“Curing All Disease by next century is too conservative" - Mark Zuckerberg

We just want to give tools to the whole scientific community.

We want to understand how biology works.

I want to understand the genetics of this person. I want to understand the

this person. I want to understand the risks they have to different illnesses.

My goal is to be able to treat the individual as an individual, understand the mechanisms and be able to intervene.

We'll have a bigger impact by getting this in more scientist hands quicker by doing it as open source projects instead. It's not just like there's some

instead. It's not just like there's some factory somewhere that you can pay to produce the data. You actually need to invent new novel scientific approaches.

The theory isn't that we're going to cure the diseases. We're not. It's that

we want to help accelerate the pace of progress for the whole scientific field.

We folded over 1.1 billion proteins and predicted their structures. And we

didn't design a model for antibodies. We

didn't design a model to be able to bind one particular target. We just designed a model that could understand proteins.

If we could design a protein to actually change the physiology, then we can actually cure someone.

Today on No Priors, we're joined by Mark Zuckerberg, Priscilla Chan, and Alex Reeves. We'll be talking about Biohub

Reeves. We'll be talking about Biohub and all their various efforts to now start applying AI at scale to do world models of cells and different levels of interactions across biology. Mark,

Priscilla, thank you for doing this.

Yeah, thanks for having us. This was

fun.

Alex, congratulations on new missions.

Thank you.

You guys made BioHub your primary philanthropic effort and then committed $500 million to this virtual biology initiative. Can you tell us a little bit

initiative. Can you tell us a little bit about, you know, why do that and how did you go from we should fund this to this is like who we are?

So, Biohub in its current form, we're super excited about. We feel like it's a really good fit for who we are and what we bring to the table and what we can achieve together. But this work started

achieve together. But this work started 10 years ago when we were thinking about how can we give back and Mark had Mark wanted to build an organization that

could cure, prevent and manage all disease by the end of the century. And

we had a series of hilarious meetings with scientists that like famous Nobel Prize winning scientists were just laughing at us. Is

that was that your starting line? We're

just going to cure all disease.

No. No. And to be clear, we don't think that we're going to be the ones curing the diseases. Our our goal was always to

the diseases. Our our goal was always to build tools that could accelerate the whole scientific field. That way, the scientific field collectively could cure all the diseases. But still, I thought that by the end of the century was a

stretch. Now, I think it's like uh too

stretch. Now, I think it's like uh too conservative. And so we kept being like,

conservative. And so we kept being like, "Okay, well, we had these series of funny awkward educational conversations where we're like, okay, but like why? Like why do you think it's

impossible?" And like, you know, just

impossible?" And like, you know, just being uh the the person in the room is just like, "Well, I don't know why. You

tell me." Finally, we got people to like they're like, "Fine, if you really must know." And we're like, "No, we do. It

know." And we're like, "No, we do. It

seems important." um it's you know they were like well we work in silos and um when you publish information doesn't get shared it gets locked up for long

periods of time and we don't have tooling you know they gave the example of like we build a great tool by one posttock in a lab and it lives on their

computer and when they graduate the tool is gone and they just it was what we heard was very hard to build shared tools to move science faster build the

shared knowledge base to quickly move science faster and that's sort of where we began in thinking about okay like if those are the problems like what can we contribute

yeah I mean so the original biohub model was basically focus on long-term tool development by bringing together engineers and scientists um across

multiple universities to focus on long-term tool development and it basically it like worked and you know we started off with um with CZI doing a number of different things and I think

over time we just felt like okay the science piece is really working and we just kept on investing more and more and more in it until now it is basically the primary and main thing that we're doing

and we've expanded the original San Francisco Biohub um to a handful now at this point there's New York there's Chicago um the real focus and the

unifying theme at this point is um is the virtual biology initiative around taking the unique um data sets that are

able to be generated um in order to model um effectively starting with the smallest pieces of of proteins but then eventually cells and whole biological

systems. But that's kind of how we've evolved is, you know, this this idea that um that we talk about around that some of this is an AI problem and you

want to build a frontier AI lab, but you need to couple that with a frontier biology effort that can do the work of um of of basically being able to uh

understand and get the data that you need to actually be able to build these models. Because unlike language models,

models. Because unlike language models, well, there's just like a lot of data out there on the internet. That's not

really the case with biology. I mean,

there are obviously a bunch of different data sets that exist that academia and scientists have generated over the the decades, but a lot of the stuff that I think we want to put into this, it doesn't exist, right? It's like you want

to be able to visualize things that people haven't been able to see before, which is why we're doing the the imaging work. You want to be able to record

