Business Of Biotech
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Business Of Biotech
Investing In The Biostack With Modi Ventures' Sahir Ali, Ph.D.
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On the Business of Biotech this week, Sahir Ali, Ph.D., founder and general partner at Modi Ventures, a family office investing at the intersection of technology and biology, talks about adapting the Markowitz model to improve returns and balance risk, his concept of the "biostack" for making direct investments into life sciences companies, and the revolutionary potential of scientific super intelligence. Ali explains why Houston, Texas is an underrated ecosystem for life sciences, and what AI will mean for medicine and the future of healthcare consumption.
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Welcome back to the Business of Biotech. I'm your host, Ben Comer, Chief Editor at Life Science Leader, and today I'm pleased to speak with Sahir Ali, Ph. D, founder and general partner at Modi Ventures, a family office focused on the intersection of technology and biology. Sahir is a young man to have already led many professional lives. He worked on Wall Street during his undergraduate years. He trained as a biomedical researcher, worked as a cancer scientist and software engineer, became a sought-after Salesforce architect and also found time to start a successful e-commerce business with his brother, Amir Ali. That doesn't cover everything. We'll get some additional color from Sahir about the experiences that got him to this point, and we'll also learn about Modi Ventures' unique investment model, how it builds on the Markowitz model for portfolio organization and what the future of tech, bio and AI in medicine might look like. Sahir, thanks so much for being here. I really appreciate it.
Sahir Ali:Oh, happy to be here and thanks for all the introduction there.
Ben Comer:Yeah, absolutely. I want to ask you a little bit about some of that intro. You were born in Pakistan and moved to the U. S. -- Houston Texas, specifically -- as a child. What sparked your interest in science and technology initially?
Sahir Ali:Right. Well, we moved here when I was 10, 11 years old, and it was an interesting time in late 90s where, um, uh, I'd never seen a computer before, in the sense that it was never close by, obviously seen a computer but never had one in in my house. So when we moved to houston, um, within a month, uh, we had a computer, a compact presario and um so sort of. It was an unattainable thing that now was there. It was gifted by my uncle. That kind of just really sparked curiosity on how it actually worked, and so I think I went down the rabbit hole.
Sahir Ali:But one of the more, I would say, profound moments for me was being part of FIRST Robotics program that Dean Kamen started. I don't know if many people would recognize that program. It was in late 80s. Dean Kamen thought you know, in every school all the athletes are treated like stars. You know you have pep rallies, you have cheerleaders, but what about STEM kids? They're the ones actually going on changing the world in science, technology, engineering, math. We should treat them like stars. So he started this program called FIRST Robotics and it's a robotics competition. It's the same concept as a competition we should cheer the kids on. And so that's the program and I became an alumni of that and in many ways that was a big part of my growth and the curiosity that led to being very technical, in fact, where where was that program?
Sahir Ali:This program. I mean the program was at, so if someone could be a coach that could just recruit neighborhood kids, it, it. It was at school level. In this particular case it was at NASA level right as well, like you know, just a community service. It was different levels. I was particularly going to an after-school program at NASA, but you know, in Clear Lake it was walking distance. But just so that everybody's aware, even today it's the similar model Schools level. Someone could become a coach. You don't need to be a technical, you just bring kids together. Yeah, so that's kind of the thing and I always tell people that by the time I was 15, I was still picking up English, but my second language became C++. So that's the kind of joke that I tell people that the program was quite influential for me. But you know, that kind of got me started on a technical journey very early. So that kind of got me started on a technical journey very early.
Ben Comer:Do you think that you know? There's been press reports and stuff about people coming out of university with code, you know extensive coding instruction, having trouble finding a job, do you? I'm curious if you think that you know that interest in coding early for you would have kind of the same results now. Or do you think that we're going to get into AI later in this conversation? But do you think that coding you know, just the technical aspect and process of doing it will become irrelevant as AI gets stronger.
Sahir Ali:Yeah, no, it's an interesting question. So I don't describe what I learned during that time or one of my formidable years. It was not. It was not coding, it was, I think, computational thinking that's. I would praise that and and yes, at the time it wasn't as simple as even google was just around, but it wasn't as easy as just picking up just to write code. I think it was the computational thinking. Where do you want it to apply that? Um, the robotics, getting early in the robotics, you know, brought that physical element together where you can manifest things. But also on the software side of things, you know, those days, you know it.
Sahir Ali:Just, it was just a very different time period where you had to really go into the technical details to learn anything. For example, if you wanted to host a website you wanted, you had to understand how to telnet. You know the term telnet doesn't exist today. For example, if you wanted to host a website, you had to understand how to telnet. You know the term telnet doesn't exist today. You know SSH, most people would know that.
Sahir Ali:So those things, you had to just be very curious to go down in the rabbit hole and really get a full stack, and so coding was probably the end result of that, but you had to become very full stack and so that sort of computational mindset, being able to very, very being curious, and had to find your own set of knowledge or acquire set of knowledge. I think that was. That is what we call human curiosity and learning and all of that. So I mean, I don't think much has changed today. Sure, you can vibe code and all of that, but just being able to kind of go and go deep and understand, get yourself technically well equipped to do something with it, I think that still exists today, but different times, I agree, of course.
Ben Comer:How did you end up on Wall Street as an undergraduate?
Sahir Ali:Yeah, so you know, before, a little before college, you know, in high school, there were a couple of interesting things that I was always involved with. One was I had understood that if I wanted to stand out, technical chops were the way to go, clearly not a jock or anything like that. So that kind of was my moat, if you want to call it, and so I was very curious on what all the technical applications were there and I'd come across sort of quant as a field and you know I Quantitative analysis is what you're referring to.
