🔬Biology Is Turning Into Software — Matt McPartland & Neil Patel, Chai Discovery

It looks a lot less like a ChatGPT and a lot more like Autodesk, SolidWorks, or Figma, if you’ve used those things where you can load up your molecule. There’s almost a Photoshop-esque design suite. You have the equivalent of a paint tool to paint your epitope and the equivalent of a Content-Aware Fill tool to get your binders generated from Chai.

To add to what Matt’s saying, this notion of target discovery, hit discovery, and optimization—where each of these has a gate and takes a few months to a few years—is a very waterfall model. The cost of trying things and getting things early is very expensive. But if you start to get into a regime where models can give you really promising candidates, you can start to make that look a lot more like a loop. It’s akin to becoming more agile in software development.

1. Intros: Matt and Neil

But now the next problem is agonists. How do you reliably one-shot hitting a switch on a cell? Or bispecifics or ADCs? I think these are the levels of abstraction that we’re going to have to climb with the product as the models get better. If you have really good primitives for structure prediction, binding, and design, and you can compose them, then you can start to grow into the outer loop of science.

Welcome to Latent Space AI for science. I’m Brandon. I build RNA therapeutics at Atomic AI. I’m joined by my co-host, RJ Honicky, CTO and co-founder of MiraOmics. It’s a pleasure to have with us in the studio today Matt McPartlon and Neil Patil of Chai Discovery. Chai is a protein-design startup that’s about 2½ years old and has made quite a splash in those few years. You have several very exciting announcements that I think you’ll tell us about today. To get started, could you two give us a bit about your backgrounds and what you do at Chai?

Thank you very much for having us. We’re super excited to talk about Chai today. I’m Matt McPartlon, one of the co-founders of Chai. My background is in AI- and biology-related work during my PhD. I actually started my PhD in theoretical computer science and then transitioned to this later.

I’ve been doing this stuff for about 8 years, and I came into the field at an interesting time, when protein structure prediction was just starting to see signs of life. This was during the AlphaFold 1 days. I was in the field during AlphaFold 2 and got to see a lot of the interesting developments at that time. I’d always been interested in applying this stuff in the real world, and Chai was a perfect opportunity to do that.

I’m Neil Patil. I help lead platform and product here at Chai, so a lot of the work around the infrastructure to train models, serve them, and then the productization piece—the design suite that lets you use the models.

I have a more meandering path. I got into programming about 15 years ago, making apps in the App Store, and got really addicted to the dopamine hits you get from that. Then I got nerd-sniped by robotics and worked on that for a bit, including self-driving cars in 2018 and 2019. I got really jaded and decided I didn’t want to touch hardware for a while.

I ended up switching and joining a SaaS company called Vanta as one of the first employees there and grew with it. I started my own security company afterward. A few years into that, I thought, “You know what? Atoms are kind of cool. I want to work on something a little more meaningful.” I joined Chai about a year ago, right after Chai-2 was announced, to help with a lot of the platform and commercialization pieces.

2. Four pharma partnerships

Awesome. It’s like the 5 stages of grief or something.

Yeah. We’re at acceptance.

Awesome. You have, I think, 4 big partnerships now and have raised a whole bunch of money. Can you tell us a little bit about those partnerships? What I really want to know is, what are you telling investors and customers that’s so compelling that they’re willing to do these big deals?

We’ve been very fortunate to partner first with Eli Lilly and then with Pfizer, Novartis, and Genentech. It’s been a really interesting ride, and I think our business model is also very compelling to a lot of people. We really care about the partners succeeding. Chai as a company really depends on how the partners succeed.

Neil probably has some interesting takes on what we actually offer and what makes that so compelling, so I’ll hand it over to him.

As you all know, drug discovery is a very lengthy process. A lot of these pharma companies are spending years and years and billions of dollars trying to find initial therapeutic candidates. At Chai, we train models that can help accelerate that process and find those initial binders and then some.

There are a lot of bio companies and AI-for-bio companies that are making their own drugs. We really don’t see ourselves that way. We see ourselves as almost a neutral software factory for making medicines. That’s what lets us work with and support all of these other pharma companies in their drug-discovery journeys.

3. The software-layer bet

A lot of this capital is just another proof point that we can start to accelerate that software factory, go after harder modalities, train bigger models, and ultimately build what our partners and customers ask us for.

But what is it? Why you and not other structural companies? Why are they compelled to buy from you?

The thesis of Chai has always been to be the software and modeling layer, which I think was very controversial at the time.

That was 2 years ago, and it’s already a completely different world. Yes.

Yeah, it’s pretty crazy. People tried this play for a while, and I think the models just weren’t there yet. Even for us, we were taking a risk in the very beginning. We were banking on the models getting there.

I had seen early signs of life in my work, and our CEO, Josh, who was on the original ESM papers team at Meta, was seeing pretty early signs of life that there might be scaling laws here. We thought we’d actually be able to start designing things. Structure prediction is getting really good.

One crazy thought is that we didn’t have a multimer structure-prediction model until 2021. That was 5 years ago, when we could start using deep learning to actually predict the shape of 2 proteins at once. AlphaFold 2 was this huge breakthrough, but then AlphaFold-Multimer came out a year later. You really needed that to unlock design in the first place. We weren’t even trying to predict multiple proteins at once.

Then inverse folding started working, and we thought, “Oh, protein design—this actually works in the lab.” Credit to the Baker lab for doing all this excellent lab validation on their models, but we’re starting to see them do interesting things and actually work in real-world experiments.

Now is probably the time to start betting on this. Before then, maybe you could take some experimental data from a campaign on 1 target that you had and cared about, and you might be able to make some progress on that and keep hill-climbing in this 1 very specific case. General models weren’t really a thing back then.