work. You want to be able to record things uh that are going on inside the body, which is why we're doing the kind of cellular engineering work, right? you

want to be able to measure things like inflammation um in ways that haven't been possible which is why the you know Chicago Biohub is focused on building those kind of devices and being able to do that and that will fundamentally

create um new types of data sets that will allow new types of models and I think is just a very exciting thing that um going back to what you're saying if the if the scientific field it primarily

needs um kind of tool development that now is going to empower scientists across the the field to be able to do their work faster that's what we think we can provide through this kind of long-term focus on

tool development. But the I I think

tool development. But the I I think there's a fun throughine on where we started and you know bringing us to our work to uh with that Alex is driving now

is that our very first request for application RFA here was around single cell sequencing and um and we wanted to look at sort of like the RNA that is

transcribed in individual cells and that b that was possible but it was still pretty early on in understanding how different cells were expressing their DNA to the point where at the beginning

we were just funding methods like getting people to describe how to do it so that others could share that methodology and then that became um us funding the human cell atlas which is

now one of the largest um databases of uh single cell transcripttos. It was

getting hard for scientists to annotate the data. So we built cellby gene which

the data. So we built cellby gene which was like a very simple annotation tool that scientists could use to make use of that data. Then a community came around

that data. Then a community came around cellby gene built around cellby gene and started contributing more and more data that we had nothing to do with sort of

creating or funding or u making happen in the world. And now cellby gene is a corpus of knowledge that a lot of uh the um transcrytoic based models are based

off of and is used regularly by the scientific community. But still there

scientific community. But still there are always critiques like this is just stamp collecting like you're just gathering bits of knowledge well sorry bits of data um and we're not going to

be able to pull scientific knowledge and wisdom and insights out of and and we're like well we didn't have an answer for a while and then imagine our delight when

large language models uh became a huge topic of conversation that could make sense of large amounts of data and I just for me is What if we could actually understand how

biology worked? Um, move it from a

biology worked? Um, move it from a discovery based science to an engineering based science where we could systematically understand how living

beings, living cells worked and um, be able to understand why things go wrong.

And so when we saw that moment, we're like, this is it. Something really big could happen here. Alex, you were uh you started at Metaphair um but you were on

the path to you know you had assembled a team at evolutionary scale and you raised venture and you were making progress in your models. What was the pitch from Mark and Priscilla who said like that's actually the right way to go after the mission?

Well, I think for me it was really kind of the moment when I understood that um you know they they really saw this as as an integration of frontier AI and

frontier biology. And I think um I had

frontier biology. And I think um I had developed conviction that you know this is really a a new era of science that's that's just beginning kind of what's going to be possible with artificial

intelligence and you know we're in the age of information theory at scale and we have these systems that can basically kind of predict the next token and they can you know learn world models from

that they can learn biology from the data and so you know I I think that it just it was really clear that you know to build kind of that next that next

kind of institution for the next era, you would really need to have frontier artificial intelligence. You would have

artificial intelligence. You would have to have frontier biology. You would need to start to put those things in feedback and really have models that are learning from the biology. And I think you know

just and you need the right scale and the right people and so this this just really felt I think like the way to do that. There's a variety of different

that. There's a variety of different models that you all have been working on and I think it's kind of interesting because some of the earliest breakthroughs in biology were things like alpha fold where you know it was a Google model that showed that you could do protein folding at scale in a really

interesting way that people didn't realize was very tractable and this was pre sort of the really big transformer waves that came later and then you're working on a variety of different things at different scale right you're doing incremental molecular modeling and

protein folding you're doing cell-based stuff you're thinking about interrogating larger scale systems in biology how well do you think that extends from sort of the micro to the macro. You mentioned almost starting

macro. You mentioned almost starting with building blocks and building up, but modeling cellular behavior is very different from modeling protein folding.

The data is very different. The modeling

is different. I'm just curious like do you think it's all uh similar in terms of it's just data and you train stuff or do you think it's actually uh there's some differences in terms of how you actually have to deal with these systems?

I mean, there are probably some differences. I mean, you can probably

differences. I mean, you can probably talk more to the specifics around this, but like I mean, I think each layer is going to end up being somewhat qualitatively different, right? I mean

the the but you need to be able to understand the protein interactions in order to be able to understand how cells work. So you can't just go straight to

work. So you can't just go straight to cells in a way without understanding the protein modeling and then if you're trying to understand something like the you know the way the immune system works or a bunch of cells interact together um

then um you know it's tough to do that without first understanding cells. I

mean you might be able to at like a very high level of abstraction simulate a system but if you really want to like understand how it's going to work you kind of want to build the simulations at each level hierarchically. So that's

basically the approach that we're going through starting with the um the building blocks and the and the protein.

