Biohub: The Future of Biology is Open-Source with Co-Founders Mark Zuckerberg, Priscilla Chan, and Head of Science Alex Rives

24:52But I think that the progress with AI is really, is obviously, you know, very exciting on this. The other thing that I'd say, just adding to what you were saying a second ago, is we really look at more kind of systems than specific diseases. So, for example, one area that seems really important to understand is inflammation. We talked about this a bunch. This is a big focus of the Chicago Biohub. There's a lot of data on that. It seems quite clear that it's connected to a bunch of different diseases, but rather than studying the specific diseases, we think that by trying to understand inflammation more broadly, that will make it so that other companies that can then use these tools can work on specific therapies.

25:42Another example is, I think that the immune system, I think, is a very good case to study for some of the work that we're doing in cellular engineering. And when we kind of ladder up from proteins to cells to whole dynamic systems within the body, I think that that one makes sense. I mean, it's sort of privileged. It can, you know, the cells can travel around through the body, all that. You know, so obviously that has a big part in addressing different diseases. How do you make the immune system function better? But exactly how do you connect that last mile, I think, is going to be more something that biotech or other academics individually studying things will be better suited to do.

26:19So this is kind of how we think about building out the tool set that just helps accelerate all these other folks. Whether the timeline is 10 years, hopefully, you know, less than 100 now, I think it's useful for maybe your average doctor or patient, human being, everybody's a patient, to think about like what's externally visible in the progress here. You worked with patients for a long time at UCSF. Like what should doctors look out for? What should people look out for if you're actually accelerating progress? This is the part, you know, I'm super excited about the progress, especially with this launch that Alex and his team have put forth.

27:00And I think it's very clear that science is going to start moving pretty quickly. And I think the thing that's less clear to me is exactly how we translate to the clinic and what that looks like. And I think what has to change is actually the way we do clinical research. research and my hope is that we're really shortening the distance between bench research and patient impact. But there's a lot of steps there that we need people who actually take care of patients to think creatively and think about how to deploy safely. And that's a gap that we have some work in. We partner with Jennifer Doudna on a CRISPR cures program at UCSF.

27:50So we're dipping our toe in understanding how the deployment of research needs to change, given how quickly research will be progressing. But that one is still, I think, is still shaping up. Maybe I could say something about our most recent launch, because I think it also kind of We should actually explicitly about it. Yeah, so, you know, because I guess it was just a week ago about now. So we announced the new ESM fold. And so this is basically an open system for scientific discovery in protein biology. It's a world model of protein biology that's been trained. It's a language model base. So it's been trained on billions of protein sequences, kind of learns these emergent representations of protein biology.

28:39And then we can use it to make predictions of atomic resolution protein structure. And we can use it to, and it's really fast. So it's blazing fast. So it's kind of, you know, illustrating this Pareto optimal frontier of kind of speed and accuracy in structure prediction. And so this allows us to kind of characterize, you know, really vast kind of stretches of the protein universe. So we folded over 1.1 billion proteins and predicted their structures and identified kind of features connecting all of them through mechanistic interpretability. But I think the thing that I thought was most exciting about this model is it's this really general model of kind of protein biology.

29:24And so you can use it as a world model. You can actually really start to search the space of the world model to design new proteins. And it's really hitting state-of-the-art across pretty much every structure prediction benchmark, and especially on protein-protein interactions and protein-antibody interactions, which is really critical for therapeutic design. And so what we found is you can actually now use the model to design proteins and design actually single-chain antibodies. And so you can do all of this digitally and then, you know, really in a small number of experimental trials, basically like a 96-well plate, you know, select from hundreds of thousands of trajectories digitally, actually synthesize, you know, 96 proteins, test them in the lab in a really kind of short, easy experimental cycle.

30:17cycle and we found nanomolar binders there and so you know that's really the level um for for therapeutic activity so it's it's it's really i think showing that you can have these kind of general purpose models um that can you know we didn't design a model for antibodies we didn't design a model to you know to be able to bind one particular target you know we just designed a model that could understand proteins and you kind of get protein design as an emergent property And then I also think it illustrates this kind of the power of open science and open source, because, you know, we release this as basically an open discovery engine.

30:57And so really, anyone can build on it. And so it takes what are these really intensive laboratory experiments where, you know, you have to screen through hundreds of thousands or millions of antibodies and high throughput screens in the lab. and you can really just kind of spin up an instance and compute and now be able to generate antibodies. You should say more about sort of like we took that data when we did an antibody screen and then we validated it. We looked at PDL in cells and then we looked at it under the cryo-EM and sort of how all that validated what you were seeing in the models. That's right.