Sahir Ali:No, I'm actually particularly referring to be a quant as a role on Wall Street.
Ben Comer:Oh, okay.
Sahir Ali:Yeah. So, yes, quant is short for quantitative, but really quant around 2006-ish time period was really referring to folks who are just mathematical. You know physics-oriented guys who are creating mathematical models at Wall Street and, you know, at the time as a young in my teenage years, to me it seemed very fascinating that these guys were paid lots and lots of money and most people didn't know who they were, and so there was an allure to that and so I read a little bit and eventually, at Rutgers it turns out Rutgers had a pretty pretty well-defined quant program for grad school and engineering electrical engineering at some point when I started to understand is, you know, it's all signal. This is pre-machine learning era. So just, I think, knowing enough and knowing enough about it at the freshman level and, you know, not many people even knew what quants were.
Sahir Ali:I think that was kind of my foray into it and luckily, you know, I ended up at an ultra investment group within Citi and you know the rest of the history. But that's kind of the sort of precursor to it. It's just that again it comes down to this just being technical, full stack, just to be able to know what I call asymmetrical information. What is it that you know and being able to use that that you know, you can make yourself stand out. And for me, that just knowing enough about what a quant does, what, what quant is, and being able to position yourself as your curiosity and interest is, at the end of the day, freshman internships are all about just you know how curious you are. You're not supposed to know much.
Ben Comer:Yeah, yeah. So you had some success, some early success on Wall Street. What? What did you do next? And we don't have to go through your entire bio, but I'm trying to pull out some of the things that you learned, some of the experiences that you eventually are going to bring together to create Modi.
Sahir Ali:Yeah. So a couple of things. One is the quantitative mindset was developed in many ways there. How do you think about asset management? How do you think about portfolio constructions? You know these sort of things early on being in financial services.
Sahir Ali:Also, it was an interesting time when I was there. It was financial crisis in 2008 and 2009. And so that is a one. It's a black swan moment, and so being in that in the space, the kind of learnings you can see and just just what you can observe is is once in a lifetime opportunity.
Sahir Ali:Yes, it was bad for the market, but, from a learning perspective, what all that happened just being in that moment and there and just you know fortunately for me, I, my internship turned into kind of a full-time gig. So I was lucky enough that you know, throughout my undergraduate I had a BlackBerry and I was paid well, and so just being in that environment and just be able to learn and the second thing is obviously quantitative trading, and all of that had kind of come to a halt in that time period. So I had an opportunity to adopt another technology. At the time it was called Salesforceforce completely orthogonal. However, salesforce 2008 and 9 was sort of early days, and so I picked up a new cloud technology and able to sort of think about how do we build pipelines and all of that, so.
Sahir Ali:So I think that so if you think about, uh, how do you look at the financial world, how do you think portfolio, how do you think about business and then selling of that, the pipeline, the lead to cash, and then eventually, at the same time, when I went back to campus, I mean I was already doing some computer vision research and all of that, and then I got really interested in how do we apply the same quant mentality to, say, cancer or medical imaging. And that's where I joined a lab that, early on, one of the few labs actually were looking at digital pathology and such. So anyways, that's where the things start to sort of intersect. This idea of being sort of quantitative stems from those days. Yeah, so you eventually reached a point where you were in a position to.
Ben Comer:So you eventually reached a point where you were in a position to establish Modi Ventures. Give us a sense this is a family office. You're making strategic investments. Give us a sense of where the money came from. Was it just your successful career up to that point to serve as the basis for your initial investments, or what would you say about that?
Sahir Ali:Right, yes, we're lucky and blessed. My family, which includes my brothers, you know we're entrepreneurial. At the same time, it's very academic and we had companies in the e-commerce space that did fantastically well, and again, that's a story for some other time. However, that also included being early in some technology, looking at the world from a sort of a I like to call it convergence what is really converging and what the derivatives will be that. So we kind of set up a tech platform for that, had a couple of other acquisitions that did well, and especially in the year 2021, end of 2021, early 2022, there was a phenomenal shift again in the financial and capital markets where the interest rates went up so much that it kind of disrupted the capital markets.
Sahir Ali:But prior to that, during pandemic, I was deeply thinking about the venture capital world, because that is a world that I was looking at it from an outsider perspective. We had made some investments but really wasn't in the venture capital world, if you want to say. I started to realize that I think it's become a pretty mature asset class over the last couple of decades. I think I'd written, I was staring at this chart where somewhere in the orders of $30 billion to $35 billion were raised in totality in 2008 and in 2021, 190 billion dollars or so in just that one year. That was a phenomenal rise. All this and that was sort of given by what we call social networks effect. Right, you create an app and just quickly distribute it and you have users and the ad space and all of that, as I was, you know, as a pandemic happened. I said what's going to be? What's going to be the next thing for venture? Obviously, the power law was driven by software. That just scales very, very quickly. Obviously, the SaaS world and anything in the software world is now saturated. So what's going to come next? And obviously AI was a thing that was on top of my mind because I was doing some deep research work using AI At the time. I was tracking what had happened with AlphaGo reinforcement learning in 2021. The AlphaFold had come out, obviously, 2017 paper attention is all you need sort of that encoder-decoder models we were starting to go towards at the time. We were just calling it zero-shot learning, right, and so everyone was very excited about that. I thought that could be very interesting.