But yeah, I mean I think that there's going to be different types of data that you want to collect for each um the modeling techniques. I think we'll see.

modeling techniques. I think we'll see.

I mean that'll all keep on advancing across the board. But I do think that like a big part of the strategy is this view that you need to build it up hierarchically. And you know, one of the

hierarchically. And you know, one of the things that's unique about us in the space is we were very intentional that the the AI efforts and the wet lab

efforts were a single effort and we've done a lot of work to bring them together. And the really neat thing that

together. And the really neat thing that we can do is really try to pull and gather data that helps us connect um

across sort of the hierarchy. You know,

you can look at transcrytoics with space within a cell and look at where it's localizing. We can look at um

localizing. We can look at um translucent zebra fish and look at the development across uh different cells and when the brain develops. We have

sensors that allow us to look at cell cell communication and different molecules. And so we can be strategic

molecules. And so we can be strategic about the types of uh experiments and data we want to collect that helps us bridge across these that makes it so

that there's some connective tissue that helps drive the modeling that you know the modeling magic that happens.

Yeah. The reason I asked the question by the way is I used to be a biologist. I

have a PhD in biology and I worked in wet labs for almost a decade and everything else.

Are you looking for a job? Uh

um I I'll we can talk about that later.

At this point in my career, you know, I love my aggressive recording. I'm like I'm like Danny

recording. I'm like I'm like Danny Glover, you know, in the Lethal Weapon.

I'm almost at retirement. Um but I think um you know, one of the things that was always lacking was this integrative nature across the different layers of biology. And the developmental

biology. And the developmental biologists would work on their own, the molecular biologists would be doing different experiments. And so that's

different experiments. And so that's what I was curious about. Yeah.

Typically there's a reductionist view of biology and there's a systems view and those people didn't really work together deeply. And so one of the exciting

deeply. And so one of the exciting things about what you're doing actually is how you're bridging that. And so

that's that was kind of the basis for the question as well.

Yeah. And if I could add something there, you know, it's I think that you know we're in the age of this kind of information theory in biology. And so,

you know, there there are levels of of complexity and hierarchy in biology and kind of each level is is made up of and, you know, constituted by the lower

levels. And so, as you want to have that

levels. And so, as you want to have that kind of more complete description and you want to have systems that can really generalize and begin to actually answer, you know, experimental questions digitally that you could ask in the lab,

you know, you need to have kind of the right basis for modeling at every level.

And so I I think what's really unique about what we can do is to as as as as Priscilla and Mark were saying, you know, really build information at each of these different layers, collect them,

collect kind of those connection points, but then also really kind of do it at the scale that will reveal that underlying information architecture. And

that's going to be really critical to actually be able to build digital representations that can answer new experimental questions.

One of the things that inspires me most about this effort is really what Priscilla said, which is like, well, uh, there's so much we actually don't understand about biology and what if we could, which I think is actually very

different from lots of other incredibly interesting and useful AI problems we attack. We're like trying to replicate

attack. We're like trying to replicate human behavior and I'm like, a lot of that data is, you know, on the internet or captured and without pretending to understand all human behavior, you can predict a lot of it. I I thought one of

the most interesting things in your release was actually you know the like mechanistic interpretability stuff you alluded to which is can we actually extract new knowledge from um you know

what the model believes is happening right uh can you talk a little bit about that yeah I'm really excited about that so I I think you know in mechanistic interpretability kind of traditionally it's been applied to large language

models with the goal of understanding you know kind of what is the representation space of a large language model how does um compute things and does that really connect to you know what we understand

about um our intuitive understanding of of the world and so there's I think this really rich toolkit that has been developed to um to start to be able to ask those questions so kind of what does

that mean for for biology one of the classes of models that we train are these uh protein language models so they're really you trained on the codes of proteins and so anything they learn

about biology is is kind of emergent and we've seen that they can learn things like biological structure and biological function and that's just kind of emergent from this you know token

prediction training task. So you know as we think about like mechanistic interpretability in those models you know we're we're really seeing the unknown because the models have been trained on billions of protein

sequences. is they've been trained on,

sequences. is they've been trained on, you know, both known and unknown biology. And yet they're developing

biology. And yet they're developing these representations that start to kind of capture things that we can really see correspond to that reductive picture of biology that's been built up over the

centuries. So kind of you can you can

centuries. So kind of you can you can start to connect the dots between proteins where we kind of really don't know anything about them um with with proteins where we we do know something

because there's that kind of underlying structure grammar that's that's linking them in the representation space of the model.