31:35Yeah. So, I mean, I think it's really critical to actually go and characterize these molecules in the lab. And we have a structural biology center here. We have incredibly powerful cryo-EM microscopes. And so we're really able to kind of look at these proteins biophysically and functionally. And so we designed proteins for several therapeutically relevant targets and we're able to confirm their function. It's delightful when it works the way it's supposed to. Yeah, that's very amazing. We're able to look at the structure also. So you can see atomic resolution, kind of the binding interface is correct.

32:15I know a lot of your work is really focused on basic research and kind of building out the fundamentals. If I look at actual translation into drugs or drug development, often a clinical trial will be 15 years. It'll cost$1.5 billion. About$50 million of that often is the molecule in preclinical work, and it's a few years of work. And the other$1.45 billion in decade plus is actually the drug development side of it. a lot of that seems to be gated on some regulatory issues, some of it's recruitment, it's a variety of things, but a lot of it also has to do with the failure of drugs and trials around things like absorption or toxicity or things like that.

32:46Have you considered at all tackling that other chain of sort of molecular design and thinking, or is the primary focus more on the basic biology and sort of the initial sort of molecules? I mean, at least my hope in building this comprehensive model of how cells work is actually also being able to predict off-target effects. I think you can do some of that actually with biological models because right now some of the off-target effects are we just didn't know, you know, your kidney cell also expressed this receptor. And then when we test it in human, like we see it happening and we see renal toxicity.

33:22And so being, and if you have a single cell atlas that looks at all the different cell types, some of which actually were not predicted before we modeled them, you can start looking at which cells actually do have receptors for the target you thought you were exclusively targeting and be able to predict some of these downstream effects before we get into the human trials. And I think that that's actually one of the more exciting applications of like a transcriptomic model to understand actually how the different cells will react when you intervene and do something. But I think when you think about delivery mechanisms and patient care, that's where you start having to be creative about when you ask, what disease do you want to care first?

34:12There are certain diseases that will be easier to deliver a therapeutic to, or the risk-reward makes more sense. And I think we were all inspired by Baby KJ, I think last year now, when the team at CHOP was able to deliver a CRISPR therapeutic to edit a mutation that he had that would have inevitably led him to significant neurotoxicity and altered his life. But we were able to, that disease was very carefully chosen because we needed to target his liver cells and if we could easily deliver a product that would work in his liver. And I think that's when the creativity, the wherewithal to choose the right applications can help us unlock the first applications.

35:10Maybe something just to add to that also, you know, because, I mean, kind of you describe the conventional, you know, drug development process, right? And I think, you know, these kind of tools have the potential to have a lot of impact on that process. But, you know, what's interesting is to really start to think about kind of the new paradigms that can open up. And, you know, what does it mean if you can, you know, the barrier to develop a drug, to design a molecule, you know, to kind of get through all of those stages is so much lower. And so you have programmable biology and you can, you know, really start to, you know, create a medicine for every individual patient.

35:45I think that has enormous implications for how we, you know, how we do drug development and what the future of medicine looks like. It'll be an exciting day when the FDA accepts like a virtual clinical trial for the phase one or something, or, you know, that's based on some response view of that person. Yeah. Or even short of that, like thinking about the specific like mechanisms where you see this acceleration, like I imagine if people feel like they can predict impact in kidney cells or have a stronger perspective on tox because they have this broader understanding, they'll be willing to try many more programs, right?

36:21Yeah, the recruitment could also change. And we have this program, Rare is One, and the basic idea is that a lot of people focus on the most common diseases, but there's this long tail. And the economics don't quite work out for companies to focus on those diseases. But if you can make it so that the groups of patients can kind of come together and organize and say, hey, we would take an experimental drug on this, then it actually, because of the cost that you're talking about and how that's a huge amount of the overall cost, if you can flip that, then it actually makes it, so the economics make a lot more sense to then if you can generate something more easily and you can pair it with a group of people.

36:59I think one of the interesting things from science and engineering is that often you can hit your head against the wall on the common problems and in this case diseases, But a lot of times you like learn a lot more about a system from finding some kind of rare or like weird side thing that's happening. Edge case. So I don't know. I think that that's like always been kind of an interesting part of this that actually connects pretty well to this because now you're going to be able to enable a long tail of new kind of ideas to get tried and enable them to potentially get tested more easily. Yeah, that's a really good point on rare.