Sahir Ali:The second part I thought which, particularly in 2020, a chart that started to float around was that how drastically the cost of sequencing had come down. This was perhaps the most significant cost reduction of any technology that we have seen. It went from $3 billion, so to speak, to finish human genome to $300 by pandemic, which is 2020. And it played a role. You know, sequencing technology, the PCR, played a role in overcoming the COVID pandemic. And then the third part, you know, which led to digital biology and all of that, and we'll talk about that more. And the third is, you know, I think in the last 20 years, we've had some phenomenal progress in what we call cellular programming, with the tools like CRISPR, base pair editing, ips, stem cells, and so I thought, you know, this field of biology is, in many ways, have started to become very deep, tech-ish engineering, and I thought that was a real opportunity to be in that space, while everybody else would just purely get excited about generative AI and all of that. So, anyways, so I was thinking about that.
Sahir Ali:And then the other part of the quant mindset comes in is that clearly there's a gap in venture capital world where we can create a different type of portfolio. So, anyways, that's the impetus to a lot of that, and Modi Ventures really stems from that. It is not yet another venture fund. It thinks about the portfolio construction very differently, in a quant manner. It thinks about the intersection of technologies that I just mentioned to you, which I've started to call biostack, and we'll go into that. And third part was how do we actually start to think about risk modulation in venture capital? Because, as most of us know, that venture capital is very volatile but it operates on power law, which means you have to be okay with extreme amount of volatility, which means 90% of the companies might just go to zero and 10% successful. So, anyways, you combine all of this. That's kind of the impetus to Modi Ventures and luckily we were able to raise our first fund very quickly and you know, as you know, we just announced our second fund two and a half years later.
Ben Comer:Yeah, and I want to. I want to get into Modi Ventures and your investment framework in just a second. But I'm curious about, you know, the the establishment of Modi Ventures in Houston, aside from your childhood experiences there, what kind of drew you back to Houston and maybe what would you say about the Houston life sciences ecosystem? It's not, you know, it's not one that is often, you know, talked about along the same lines of you know, san Diego or Cambridge, or even New York.
Sahir Ali:Yeah. So I'm glad you actually asked this question. Why Houston, besides my own personal connection to it, family connection? Well, houston, well, let's. Let's start with the basic. First of all, houston is home to the largest medical complex in the world Texas Medical Center. I think most people may not realize this, that 120,000 people go to work in that small district here which, if you have an aerial view of Houston, you're going to start to see three downtown looking areas. One is the core downtown of Houston. The other is the financial district where the which we call the private equity world and everything else called Westheimer, and the other is TMC. It's high rises in all hospital systems.
Sahir Ali:You know this area does more heart surgeries than anybody else. It has the best cancer care therapies. You know, obviously, md Anderson is part of this and also, in many ways, one out of three approved oncology drugs in FDA, the clinical trials happen in that area. So, effectively, what you have is a very deep, complex arc of what we call applied medicine. What it has lacked, typically, is the, I would say, the bottom arc, which is company creation. Innovation is there, it's just that the capital may not be there. And so how do we turn this into a full flywheel of life science where you have the large human data, you have the clinical trials, you have the know-how of practicing medicine. So the key thing is how do we turn that innovative wheel on, how do we bring in the right capital, how do we start companies here? And that is really what Houston offers, and I think it is under the radar and I think it's going to do really well.
Sahir Ali:Lastly, the state of Texas has also contributed to sort of how do we advance this? For example, there's a grants mechanism called SIPRIT, which is for cancer and oncology. It's a $3 billion pool of state money available to companies, private companies in Texas $100 million a year up to $100 million a year in funding. They can also match private investment two to one. It similarly is proposed for DIPRIT, which is for dementia $3 billion again. And so you also have that.
Sahir Ali:We have something called Helix Park, the Texas Medical Center Initiative. I don't know the acreage, but it's a multi, multi-billion dollar development plan that's ongoing. At the moment we're sitting in one of those buildings. It is all just about biotech innovation here. And also, lastly, I'll say I'm on the board of something called Rebel, which is spun out of the Rise Biotech Launchpad. It's going to create incubate companies that are going to be at the intersection of drug delivery mechanisms with tech bio. So, anyways, there's lots going on here and I thought this is a real opportunity to build something which is probably going to have an inflection point. So, selfishly, this is not just a personal connection, houston, but I think there's a lot here that we could do with private capital, innovation and turn that into a flywheel, so I'm very bullish on that.
Ben Comer:Let's talk about Modi Ventures investment framework I mentioned. It's based, at least in part, on the Markowitz model. You mentioned previously that you wanted to set something up that was kind of fundamentally different from other VC groups. What could you say about Modi Ventures investment framework, I guess, and maybe how you think differently about risk and return, especially in biotech?
Sahir Ali:All right. So you know, and again I'll go back to 2021. So there was a couple of interesting observations for me. One was that if you look at the venture capital world, everyone talks about power law, which effectively just basically comes down to is that there's going to be a very handful of companies that will drive the returns. Rest will just go to zero and that's a very that, even as a mathematical distribution, that's a very sort of a volatile way of looking at things. In fact, you look at overall every asset class available in the market. Venture capital tends to be high risk, high reward, and so the couple of things I thought of was that, if you think about 90% of the money that actually funds venture capital, that those entities are not generally what you would associate with high risk, high return sort of profile, like endowments and pensions 401ks yeah.