Uh and at the extreme it could be you know we're going to understand systems in the body that we didn't before or the mechanism of action for a new treatment be because we can ask the model right interrogate that representation.

That's right. The hope is that you kind of really learn the underlying basis for how it's making the predictions and so you open up the black box and you can actually understand kind of the biology that the model is representing.

So a asking for a friend um uh you know you you guys all believe in uh venturebacked companies as a way to have impact on the world. Um, what was it

like collecting data on zebra fish or the span of the data or the wet lab work or just the scale? Like what makes this a better fit for this big nonprofit, you

know, ecosystem effort versus a venture-backed company?

Um, well, I think we just want to give tools to the whole scientific community.

And I mean like so I I think in order to have the biggest impact I mean part of it is just we're I mean it's not actually clear that we couldn't run it as a business if we wanted to. I just

think that we'll have a bigger impact by getting this in more scientist hands quicker um by doing it as open source projects instead. So um yeah I mean I think that

instead. So um yeah I mean I think that that's uh that's that's kind of the approach but I I don't know it's an interesting question. I I'm not sure

interesting question. I I'm not sure that I mean obviously you were doing it as a as a nonprofit company um a bunch of the modeling before then you run into certain issues I mean you have to raise a large amount

of money in order to build a compute clusters um you know I mean it's I think in a lot of ways the data is actually even more of a constraint and um because if you look at like the scale of these

models compared to language models they're smaller but they're smaller because the amount of data is less in order to get the data uh it's not just like there's some factory somewhere that

you can pay to produce the the data like you actually need to invent new novel scientific approaches to be able to do the you know for example the type of cellular engineering we're doing in New

York or the types of devices in Chicago which is why you know when we're talking about this concept of frontier biology and frontier AI the frontier biology is you need to do real science to advance different biological methods in order to

be able to observe the things that create the data that go to the model.

Um, so it's not just like an off-the-shelf thing that you can create.

Now, that's a pretty big effort. I don't

know that there are like that many things like that that are done as um as biotechs. I think it's just the scale of

biotechs. I think it's just the scale of the ambition of what we're doing, the horizon over which we're committed to doing it. I think part of the theory is

doing it. I think part of the theory is like if you're building tools that are this complicated, you kind of want to have a 10 to 15 year time horizon on on building out these efforts. And then the

scale of capital required. I mean, I guess there's no rule that said that you couldn't do it as like an incredibly well-funded startup, but I think that this just made more sense. And then it

also is is simplifying strategically to not have to think about how you're going to make money with the different things.

I mean, we just we want to get the models in people's hands. We release

them as open source. I think that that's like a very valuable thing to do. And

again, I mean, the the theory isn't that we're going to cure the diseases. We're

not. um it's that we want to help accelerate the pace of progress for the whole scientific field.

As the person least experienced with making money here, I would say that there you the sort of neutral nonprofit nature of our work actually helps

harness more people to enter this uh effort. Um, and to actually achieve the

effort. Um, and to actually achieve the mission of like understanding the totality of human biology and to cure, prevent, manage all disease, you

actually do need the entire academic biotech industry to come together and to work on this in a sort of unified way.

um in part because there's a lot of talent out there and it's it's not helpful to leave any talent uh exclude any talent from the effort and there's a

super long tale of diseases. Um there

are the common ones and even the common ones I think if you unbundle heart disease cancer neurodeenerative diseases even if you unbundle like

dementia or uh depression there are many many many subcategories that become more and more niche and that's not even looking at the long long tale of rare

diseases. those often get orphaned and

diseases. those often get orphaned and don't get brought along when we're sort of looking at what the most efficient way to impact the lives of many. But if

you sort of decentralize the effort and put the tools in many people's hands, you start getting people who are like, you know what, I am super interested in spinal muscular atrophy and that's something I care deeply about and if you

put the tools in that person's hands, they're going to be able to make progress. In a way, if you had to focus

progress. In a way, if you had to focus your efforts and make big bets, you probably wouldn't because it's just a

niche individual uh small group disease that actually will in turn if we can understand that disease process helps us unlock knowledge about a lot more about the human how the human body works.

Do you have any thoughts or predictions in terms of what disease areas this work will impact first? I know it's very hard to be predictive about these things, but just given the nature of the work and the nature of the models, are there areas you're most optimistic about in

the uh short to medium term?

I that's actually not how I think about it at least.