37:37in our rare disease cohorts. First of all, they're incredibly inspiring and powerful, but patient groups are self-organizing patient registries, natural history registries, biobanks. They're organizing their own clinical trials. There's gene therapy that one disease group has moved forward over the course of like, I want to say like three to five years rather than decades. And the speed is so fast because the patients themselves have organized the resources that a scientist or a clinician might need. And it's incredible. But I think to some degree, you're going to need something like this because there are going to be many more new things that can get created.

38:20But that doesn't mean that for the general population that you're not going to want the same level of vetting that we've had historically, but making it so that people who want to be on more of the frontier have the ability to do that is, I think, also going to be pretty helpful. Yeah, letting people opt in to be part of trials, I think, is one of the big shifts that is starting to happen, but could really help accelerate biology in general. All three of you have mentioned at different points, like the power of open ecosystems in such a large space. I think some of that logic around open source and the breadth or diversity of data collection that you were describing, it should also apply in the like language model world and the multimodal AI world.

39:01Like, do you think that's right? Does any of the work you're doing here change how you think about AI and meta? I mean, I think it's sort of a similar philosophy overall. And, you know, Priscilla was talking about this, that, you know, a lot of our focus is building tools that empower individuals to do things. And that's sort of a common theme across a lot of the things that I work on is just kind of putting the technology in individuals' hands. We don't believe in this like very centralized future where there should be a small number of institutions that basically are advancing all of this stuff.

39:35Our vision is not that there's gonna be like some central super intelligence that solves all of science. I think like people are really important and I think will be more important in the future and giving people more tools to be more productive is gonna be like a critical part of any kind of positive future that both, and that's how progress has always been made historically, right? It's not through centralization, it's through empowering individuals to try things that are somewhat out of the mainstream that other people didn't think were good ideas because they thought they were good ideas that already had been done.

40:07So I think that that's very central to the whole ethos of, I mean, to some degree, it's like why you create something like social media, right, to give people a voice. It's, you know, I think a lot of the stuff that I care about in terms of empowering people with individual AI. Open source is one instantiation of it. It's not the only way to do it. It certainly is one way that you basically are saying, we're going to take this technology and put it in everyone's hands. In terms of science, I think it really makes sense. And we're deeply committed to open source. There are obviously interesting considerations on this that are important too, because there's a lot of considerations around biosafety and things like that that we're going to need to balance and think through how to handle.

40:48But I think overall, this is like very deep in the ethos of the work that we're doing both at Biohub and like probably a theme for a lot of the stuff that I do is just like we believe that a positive future is one where you build a technology as a tool, you put it in individuals' hands, and that's kind of how society makes progress. You have this, I think, an incredibly ambitious mission at Biohub. And yet, you know, the AI scientists that work here could also go work in commercial enterprises. How do you think about the talent and like how to bring people to Biohub? I mean, where do you want to start?

41:28You know, yeah, I mean, it's a very hot market for AI researchers. But I think that part of what that means is that there's a lot of demand and they're very in demand and can work on the things that they want to work on. And I think this gets back to this point again about frontier AI and frontier biology, right? So yeah, I mean, I think like the AI researchers who work here could go work on language models or things at any of the main labs, but those labs don't have the frontier biology part attached to it. So I think that there's like also just very large mission component of this, which is like there's an ability to do this unique work here that you just can't really do at the other places.

42:17So if that's what your focus is, then I don't actually think that there's any other organization in the world that's doing both the frontier biology and the frontier AI. Yeah. Why are you here, Alex? I mean, I think it's really simple. Our mission is to take care of disease. And I think, you know, it's just such a powerful— And you say it with a straight face. In a less than 100-year timeline. It's very serious now. There's no more. Yeah. Yeah. It's a really powerful mission. And I think, you know, you, yeah, I mean, it's just, you know, scientists, I think, are very motivated by that. Yeah. It's something people are deeply motivated by.

42:57And I think, you know, we're at this moment in time where that actually seems like something that can be achieved. And I think, you know, we're building a really unique place where we're tackling that problem. And, you know, we have the resources, I think, kind of the right things to actually really go after that and do that. Yeah, I mean, that resonates with me as somebody who talks to and hires a lot of research scientists. They want to know if you have the data, if you have the tools, if you have the compute, if you have the talent, and then what the mission is. And so I actually think that's super competitive.

43:34The other thing is that you don't need a very large team, right? So I think it's like an interesting thing about the world is that people care about different missions. And that's good. I think that's like part of the whole, and part of why giving, building these tools and giving people the ability to explore what they care about, whether it's like across science or just across everything, is like such a powerful way to make progress in society is that people care about different things. And in order to make progress in AI, you don't need like many, many hundreds of AI researchers or thousands or anything like that.