Sahir Ali:Yeah, they. You know it's public money. However, they do have exposure to venture capital. I mean, in fact, some of the college endowments, the Yale model. They're up to 10%, 14%. So I was like, okay, well, how do they think about this space? And turns out that they may not associate such high volatility to venture capital is because they don't invest directly into companies. They invest into fund managers who are extremely established. In fact, if you want to get 50, $60 million from a pension fund or endowment fund, you got to be on your sixth or seventh or eighth fund. You know you got to be very established funds to be able to show extremely tight record. So I thought they're looking at this space as in the venture capital world lower risk and perhaps also lower return, because as the funds go, you know the larger funds you're not returning, the proverbial 10 Xers, right, it's just a law of large numbers and also Cambridge report, year after year, identifies that. So that's one 90% of the money that funds the venture capital are in established funds and those.
Sahir Ali:The second is there's another sort of what I call an asset within the VC world, which is direct investments. You can go invest directly into a company and if that company goes on to become a very successful one, you can find yourself into 100x or 200x mixes, but of course the outcome is very binary there, and you could. You know that's where the power law really comes in. And so I thought you know when you think about. What Markowitz was talking about is that if you look at the various assets that are available in the market and you look at the base case, which is a stock and a bond problem, a bond tends to be riskless but you know very low risk but low return, stock volatile, but it could give you a high return. But if you have sort of the this, if the equation of market is based on correlation and you know the risk return profile, if you could create an uncorrelated baskets of those, you could technically bend the curve. What that means is, if you can mix them up, you could take less risk than your lowrisk asset and get a higher return. And so that's where I thought if we could do Modi Ventures in a way that we could mix the right limited partnership positions with direct investments in a unified framework and we'll come to it what that framework is from investing on a biostack then I thought there was an opportunity to apply a bit of quantitative model where we can modulate the risk and get a pretty decent return and don't have to play what I call in a power law space where plenty of people are. So that is the fundamental framework, without getting into a lot of the more proprietary details, but that's the mindset and the framework. So it's not a fund of funds as such, but it's also not a co-investment in such. There's a fundamental quantitative way of sort of looking at this and inspired by the Markowitz or the Fish and Frontier in the venture world.
Sahir Ali:And then I thought if you have this sort of framework, what's worthwhile to look at is the biospace. Why is that? Because I don't want to use the word biotech, but when it comes to bio and medicine particularly, it has a very interesting spectrum, right? I'll give an example here. When you look at any tech company, which is primarily what venture capital is driven by, it has seed series A, series B, series C. These are sort of series that define some kind of product market fit tied to how much money can you raise.
Sahir Ali:However, if you think about the bio world, some of the attractions defined by your experimentations and clinical outcomes, right.
Sahir Ali:So you go from lab to IND, you put your lead candidate in, then you have phase one, you have phase two, phase three and then market approval. Actually, it turns out at phase one, phase two, phase three you can actually have some deterministic outcomes. You can actually say at phase two who could be a potential buyer, what could be the peak sales. You could do those deterministic sort of outcomes, and so that actually is very interesting than just purely tech, where series A doesn't mean much in terms of what your outcome could be. And then, if you look at phase three plus, you have royalty funds right. There are ideas that you can actually partner with a biotech company in exchange for royalties rather than equities. So I thought you take this biospectrum and then you apply that framework that I just said a mix of LP positions and directs and how do you capture the entire spectrum? That's where the bending of the curve happens, and I thought this framework just lent itself very well because you have different assets, a different volatility, different and could be uncorrelated.
Ben Comer:Now, to do that, did you have to create it's part of Modi Ventures a proprietary software that you're able to do that kind of analysis across the industry?
Sahir Ali:Yes, so I wouldn't call it a software. It's not like packaged thing where I can just give it to somebody, but there is some deep analysis and modeling as such. So I'll give you one example. When you think about, there are hundreds of funds. In fact, last I looked at it there are close to a thousand biotech funds even right Around.
Sahir Ali:And the key thing is for me, how do you find uncorrelated funds that you want to? So, for example, typically what happens with fund of funds, the asset managers who run fund of funds, they go after the top managers, and my sort of hypothesis there was that these fund of funds run by, say, large banks or the asset managers, they tend to be in very correlated funds. What I mean is they tend to be in very correlated funds. What I mean is they tend to go after the top funds and they kind of end up investing in each other's syndicates. Someone led a series B, someone will come after series C, and that I'd call it sort of a correlated sort of outcome. So how do you actually model something where you can start to see uncorrelated funds?
Sahir Ali:And one thing that has what I haven't talked about is one data structure, as we call it in computer science or computational algorithm that has driven everything I've done across I would say the work at Wall Street, in Salesforce, in cancer research and AI has been graph algorithm. How do you think about the world in a network graph? So we so the proprietary thing I'm talking about is that we did. We looked at all the venture capital funding in the last 10 years at the time in 2021 and got our hands on some data and we looked, we mapped it out and we created a graph network of that and applied a graph algorithm and we start to see where the uncorrelated nodes are and the node is represented by a fund. To see where the uncorrelated nodes are and the node is represented by a fund. And so that's where an interesting way of actually looking at okay, well, if a fund that is clustered together with other funds, we don't want to go and invest in more of those, we can just invest on one of those and then go after an uncorrelated node which is in a spatial distance away from that, and so what that allows you to do is get uncorrelated sort of basket of these funds. So that's one example I can give you on how the quantitative analysis and modeling actually comes in handy.