The way I think about it is like we want to understand how biology works. The

ideal world is you would say I understand I understand the genetics of this person.

So I want I want to think about people at the individual level. I want to understand the genetics of this person.

I want to understand the risks they have to different illnesses. I I want to understand the mechanistic connection

between say a diff a gene variant a protein and a disease process because if you understand that through chain then you can design a protein design a drug

bespoke to them and actually make an intervention and right now and I'm sure we've all had experiences being sick and if you have something that's even

remotely um non-standard you go you go into PubMed You look up a paper, you look up the supplement, and then you start going through the methods, and you're like, am

I represented in this paper, and we're just making guesses. We really have no mechanistic understanding. We're saying

mechanistic understanding. We're saying like, okay, you're kind of like these people that we studied, and this drug kind of impacts the pathway that we

think is implicated. Let's try and see if anything happens. And time passes and sometimes it works and sometimes it doesn't. So my goal is to be able to

doesn't. So my goal is to be able to treat the individual as an individual, understand the mechanisms and be able to intervene. And there are different

intervene. And there are different diseases that are at different stages of filling out that whole through line. And

so for some diseases, you just want to understand which genet gene variants actually cause disease and which don't.

And that is that in itself can be super empowering to patients. Um and if beyond that there are some diseases where we understand the chain we just can't

intervene and change a specific protein function. That's super exciting too.

function. That's super exciting too.

Like if we could design a protein to actually change the physiology then we can actually cure someone. But to me,

like that is just as exciting as understanding contributing to our understanding of like how someone gets sick in the first place.

Yeah. That's a very exciting vision because you're basically saying you can bring generalizable tools to provide very personalized things for each individual person. Yes.

individual person. Yes.

And that's the power of the approach is you have these big models that you build that can then apply anywhere. I know

that you mentioned earlier that you were going to try and cure prevent all diseases um within a hundred years and you mentioned hey it could actually be sooner now given all the advances in AI.

Do you have some thought of when we think we'll be closer to that goal or some?

I mean, I'm optimistic it'll be sooner.

I mean, I think that the thing that's complicated is that it's a dynamic system, right? So, if you fix something,

system, right? So, if you fix something, there will obviously be future things that you need to work on. So, I don't think that the current set of things that we're aware of are going to be the only things that need to get worked out.

But, but I don't know. I think that the progress with AI is is really um is is obviously, you know, very exciting on this. Um, the other thing that that I'd

this. Um, the other thing that that I'd say just adding to to what you were saying um a second ago is we really look

at more kind of systems than um than specific diseases. So for example, one

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 that's very it's um it seems quite clear that it's connected to a bunch of different diseases but we

don't 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. Um,

another example is and I think that the um the immune system I think is a very good um case to study for some of the work that we're doing in cellular

engineering and when we're kind of lad up from proteins to cells to like whole dynamic systems within the body. I think

that that one makes sense. I mean it's sort of privileged. It can the cells can travel around through the body all that you know. So obviously that has a big

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

function better? But exactly how do you connect that last mile I think is going to be more something that biotech or other um academics individually studying things will be better suited to do. So

this is like kind of how we think about building out the tool set that just helps accelerate all these other folks.

whether the timeline is uh 10 years hopefully you know less than 100 now um I I think it's useful for maybe your uh average doctor or patient human being

everybody's a patient um to 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 for us especially with this launch um that uh Alex and his team have put forth and I

think it's very clear that science is going to start moving pretty quickly.

Um and I think the thing that's less clear to me is um exactly how we translate to the clinic and what that looks like. And I think what has to

looks like. And I think what has to change is actually the way we do clinical research. Um and um my hope is

clinical research. Um and um my hope is that we're really shortening the distance between bench research and patient impact. Um but there's a lot of

patient impact. Um but there's a lot of steps there that we need um people who actually take care of patients to think

creatively and um think about how to deploy safely. And that's uh that's a

deploy safely. And that's uh that's a gap that we have some work in. We

partner with Jennifer Downper Cures um uh program at UCSF. So we we we're dipping our toe in understanding how the deployment of research needs to change

given how quickly um uh research will be progressing but that one is still I think is still shaping up.

Mhm. Maybe I could say something about our most recent launch because I think it also please we should explicitly Yeah. So you know cuz I guess it was

Yeah. So you know cuz I guess it was just a week ago about now. So we um announced uh the new ESM fold and so

this is basically um an open system for scientific discovery in protein biology.

