific program. I think just being very– taking interest in people and being thoughtful about keeping that interest in your– like in a structured way. Maybe you should remember from the top of your head, maybe you should write it down for yourself. I think paying attention, is the general theme here, is like purposely and spending a lot of time paying attention is quite valuable.

Grant Belgard: What are you still trying to get better at?

Deniz Kavi: Yeah, I think I spent a lot of time living and breathing with scientists, and I still do most of the time. I think we are as a company looking a lot more like how we can get this enterprise level adoption question of, a mature company in our space should look like, nine-figure revenue per customer. And that means basically everybody at this company uses you and like for a wide variety of core tasks. I want to become a better– I run a what, 15, 20 person company today. I’m not like a huge organizational figure. They’re all in the same room, basically. My– I want to learn about how our customers think about systems, how they think about the organizations of their large organiz- companies, like making those func- machines function well.

Deniz Kavi: Having managed a small number of people so far is a very complex problem, and both from understanding of what product we build next, I would like to be understanding of how the organization behaves and reacts to those things, but also running my own team more effectively. I think so I have basically over the course of the company, I’ve picked up skills I’ve never had and I was quite bad at the start, and I’ve become pretty good at.

Deniz Kavi: I think the next thing I want to be best at is understanding the behaviors and intents of a large organization and building products first and foremost for those organizations, but also apply those learnings to my own

Grant Belgard: What would be exciting for you to see happen in this space over the next few years?

Deniz Kavi: I think if we got 50% of the way to what the models claim they’re able to do, honestly, it would be very exciting. I think that would be a pretty meaningful proliferation of these models for different use cases. The approaching design is reasonably good, but if we got to I guess whoever those sort of hype people say on Twitter about these models, if they become real, which I think is like within the bounds of the we get today, would be very exciting. I think ultimately Tamarind is a index on the belief that, models will be getting better for molecular design applications, and it’s obviously good for me that the models get better, but also for the value of how we can produce more medicines more quickly, more effectively, and then even before that, just producing new medicines, period, so you can have another list item in the list.

Deniz Kavi: So I guess beyond the more boring and the models get better, but if they got as good as what people, some people think it, they are at right now would be the main thing I’m looking forward to.

Grant Belgard: Last but not least what would you like our listeners to take away from this conversation?

Deniz Kavi: Yeah, I think the general thing is you don’t really need permission to do things. You can start a company, you can work on a research project. That doesn’t mean that it’ll be easy. People will be rejecting you very frequently. But being in an environment where you are constantly rejected, being very– in an environment where you you’re punching above your weight is a very valuable place to be. I think people go and follow existing tracks a lot in life, which is very comfortable. It’s not pleasant, but what you need, what you’re supposed to be doing. I think you can go above your station and if you can hold yourself long enough there, you probably will ascend to a larger point. I am a not very long career– I did this out of school, and I started a pretty meaningful company still, and, much more to be done for sure. It’s a pretty, pretty small company still.

Deniz Kavi: And I think that the way– The limited success I have had came from being– putting myself in a place where I did not feel welcome or I felt like I was not supposed to be in this place.

Deniz Kavi: And oftentimes I was not supposed to be. Like, I was clearly not qualified for some of the things I began doing. And then just being in that situation makes You rise to the occasion when you’re put in an occasion where you have to be responding to something very hard, and putting yourself purposely in a place where you have to do something quite hard or at the time not possible for you to be doing is quite valuable.

Grant Belgard: Dennis, this has been fun. Thank you for joining us.

Deniz Kavi: Yeah, thank you for having me.