Sahir Ali:And looking at even just the funds world, which one, which funds, we want to invest in? Yeah, and what's the outcome of that? I'll give you a more tangible outcome of that. So, for example, we invested in a fund that purely only looks at phase two plus assets. We also have a fund that only does sort of royalty investments, meaning after phase three. We have a fund that looks at completely probably the most earliest investments that are spinning out of colleges and universities and pre-IND, so to speak. We have a fund that is quite generalist but 28% of their portfolio looks at digital health and medtech and all of that which is not governed by that biospectrum, but it is in the biospace. We have a fund that 33% of their allocations are in computational bio, the tech biospace. We call it the AI, drug discovery, the computational nature of those things. So you can start to see how do you find those funds that are uncorrelated, and that one part is it allows us to look at where they fit into the grand scheme which I call a network.
Ben Comer:So by cobbling together these uncorrelated funds. That's how you get to what you've described as bending the curve, meaning that it's a similar amount of risk. However, you potentially have a greater endpoint. You have a better result. Is that what you're saying?
Sahir Ali:Yes, partially correct. However, the bending of the curve happens by mixing lower risk assets, which I will bundle as limited partnership positions, with direct investments, where we're also making direct investments. So think about it like a bond in a stock. That's where the so modern day portfolio, the wealth manager, will say we'll do 60% in stocks, 40% in bonds or 20% bond. It allows you to modulate your portfolio to say is it a low risk? It's a moderate risk, a high risk, right? So that's one framing, even within the LP positions that I described to you.
Sahir Ali:Not every fund carries the same level of risk. For example, we are LPs in Coastal Ventures. I would consider that as a lower risk investment established funds. However, we do have a couple of emerging managers. I would consider them a little bit higher risk. However, if you compare the both, one has a higher propensity, if they're successful, to deliver more higher returns and outsized returns than, say, a billion-dollar fund. It's a reality and so it's a risk-return profile, even within the limited-partisan position. But we also take that as a full portfolio approach of LP positions and directs. Typically, what you see in the industry is a fund of funds that might just offer co-investments with the funds they work with. In fact turns out our direct investments are not correlated with the funds we're in. We don't go and double click on their companies as much we have our own views of the world. Yes, once in a while there is an overlap because that's the relationships we have. But it turns out in fund one we only were correlated with the funds we invested in. The companies they have less than 12%.
Ben Comer:Yeah, that's really interesting because, of course, you do also have the direct investment in exceedingly risky assets in the you know meaning companies, early stage biotech companies are. Well, let me ask you this you objected to the word biotech. Do you prefer tech, bio, or what do you dislike about biotech? No objection, it's just that.
Sahir Ali:You know. I think it's just all about framing. I'm starting to call it investing on the biostack rather than biotech or tech bio, and I'll tell you why. So the way I describe biostack, like anybody else would, is the DNA, rna, protein, cells, tissues, organs and organism. So that's your stack and I'm taking a page sort of a tech stack, right From electrons to silicon wafers, to the transistors, to the chip, to the motherboard, the operating system, the computers and the cloud, and you know the whole stack. In fact, if you were a venture capitalist, you invested in some parts of that stack to get some diversification and all. So you know you made some investments in the infrastructure layer of the cloud. You made some investments in the software layer, you made some investments. So I think, although you could make that separate layers here, I feel like because the space of bio and medicine is having a converging moment, the better framework is the bio stack. I'm saying this is not true for everybody else the better framework is the biostack. This is I'm saying this is not true for everybody else, but at least for us.
Sahir Ali:So if you looked at the stack, we have profound sort of convergence and breakthroughs at each of the stacks. So, for example, at the DNA level, we call it genomics, being able to read what's in the DNA. That's a read breakthrough. That happened, you know, with the rise of DNA sequencing, ngs and all of that. But we can also write on that stack now through CRISPR. So we have a read and write breakthrough. Now we go up the stack on RNA. We have RNA sequencing, quantum science, a bunch of companies being able to read, but we also have companies who have foundational models that can predict the structures of the RNA. But we can write at the RNA stack as well. By the way, most people know that technology, that was mRNA. So I can make a case that the entire stack has now read and write breakthroughs. We can actually create digital biology, use digital biology and at each stack you have massive amount of sequences of data coming out. This is where LLM technology is extremely exciting to say. Well, we have sequence-to-sequence breakthrough in computer science. We have digital biology breakthroughs. We're understanding. We're starting to understand the cell. You know there are a bunch of companies pursuing virtual cell concept. We can start to think about biology as an engineering principle, systems biology. The stack is the guiding framework.
Sahir Ali:Now I'll give you one last example, in the protein layer. So some of the investments we've done so at the protein layer, so some of the investments we've done so at the protein stack. We have a company called generate biomedicine right, it's pursuing the novel protein designs, generative, ai based, um uh therapeutics that are at the protein level, right. So so that's, that's, that's your traditional tech, biocon kind of company. But we also have an investment in a company called unnatural product that looks at macrocyclic peptides. These are peptides that are between small and large molecules, but you can engineer them to have a permeability in the cell like a small molecule, or they can bind like a large molecule.
Sahir Ali:That's a new sort of modality of drugs, as Merck is in there and in fact the CEO of Merck about a year and a half ago said this will be the new class of drugs. So you can see that they're both kind of uncorrelated, they both kind of use AI, but very different to each other. The last example I'll say on the protein is we have an investment in a company called AtherBio which looks at the enzyme space and say how do we reduce it down using computational techniques to do something in manufacturing space? Because enzymes are the building blocks for us. We can take that down in manufacturing, say for removal of PFS, low-grade brimes of lithium to high-grade, that sort of speak. So that allows you a framework to say I'm going to target a layer and make some uncorrelated bets. So that's how we think about it and at the organism level, by the way, we call it the health.