It's a world model of protein biology that's been trained um it's a it's it's it's a language model based. So it's

been trained on billions of protein sequences, kind of learns these emergent representations of protein biology, and then we can use it to make predictions of atomic resolution protein structure,

and we can use it to um and it's it's it's it's really fast. So it's blazing fast. So it's kind of um you know,

fast. So it's kind of um you know, illustrating this paro optimal frontier of kind of speed and accuracy in structure prediction. And so this allows

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

protein universe. So we folded over 1.1 billion proteins and and predicted their structures and and identified kind of features connecting um all of them through mechanistic interpretability.

But I think the thing that that I thought was most exciting about this model is it's it's this really general model of of kind of protein biology. And

so you can you can use it as a world model. you can actually really start to

model. you can actually really start to search the space of the world model to design new proteins. And um it's it's really, you know, hitting state-of-the-art across um pretty much

every structure prediction benchmark and especially on protein protein interactions and protein antibbody 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 to design actually singlechain antibodies. Um 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. Um you know se select from hundreds of thousands of

trajectories digitally actually synthesize you know 96 um proteins test them in the lab in a really kind of short easy experimental

cycle. and we found nanomolar binders

cycle. and we found nanomolar binders there. And so, you know, that's really

there. And so, you know, that's really the level um for for therapeutic activity. So, it's it's it's really I

activity. So, it's it's it's really I think showing that you can have these kind of general purpose models um that 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 um this this kind of the power of open science and open source because you know we we release this as basically an open discovery engine and so really anyone

can build on it. And so it takes what are these really um 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 know you can really just kind of spin up an instance and compute and now you know be able to generate antibodies. you

should say more about sort of like we took that data uh when we did an antibbody screen and then we validated we looked at PDL in cells and then we

looked at it under the uh cryo and sort of how all that complemented validated what you were seeing in the models.

That's right. Yeah. So I mean I think it's really critical you know to to actually go and and characterize these molecules in the lab and it's you know

we have a um a structural biology center here. where we have um incredibly

here. where we have um incredibly powerful uh cryo uh EM microscopes and and so we're really able to kind of look at these proteins biohysically and

functionally and so you know we designed proteins for um several uh therapeutically relevant targets and we're able to confirm their their function and some when it works the way it's supposed to.

Yeah, it's very amazing.

We're able to look at the structure also so you can see atomic resolution kind of at the binding interfaces.

Correct. I 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 and pre-clinical work and it's a few years of work. And the other 1.45 billion in

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

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. Have 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 uh building this like comprehensive model of how you know cells work is actually also being able to predict offtarget effects. I

think you can do some of that actually with um biological models because right now some of the offtarget 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 uh renal toxicity and so being and if you have a single cell atlas that looks at all the

different cell types um 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 that's actually one of the more exciting applications of uh the like a transcrytoic uh model to to understand actually how the different

cells will react when you intervene and do something. Um and you know as I but I

do something. Um and you know as I but I think when you think about delivery mechanisms and um patient care you start that's where you start having to be

creative about um what you asked like what disease do you want to cure first.

There are certain diseases that will be easier to uh like deliver a therapeutic to or uh the riskreward is uh makes more sense. And you know, I think we were all

sense. And you know, I think we were all inspired by baby KJ uh I think last year now when um the team at CHOP was able to deliver a crisper therapeutic to edit a

mutation that he had would have that would have inevitably led him to uh significant uh neurody neurotoxicity

and um altered his life. But we were able to uh that disease was very carefully chosen because we needed to target his liver cells and if we could

easily deliver um a product that would work in his liver and I think that's when the creativity the um the wherewithal to choose the right

applications can help us unlock the first applications. Mhm.

first applications. Mhm.

Maybe something just to add to that also, you know, because I mean kind of you described the conventional, you know, drug development process, right?

And I 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 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 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

much lower. And so you have programmable biology and you can you know really start to you know create a a medicine for every individual patient. I think

that has enormous implications for how we you know how we do drug development um and what the future of medicine looks like.

Yeah. 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 personal view of that person. Yeah.

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 um or have a stronger perspective on talks because they have this broader understanding, they'll be willing to try many more um programs, right?