44:08I think you can really make progress with, you know, a very strong group of a dozen or a couple dozen people. And yeah, I mean, And finding people who like care about this mission is not a particularly hard thing. I mean, this is like a super important thing in the world. So I think that that's, yeah, it's just kind of a cool thing about the world is that people obviously are drawn to different missions. So I think the like simplest mental models that folks have, even if they're paying attention to the space, are essentially like, okay, you know, structure prediction models for proteins and protein-protein interaction models.

44:41And then so there's this one piece, which is fundamental understanding. And then there's this like theory of someday we're just going to be able to like zero shot things into either the clinic or the clinic with much, much better hit rate. What needs to happen for us to go from ESM fold two to this other piece? Is that feasible? I think that's a great question. I mean, I would say that I'm really optimistic on that. So I think, you know, on the one hand, you know, these are problems that historically, you know, people could spend kind of an entire career working on. Like, how do you figure out how to effectively optimize a drug?

45:17How do you get it, you know, get it through preclinical? How do you do the early safety? I think that, you know, when you have a new scientific paradigm, kind of, you know, questions that were once hard kind of become simplified through the new paradigm. And so I'm very optimistic that kind of many of these core problems will be solved kind of in an emergent way through these models. And I think one great example of that is toxicity, whereas if you can kind of really digitally kind of simulate everything and be able to predict, you know, where a drug is going to distribute and bind across the human body.

45:55You know, like you kind of have the beginning of a solution to that kind of problem. So I think that once you have these kind of accurate representations at the molecular level, you know, we're going to start to see really rapid progress on a lot of these core problems. What is the most exciting use or experimentation with the models you've seen in the last week since release? Yeah, I mean, it's just been great to kind of see it get integrated in all kinds of things. I think one of the really interesting things that we've been seeing is people kind of connecting it with agentic systems to just kind of do automated design and kind of just automate that whole process.

46:35So it's really, I think, another example of how you can kind of see bringing together Agentec and Frontier AI with, you know, the ability to have a world model for biology and actually reason about biology and, you know, really kind of start to automate the entire design process. Are you taking, you know, how do you decide what the next step in the research agenda is? It's like world model for biology. And then I could, I'm just gonna be very coarse here. Like I could scale it up. I could add more data. I could like adding data is a non-trivial thing in terms of new methods and domains. Like, what is, do you take input from the larger ecosystem about, you know, how people are using it and what would make it more useful?

47:19Or is it really like we understand, like, the next step of structures or coverage that we're looking for? I mean, I think there's two things. So, like, we have a view on kind of the next big challenge, which I think is, you know, the virtual cell. And, you know, really being able to kind of ladder up the hierarchy of biological complexity to the cell. Sorry, very basic question. Yeah. virtual cell model. Like, what is the input and output, I should expect? Yeah. I mean, I think there's different views on that. But I think kind of what you ultimately want is a system that can really model each of the levels of complexity.

47:54So, you know, the proteomic layer, the genetic layer, the transcriptomic layer, and connect that to the phenotype. And you need enough generality so that you can ask the model questions about a new intervention in a context that it hasn't been trained on and kind of get an answer from it. And the gap that we need to close as a field is being able to really make those predictions that can generalize. So that's going to require enormous effort to generate data. Yeah, and then, I mean, in terms of what you decide to do next, I think this is like a pretty normal process of constraint management, right?

48:35I mean, it's like, I think every lab in every field across the world probably feels compute constrained. I think that that's probably true here too, right? It's like, so, I mean, I know like, you know, there's always questions like, okay, should we double down more on advancing the protein piece? Should we do more of the cellular stuff? I think that those are kind of ongoing debates in terms of how you sequence that. And then, yeah, within that, there's kind of being at the Pareto frontier about how much you want to train the different models in order to like in the size of the models is also dependent on the scale of the data that you have for obvious reasons.

49:09So yeah, I mean, I think there's some of that is just where you want to be on the curves and the normal constraints. But I think that this is like probably the same process that like any research organization goes through of like you want to go in all these different directions and you're just trying to constraint optimize and make enough progress to do world-class work at one thing at a time while planting some seeds that can blossom over the next couple of years as well. Yeah, this has been the most dynamic period of technology at least I've seen over my career. I mean, it's so exciting in terms of everything that's happening with AI.

49:42And every week there's something new that's changed. Are you tired or invigorated? I'm both. I feel like everyone feels. I feel like everybody's in this manic phase. Yes, it's a combination of invigorated and exhausting. Yeah, it's wonderful. And so I guess, you know, things are very unpredictable right now. It's really hard to know what's coming. We have this almost like early signs of experimentation on the model side with agentic flows that we're starting to see in really interesting ways. Models starting to help more and more with models. That's still very, very early days for that. If you're thinking back five years from now and you were to define what success was relative to your efforts, and I know things are very dynamic, things changed a lot, but you have this common thread of tooling for the biohub.