Ben Comer:So some digital health and medtech fits there, got it. So, in thinking about the companies that you're making direct investments into, are you, do you see those companies as being, you know, at one layer or another of the bio stack? Is that you know how? Yeah, and that's how you are you trying to balance it throughout the stack, or are there, you know, certain parts of the stack that you're more interested than others? You know?
Sahir Ali:that's a good question of the stack that you're more interested than others. You know that's a good question. I don't think I was. It's, ultimately what we have to see is where the returns are going to come from at the end of the day. And so I didn't really, I don't really think about what part of the stack that I think it's going to be. It's not like the tech stack where the software just made a lot of sense. Now you know it had the distribution, so what this framework is.
Sahir Ali:In the hindsight it seemed like our stack turned into more of a protein sort of layer, became like the bell, if you were to imagine the bell curve and the organism level where we start to make some deep tech investments into, say, a dental robotics company or a company that can take digital auscultation and turn it into ambient capability and things like that, right. So I don't have to necessarily see which stack becomes more proliferating, but that stack is where we want to be able to deploy, which also allows us to be uncorrelated. That's what I keep highlighting, that term which, by the way, if you are, by virtue of that, you're also diversified. Now one could also say that, hey, you're just diversified, now it's the one. One could also say that, hey, you're just too broad. It's a fair thing, but our math model is also very different and so in that sense, it's okay for us to be quite broad. Uh, and you know, I'll also say that we don't have to worry too much about the market trends.
Sahir Ali:For example, you know funds who just do a lot of therapeutics. They tend to be very much focused on what a pharma is going to acquire in next couple of years, and so you get very correlated to that. So in and one bad phase three that failed, and then the entire field sort of takes a setback. What we have here is it allows us to be resilient, a portfolio, to be resilient to to some of those forces, because you know, as we both know and everybody knows, that biology, investing in bio world and biomedicines is not just risky but also uncertain. Right Investors know how to manage the risk. It's not the risk part that scares people away from this space, it's the uncertainty part, because biology is uncertain. And so this allows us, this biostack framework, allows us to be a bit, build a resilient portfolio, other than you know the mix, the quantitative framework of LP positions and directs, which takes the whole thing and takes this risky space and gives a basket of low-risk assets which are limited partnership positions.
Ben Comer:Right, so here is there anything else you could say about the criteria that you use to select the companies that you're going to make direct investments into?
Sahir Ali:Yes. So this is where I would like to redefine what I call tech bio a little bit. What I say, that I like to see how our companies are at the intersection of these three fundamental technologies that are converging. So one is, as I mentioned, I call it sequencing-based digital biology. Right, so we can now digitize the biostack, as I said. And so anyone that's building something deeper in that tech for example, one of the companies we're invested in is called Glyphic Bio. That is built on top of nanopores, that can do more accurate protein sequencing worthwhile building that space, because protein sequencing is going to have a huge sort of market, um, and so that's that's sort of uh, anything that is touching the sequencing and digital biology. Uh, it could be a software company, it could be an analytics company, it could be someone's that doing something in liquid biopsy and so those sort of things. But once you have those sequences one of the biggest obviously not many people are not convinced that there's a role of artificial intelligence on that biostack, a profound one, right From not just drug discovery and design but being able to understand causality of disease, for example, one thing we've known is from Alzheimer's.
Sahir Ali:What are some of the variants at the genetics level right. Machine learning has played a key role to understand APOE variants. We have AI in medicine, radiology, pathology. We're starting to use AI in physical settings that we never thought was possible or we have never done but we're not possible, but haven't done ambient AI between the doctor and a patient, those conversations. So AI has a profound role in the entire biostack, from organism down to DNA. So that's the second sort of if I was thinking about, if you could visualize a Venn diagram, so sequencing artificial intelligence.
Sahir Ali:And the third intersecting circle is that, for the first time you know since we have understood in mid-1800s that human disease effectively is about cells being lost and malfunctioning and the understanding that we need to control the cell. And if you can control the cell, we can control the system's biology with it. The cell programming space is very, very interesting If it combines with digital biology, ai, the idea that we can now have tooling to go and manipulate and control the cells, such as the CRISPR, the gene editing, the viral vectors, gene therapies, ips, stem cells. So I'd like to invest in that sort of intersection. So the criteria could be that you could be creating new therapeutics modality it's fine. You could be a platform company that brings a certain platform. But again, you've got to build a platform, not for the sake of building a platform, but think about who those partnerships are very early on. Is there a partnership that you can think about? Who can help build you with that? One of the learnings for TechBio world has been is that platform can be very exciting, hugely exciting, but if there aren't any partners and at the end of the customers, it's a. It's an interesting learning.
Sahir Ali:So you know we all adopt from that we can look at. You know, how do we actually have curative things, for example? You know how do we start to think about cures? How do we think about reversing disease? How do we think about disease modifying stuff? So that is again part of the biostack.
Sahir Ali:And lastly, I'll say that AI in medicine and AI in healthcare is very different than AI in medicine. In my opinion, healthcare is when you have to deliver care and all. It's a very difficult space. We tend to stay away from that, not saying we don't do it. But AI in medicine what can we do from troves of data in medical imaging and everything else? What can we actually turn them into digital biomarkers? How do we enable oncologists to say here's a risk score, here's how you better risk stratify these patients. How do we identify for pharmaceuticals that companion diagnostics, that works on rudimentary imaging HNE images, radiological images right, these are some real opportunities to have and real market opportunities as well. So this is worth deploying for. Yeah, so these are some of the criteria. I mean it's a very broad sport, but again, you need something like a biostack to kind of help you frame that. So it all comes down to that sort of stack.