Yeah. The recruitment could also change and we we have this program rare is one and the basic idea is that a lot of people focus on the the most common diseases but there's this long tale 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 a 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 the overall cost if you can flip that then it actually makes it um set the economics make a lot more

sense to then if you can generate something more easily and you can pair it with um a group of of people. I think

one of the interesting things from you know science and engineering is that often you know you can hit your head against the wall on the common problems and and in this case diseases but a lot

of times you like learn a lot more about a system from finding some kind of you know rare or like weird side thing that's happening in edge case. Um, so I don't know. I I think that that's like

don't know. I 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 tale 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 um on in our rare disease cohorts. First

of all, they're incredibly inspiring and powerful, but patient groups are self-organizing patient registries, natural history registries, um bio banks, um they're organizing their own

clinical trials. There's gene therapy

clinical trials. There's gene therapy that uh one disease group has moved forward over the course of like I want to say like 3 to 5 years rather than

decades. And the speed is so fast

decades. And the speed is so fast because um the patients themselves have organized the resources that a a scientist or a clinician might need to

and it's it's it's 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. But that

doesn't mean that for like 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

historically. But making it so that people who want to be on more of the frontier have the ability to do that is is I think also going to be pretty helpful.

Mhm. 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 um at different points like the power of open ecosystems in such a large space. Like I

think some of that logic around open- source and the breath or diversity of data collection that you were describing. Um it should also apply in

describing. Um it should also apply in the like language model world and the multimodal AI world like do you think that's right? Does any of the work

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 our focus is building tools that empower individuals to do things and that's a sort of a common theme across a lot of the things that that I work on is just

kind of putting the technology in individuals hands. We don't believe in

individuals hands. We don't believe in this like very centralized future where there should be a small number of institutions that um that basically are are advancing all this stuff. Our vision

is not that there's going to be like some central super intelligence that solves all of science. I think like people are really important and I think we'll be more important in the future and giving people more tools to be more

productive is going to 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 um through

right? It's not um through centralization. It's through empowering

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 have been done.

Um, so I I think that that's that's very central to the whole ethos of um 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 the stuff that we 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. Um, 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. Um, there are obviously

source. Um, 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

how to handle. Um, but 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 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 um uh this like I think uh incredibly ambitious mission at Biohub and yet you know um the AI scientists that work here could also go work in

commercial enterprises. How do you think

commercial enterprises. How do you think about the talent and like how to bring people to Biohub?

Um I mean where do you want to start? I

I think you know um yeah I mean it's it's a very um hot market for AI researchers but I think that part of the part of what that means is that um

there's a lot of uh demand and you like they're very in demand and can work on the things that they want to work on.

Um and I think this gets back to this point again about frontier AI and frontier biology, right? So if um so yeah, I mean I think like the AI researchers who work here could go work

on on language models or things at any of the the main labs. Um but those labs don't have the frontier biology part attached to it. So I think that there's

like also a 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. Um if so if you're if

other places. Um if so if you're if that's what your focus is then this um then you know I 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 it's really simple.

Yeah. Our our mission is take care of prevent disease and and I think you know there's it's it's just such a you say with a straight face in a less than 100 year timeline.

It's very serious now. There's no more that's Yeah. Yeah. It's it's a really

that's Yeah. Yeah. It's it's a really powerful mission and I I think you know you um yeah I mean it's it's just you know scientists I think are very motivated by

that. Yeah. It's it's it's something

that. Yeah. It's it's it's something people are deeply motivated by and 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 um where where we're we're tackling that problem and you know we have the resources and I think kind of

the the right the the right things to actually really really go after that and do that.

Yeah. I I mean that resonates with me as somebody who you know talks to and hires a lot of research scientists. They want

to 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 uh uh I think that's super competitive.

The other thing is that you don't need a very large team, right? So I 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 I 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 um

or thousands or anything like that I think you can really make progress with um you know a very strong group of a dozen or a couple dozen people. And

yeah, I mean, 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,

world. So, I think that that's yeah, it's it's just kind of a cool thing about the world is that people obviously are are drawn to different different missions.

So, I 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, um structure prediction models for um for

proteins and protein protein interaction models. Uh and then so there's this one

models. Uh and 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 uh much better hit rate. Um what needs to happen for us to go from ESM fold 2 to this other piece is that feasible?

I think that's a great question. I mean

I I would say that I'm really optimistic on that. So I I think you know on the

on that. So I 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 how do you figure out

how to effectively optimize a drug? How

do you get it you know get it through pre-clinical? How do you do the early

pre-clinical? How do you do the early safety? I think that you know when you

safety? I think that you know when you have a new scientific paradigm kind of you know questions that were once hard um kind of become simplified through the

new paradigm. And so I'm very optimistic

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 uh through these models. And I think

one great example of that is is toxicity. Whereas if if you can kind of

toxicity. Whereas if if you can kind of really digitally um 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, you know, like you kind of have um the the beginning of a solution to that kind of problem. So I I I think that I think that once you have these kind of

accurate representations at the molecular level, you know, you we're going to start to see really rapid progress on a lot of these core problems. What is the most uh exciting use or

experimentation uh 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 um and and kind of just automate that that whole process.