50:27You have a common thread of empowering scientists at scale. You're looking back five years from now, is there a specific thing that you really want to make sure that you've accomplished or achieved or a primary goal? Well, I think we have a pretty clear view of this hierarchical set of world models that we want to build around biology. And the other part of that is that we want to do the highest quality work in the world. And I think we're basically set up to do that between having a world-class AI research team and this collection of biohubs, which are world-class life sciences research organizations.

51:01I think that that's fundamentally a setup that no other organization in the world has. But, you know, you can have a lot of great ingredients and that doesn't guarantee that you succeed. So, I mean, to me, like five years from now, looking back, I think, you know, I'm sure other labs or efforts will try to produce like things that approximate what we're trying to do. And I just think that we should be able to do something that is meaningfully better and a unique intellectual contribution to the world, right? I think that that's kind of what you, whenever you do any kind of research, That's what you're trying to do.

51:34All right. So yeah. So if we do that, I think we'll all feel very good. I would also expect that at some point we'll just start seeing a lot more idea generation from the people using the models. But I have enough faith that that part will materialize that for me, it's more just about like making sure that we do world-class work. And I think if we do, like the rest almost will take care of itself. Very last question for you. Snapshot of it's mid-2026. What's the biggest update in your own thinking about Biohub or the domain from the last year? Well, from the last year, I mean, you joined in the last year.

52:07I mean, I think the biggest thing that we basically rotated, and I think in the last year, we basically kind of formalized that Biohub is the main focus of our philanthropy. So I think this has been a very big shift. but Alex and the team coming in I think has been interesting not only because it's it's a world class group right I mean you guys have worked together for a while I think also I mean you talked about how stuff is changing so much in the field I think one thing that's underrated is like this is like a extremely talented group of people who also are like know each other and work well together and like are stable and good and like I think that that also is underestimated in terms of the compounding benefit of like people being able to like work well in a stable environment over time.

52:54So I think that that's a really important piece. But part of what we wanted to do was prior to Alex leading the effort, the previous leaders of the biohub were basically primarily biologists who were interested in technology, right? And now I think we, this is the point where we really flipped that, right? Where, I mean, obviously you have a background in biology as well, but like you are primarily an AI researcher who has a background in biology. I think that that's like a deep reflection on kind of the way that we expect that this is going to kind of drive more value in the future. So those are probably the biggest updates in the last year in terms of the work that we're doing.

53:41I mean, it's a new leader, not just the leader, but a team that I think has been, is, is like a really good. And then, yeah, I mean, I think on the rest of the industry, it's like, it's on track. I mean, I think like, it's, it's kind of this crazy thing. Cause like when you have an exponentially growing curve, I think the way that an exponential curve feels is it's growing so quickly that the, the kind of emotional feeling is it can't possibly keep going. Right. Because like it's, cause it's just like, but, but I mean the nature of an exponential curve is it doesn't just keep going, it keeps accelerating.

54:14Great exponential growth is accelerating. So I think that that has all of these emotions and psychology attached to it. But I think fundamentally, when you look at the curve in the industry, the kind of fundamental thing is it is on track. It has remained on that curve, which I think has all these very profound implications for all of these domains. But certainly it validates and makes one feel very good about making a very big investment in the things that will play out if you stay on that track. And it seems like we are. So that I think is very good news. I think the most important aspect of what you're doing there is you're actually closing the loop with the actual biology.

54:55Because with code and research, it's closed loop systems. And so they're very fast to iterate. This is an open loop system. So you're closing a loop and that's really crucial to progress. Yeah. For me, one of the biggest changes with the strategy we're driving now and Alex at the helm is, you know, before we had amazing teams moving generally in the same direction and understanding like the potential collaborations and interconnectedness of our work. But now we are arms linked moving together. It feels very directed. It's very directed and it's very exciting. It's a little bit scary, but it's like truly a team playing off each other and trying to make progress towards this goal.

55:42And that has taken a lot of work, but also the maturity, our teams being able to have their work at a level of maturation where it actually does make sense to interlock. Amazing. Well, to teams being on the curve, thank you guys for doing this. Thank you for joining us. Thank you. Thank you. Find us on Twitter at NoPriorsPod. Subscribe to our YouTube channel if you want to see our faces. Follow the show on Apple Podcasts, Spotify, or wherever you listen. That way you get a new episode every week. And sign up for emails or find transcripts for every episode at no-priors.com.