Ben Comer:You mentioned Rebel a minute ago and I did want to just ask a quick question about that organization. I saw the announcement. Modi made a strategic investment in partnership with Rice University. What caught my eye was that the company is developing intelligent bioelectronic therapeutics. I wanted to get you to tell me what those might be.
Sahir Ali:Yes, well, you know it's offensive word saying combination therapies, and so here's, you have a therapeutic along with something that could be a device, a digital device, and these are, this could be a combination of those. So, drug delivery mechanisms. And how do we, how do we embed, maybe, a device that can control regulation of the blood? Uh, sorry, a drug in turn? Uh, I you know if we could, if we could have interesting devices that control, uh, how do we actually start creating new proteins within in vivo? I think these are, these are all the sort of purview of of that incubation in the venture studio, and I think these are all the sort of purview of that incubation in the venture studio, and I think these are things that are worth building for, because this could have a huge, tremendous impact on some of the very aggressive diseases and areas today that we just perhaps don't even have right treatments. And it's a bold one. It comes from Rice.
Sahir Ali:Omid is a fantastic scientist and a PI at RISE. Some of the technologies that they've developed, you know 100 plus patents. How do we turn those into companies? And that's going back to what I was saying the opportunity space in Houston. There's fantastic scientists here. In fact, there are institutions who have done over the lifetime 100 plus INDs.
Sahir Ali:It's just that I think we're just trying to put together an innovative sort of factory or lifecycle or a flywheel, and so I'm just playing my small part in this and I think you'll see some exciting things come out of Rebel. So very excited about that and excited about our investment there as well. And plus I'm very committed to building the ecosystem in Houston in ways we can, and I think Rebel in many ways is thinking about very similar to how Boston biotechs have in the last couple of decades right the flagships and the third rock of the world. I think there's opportunity to do something here, but also take some learnings along the way to do something here, but also take some learnings along the way, I wanted to ask you about one of the other companies that you've invested in Lila Sciences.
Ben Comer:They're working to develop scientific super intelligence. What would you say about that?
Sahir Ali:Yeah, that's probably the most interesting and exciting investment, just given where the markets, where the I guess the general exuberance is. I mean, if you think about the current state of AI, right since 2020, when the chat GPT sort of took everybody's imagination, what we have done very well is in what I call in the in silico world, in the software software world, if someone was to prompt something magically, you know, a set of sequences come out that appear like an essay, it appear like an analysis of something, whatever you want to frame it, as it can generate a movie, it can generate images, it can operate on images and understands again, from pixel to to text, to everything, anything that can turn into a bunch of sequences. Great, either llms or these diffusion models just tend to do very well science. So, if you want to make a super intelligence, on the science side of things, though, science has a different sort of a flywheel or a path where someone has to create a hypothesis, that has to go and experiment physical experimentation, physical access to the world to be able to do that, and then you collect data, you analyze it and then you repeat that loop. So if we were to think about what is the role of agentic AI or any sort of AI framework in creating science. So you have to create something called a super intelligence, and that super intelligence has to have access to the physical world.
Sahir Ali:And that's what Laila's sort of core thesis is that if we have a super intelligence layer or AI layer, which is a software layer, but what if it had access to something called AI factories where it can actually conduct experimentations on that stack from maybe DNA RNA material sciences, on that stack from maybe DNA RNA material sciences, how do we turn that into sort of a full, sort of an AI factory? And so what could be the business models of that? I mean, I don't want to go into details of that, but one couple of things that could happen is we could improve therapeutic indexes of existing therapies. We could find new materials that could be better soluble, or it could have different sort of Z index, as we call it in chemistry, which would require, say, 300 scientists or 100 scientists. Here you could just have two scientists who can do proper prompt and let the experimentation loop happen. Right?
Sahir Ali:This is a fundamental sort of shift in thinking about how we do AI science, so AI-based science. So that's one, and the second part I'll say is that I think it's a bigger, bolder sort of plan to what I think seven or eight years ago, openai and DeepMind were saying is that if we could tie reinforcement learning with sort of a model that can just generalize, well, on multimodality, what can we do and I think it's a similar sort of question here is that if we were to build this sort of platform and sort of create that loop, what could happen? So it's a very exciting thing. I think you'll hear some really wonderful things coming out of this one, but I'm very super excited about Lila.
Ben Comer:Well, I'm not going to ask you to put a specific timeline on when that super intelligence might emerge here, but I do want to find out what you think AI-driven science, or just new applications of AI, might be capable of in, say, the next five years.
Sahir Ali:Yeah, so in science, one of the things that, if you think about the past, scientists used to use rulers and then the microscope, then increasingly electronic sort of tooling, right the sequencing and all of that what is the scientists of today and future is going to be able to do? They will be able to use these sort of platforms where they can quickly iterate on an idea, quickly experiment. They don't have to keep having to pipe it, you know that sort of the boreas sort of things. So I think we're sort of at an early stage of can we, if it has taken humanity to go from discovery of electrons to electricity, to say transistors, 60 years, can we sort of use our creativity in the same way but reduce that timeline by, say, 10 years? And that's what scientific superintelligence allowed us to do. That's my hope, that's the, that's the opportunity.
Sahir Ali:Space is that, yes, we, we have done phenomenal progress, but it comes with the time and lots of sort of human skills and all of that. Scientific superintelligence just really accelerates all of that. I think it could perhaps put us in a whole different path that we just haven't. And also remember, science is a unique field where it's not about what the public data is right. It's also about proprietary and data that doesn't exist today perhaps, and that's where I think that intersection of automated labs and being able to have an LLM capability, the prompting to be able to do that, create that flywheel. I think there's a lot there, but I think you know the market Synthetic data will be important you think, yeah, synthetic data will be important.