So it's it's really I think another example of how you can kind of see bringing together um Agentic 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 um the the entire design process.

Are you taking um you know how do you decide what the next step in the research agenda is? Um it's like world model for biology and then I could I'm just going to be very coarse here like I

could scale it up. I could add more data. I could like adding data is a

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 um the larger ecosystem about you know how people are

using it? What would make it more useful

using it? What would make it more useful or is it really like we 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 the virtual cell and you know really being able to kind of ladder up the hierarchy of biological complexity to the cell

and sorry very basic question this 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 uh a system that can really model each of the levels of complexity. So you know

the the the proteomic layer, the genetic layer, the transcrytoic layer and connect that to the phenotype. And you

need enough generality so that you can um ask the model questions about uh a new intervention in a context that it hasn't been trained on and kind of get

uh an answer from it. And you know the gap that we we need to close as a field is being able to um uh really make those predictions that can generalize. So

that's going to require you an enormous effort to generate data.

Yeah. And then I mean in terms of what you decide to do next, I think this is like you know a pretty normal process of constraint management, right? I mean

it's like 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 um so I mean I know like you know there's always questions. It's like

okay should we double down more on advancing the protein piece? Should we

do more of the cellular stuff? fact that

those are kind of ongoing debates in terms of how you sequence that. Um and

then yeah within that there's kind of being at the paro frontier about how much you want to train the different models in order to like in and the size of the models is also dependent on the scale of the data that you have because

you know yeah for for obvious reasons.

So yeah, I mean I think it's there's some of that is just where you want to be on the curves and then 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 worldclass work at one thing at a time while planting some

seeds that can um blossom over the the next uh couple years as well. Yeah, this

has been the most dynamic uh period of technology, at least I've seen over my career. I mean, it's so exciting in

career. I mean, it's so exciting in terms of everything that's happening with AI and every week there's something new that's changed.

Are you tired or invigorated?

I'm I'm both feel like everybody's in manic phase.

Yes, it's a combination of invigorated and exhausted.

Yeah, it's wonderful. And so, um I guess uh you know, things are very unpredictable right now. It's really

hard to know what's coming. we have this um almost like early signs of exponentation on the model side with agentic uh 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 5 years from now and you were to define what success was relative to your efforts and I know things have are very dynamic

things changed a lot but you have this common thread of tooling for the biohub you have a common thread of empowering scientists at scale you're looking back 5 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 mean, I think we have a pretty clear view of this like hierarchical set of world models that we want to build around biology. And the other part of

around biology. And the other part of that is that we want to do the highest quality work in the world, right? I

mean, and I think we're basically set up to do that between having a world-class AI research team and this collection of of biohubs, which are worldclass life

sciences research organizations. I think

that that's like fundamentally a setup that no other organization in the world has. Um, but you know, you can have a

has. Um, 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 5 years from now, looking back, I I think you know, it's other 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? And I think that that's kind of what you whenever you do any kind of research that's what you're

trying to do, right? So um yeah, so if we do that, I think we'll all feel very good. I would also expect that at some

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

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 worldclass 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. I mean, I think the the biggest thing that that we basically rotated and 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 is like been a very big shift. Um 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 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 benefit of like

people being able to like work well in a stable environment over time. Um, so I think that that's a really important piece. Um, but part of what we wanted to

piece. Um, 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 in in AI in in in biology.

I think that that's like a deep reflection on on kind of the way that I that we expect that this is going to um kind of drive more value in the future.

So those are probably the biggest updates in the last year and in terms of the work that that that we're doing. and

it's a new leader, not just a leader, but a team. Um, 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 every it's it's kind of this crazy thing cuz 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 because it just like but but I mean the nature of an exponential curve is it doesn't just keep going it keeps accelerating right exponential growth is accelerating um so

I think that that has all these like emotions and psychology attached to it but I think fundamentally when you look at the curve in the industry um the kind

of fundamental thing is it is on track it it has remained on that curve um 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 in the things that that will play out if that if you stay on that track. And it seems like we

are. So that I think is very good news.

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 because with code and research it's closed loop systems and so they're very fast to iterate. This is a openloop system so you're closing a loop and that's

that's really crucial to progress.

Yeah. For me, one of the biggest changes uh 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 uh like the potential uh collaborations and interconnectedness of our work. But uh now we are arms

linked moving together.

It's very directed and um it's very exciting. It's a little bit scary, but

exciting. It's a little bit scary, but it's like truly a team um playing off each other in trying to make progress

towards this goal. And um 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.

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