Sahir Ali:Yeah, synthetic data is, is, is extremely important. I think you know you can see some examples in pathology and and and especially what we call imbalanced, uh areas of of data, where you know there's only one, uh one case that is positive out of you know, say, a hundred thousand, you know, glioblastomas of the world. Right, synthetic data could be very supremely useful in medicine, but in many other ways. So I think, uh, that's that's kind of my hope, is that, um, I think, and also there's a clear space to build. Right, while everybody's excited about generative ai in general sense, and enterprise and consumer and all, I think this is a space ripe for innovation. Uh, capital markets. I think, uh, what I call engineering of life has always been the most important part to us.
Ben Comer:So super intelligence in five years.
Sahir Ali:Yeah, I mean, I don't know what to say about predictions. All predictions are bad. So I will say that I think maybe in five years we might hopefully have a moment where we can start to capture people's imaginations and say that this is something that ultimately will make a material difference to people. Ultimately, this space that most of us are excited about, investment, is because we have to remember the direct implications are that people's lives. We have to remember the direct implications are the people's lives. We can save lives. We can alleviate pain and suffering that comes about with these aggressive diseases, and it's not the person with the disease but there's an entire sort of society, family that suffers with it. If we could create preventative and curative medicines, it can also lessen the burden on society, the financial toxicity, and there's lots at play. And so, again, we don't want to create technology for the sake of technology, but what is it ultimately going to help? So I don't have a five-year prediction or 10-year.
Sahir Ali:I'm hoping that in five to 10 years we are going to start to shift towards precision medicine. Again, precision medicine is about right data, right target, right patient, and all of that has to do with being very precise. Personalized, generative is the term in there as well. So platforms like Lila can go beyond just medicine, but also in material sciences. But I'm more excited about Lila doing something in the space that I'm more excited about and then other companies that are building not just ours but the whole ecosystem. I think we're in an inflection point of what I think truly the intersection of tech, bio and medicine can do for us.
Ben Comer:Yeah, and we're running short on time here, but maybe my final question for you, sahir, is just about that the future of tech and bio and maybe, getting back to Modi, why you think new financial engineering will different approaches to strategic investing as tech bio, you know as a kind of individual industry, you know continues to progress.
Sahir Ali:Yeah, well, you know, as I said, the space has become mature. So now you can actually have some interesting financial engineering. For example, private credits, royalty plays are sort of 10, 15-year concepts. There are funds who are just looking to underwrite the entire phase three with different financial stacks offering mix of private credits, mix of royalties. You come into early stage.
Sahir Ali:States like Texas, maybe some North Carolina, california, led the way with stem cells back in the day. What roles do state play in funding while the national funding is in some kind of a disarray? So that has changed. In fact, we are forced to think about what does curative medicine look like and how do we price those right? So the crispers of the you know the sickle cell and crisper, one, treatment that can just reverse the disease, our, our financial models for reimbursements and everything else is focused on not curing but managing the disease. So so new ways of new. We have to think about new ways. Even in the venture capital world we have not had technology that was moving at pace that is, in this space. We've had the breakthroughs of AlphaFold and we're building on top of that. We have artificial intelligence that's just pretty much applied across the board, everywhere. So that is going to have a big compounding effect. We're going to need to figure out how do we think about the value of some of these assets in interesting ways. I mean, these are platforms that we've never seen before. Traditionally, we think of biotech as a certain molecule and what kind of peak sales it's going to have and a few other things, but beyond that we're going to have we have new technologies and new sort of platforms. So there's an exciting, exciting space.
Sahir Ali:Also, I don't want to underscore, I want to underscore the consumerization of health. It is, it is absolutely here. There is a generational shift between Gen Zs and millennials. You know, as myself being a millennial, we were considered the online first. Gen Zs are digital, everything. They may be okay with the idea of an AI doctor, than say you and I, you know that's not the era, that's not what we've sort of mentally tuned ourself.
Sahir Ali:But you think about the alpha. I don't know what the generation is called before, but after. Gen Zs yeah, gen Alpha, yeah, gen Alpha. So Gen Zs are now entering workspace and in the next five, six years they will be in positions where they're going to start to make a decent amount of money. There will be a big consumer market. Alpha Gen similarly. So I think, if you think about the social media's rise, it was millennials and then taken over by Gen Zs. So what is? And I think in this space I mean I was reading a report just two weeks ago that in Gen Zs and some later millennials they're not drinking alcohol as much and this is the first time in 90 years we've seen that sort of data- so, there's consciousness of being healthy.
Sahir Ali:There is a digital first rise of function health of the world. I think consumerization of health is a big part of my prediction of the next five to 10 years, and I'm taking some bets there as well. So let's not forget about consumerization of health.
Ben Comer:Well, I really enjoyed speaking with you. Sahir, Thanks for being here.
Sahir Ali:No, thanks for having me. This was a great conversation, really enjoyed it, thanks for being here.
Ben Comer:No thanks for having me. This was a great conversation. Really enjoyed it. We've been speaking with Sahir Ali, phd founder and general partner at Modi Ventures. I'm Ben Comer and you've just listened to the Business of Biotech. Find us and subscribe anywhere you listen to podcasts, and be sure to check out our new weekly videocast of these conversations every Monday under the Business of Biotech tab at Life Science Leader. We'll see you next week and thanks, as always, for listening.