4. The 50-target challenge

We took that bet seriously and decided to push as hard as possible and shoot for generality in our approach. When Chai-2 came out, our second paper after Chai-1, we showed the world that this was actually possible and possible at scale. We didn’t show this for 1 or 2 targets. It kind of worked, and we thought, “Let’s just go all in.”

Josh likes to say we set a bold companywide challenge to design antibodies for 50 targets, and we actually saw some signs of life. We thought, “All right, let’s do this with real statistics and see if this actually works.”

It’s an interesting story how we chose these targets. We asked, “What targets are we going to choose? We should choose some interesting targets.” At that point, we were ramping up with CROs and figuring out what our wet-lab process looked like. After trying some things with many proteins, we said, “Here are the interesting targets. This is what we should look at.”

Half the time, the targets just didn’t work. We were still learning, so we thought, “Maybe we should just go with targets that the CROs have actually validated. Let’s get the CRO catalog and see what they’ve already worked on, and restrict that to an interesting set.”

5. What is an antibody, and why target it

From that, we chose 50 targets, designed antibodies against them, and got hits for half. At that point, I think pharma started to realize, “Okay, there are actually signs of life here, and this might work in some of our programs.” So antibodies are maybe a more challenging domain than other structure-prediction problems.

So why tackle antibodies? Maybe back up: what is an antibody?

Yeah, and what do you do with it, and why is it an attractive target? The analogy that everyone gives is this lock-and-key kind of problem, where your target—this protein that you're trying to bind to—might be some disease protein that's kind of like your lock. Then you want to design this key that fits into it and, in our case, just sticks there.

The interesting thing with antibodies is that they're really flexible, general proteins. In a lot of ways, they're very general; in a lot of ways, they're actually pretty uniform. But at least the way they bind to a target is very general, so you have a lot of optionality in how you design this kind of binding interface.

The structure-prediction problem for antibodies—predicting how this antibody actually binds to the target, how the key fits into the lock—has been a notoriously difficult problem. The nice thing is that we've made a lot of progress on structure prediction. The field as a whole has come a long way in getting structure prediction to where it is.

But in the design setting, you can be a lot more selective about the types of designs you want to make and the types of structures you actually want to focus on. In some cases, it might actually be even easier to design a protein binder that is an antibody than to actually predict how it might bind that target in general. If you have the freedom to choose, you can just pick the easy cases, if that makes sense.

With antibodies, there's a whole machinery in the body that works with them. What does the body do with them naturally, and what can you do with them that is sort of not natural but useful for therapeutics?

This is coming from a non-biologist here, but I think of antibodies as these Y-shaped proteins. They kind of look like a peace sign with your fingers. Each of these fingers is kind of like an arm of the antibody, and it's actually only the tips of your fingers—the tips of the antibody—that engage in binding.

So this makes them really nice therapeutic design targets for that particular reason. The nice part is that the rest, apart from the tips, is actually relatively constant. This is called the framework region of an antibody. In the design problem, you're typically just designing the very fingertips, and you can choose, for the most part, these framework regions that your immune system already recognizes.

6. ADCs, bispecifics, and pressing the switch

Antibodies are these Y-shaped proteins that your immune system recognizes. It knows them really well. They're kind of one of the lines of defense in your body against pathogens and other types of diseases. I guess antibodies can, on one end, connect to proteins on the surface of a cell, typically, or other things, and then the other end helps the immune system identify a pathogen, typically. But you can also do things like you mentioned: ADCs, antibody–drug conjugates. That means putting a drug on the other side or something like that, which causes the drug to be released into the cell when you bind to something.

Right. They're this very general framework where, on the ends, you have these CDR loops, and you can design them to bind to arbitrary things. Maybe on one end you bind to a cancer cell, and on the other end you bind to a toxic molecule. You're now precision-delivering that toxic molecule to a cancer cell, right?

Or you just have two ends bind to things and force induced proximity to have some effect in the body. A lot of drugs historically are really just about blocking things—antagonist behavior. But maybe you can have agonist behavior. You can really precisely press a switch.

There's a GPCR, or G protein-coupled receptor, which is one of these doorbell proteins that sits in your cell membrane. You can have an antibody very precisely engineered to poke it in a certain way that causes a downstream chain reaction. One of the things that's really exciting about where we're getting to with some of these models is that we can start to get that precise.

7. How this was done before: mice and yeast display

We can really target a very specific epitope, meaning a binding spot, or a very specific set of atoms for the antibody to go after. Historically, with a lot of drugs, you're just brute-forcing a lot of antibodies and trying to come up with a bunch of things to see what sticks. Maybe that gets you a binder to some spot on your target molecule, but that doesn't let you precisely engineer where you're poking it.

I know you're not biologists, but do you have any idea how they used to design these before these models came up?

Or what is actually still the state of the art in terms of drugs that have made it to the clinic?

Yeah. Josh, our CEO, likes to say that our biggest competitor is the mouse—or nature, in certain ways. Traditionally, these types of drug-like molecules were either discovered in immunization campaigns. You would literally just infect a mouse with a disease and see what antibodies it makes to try to combat that.

Other ways of doing this are super-large yeast display, and so on. You might start with, “Hey, I really like this framework. How am I going to figure out the right loops to design to bind this target?” Then you try as much as you possibly can and literally search for a needle in a haystack. This would be on the order of at least billions of potential molecules that you're screening against this 1 target.

In that case, you might end up with 1, 2, maybe a dozen potential hits to this target. You don't know much about those hits. All you know is that they kind of stick to the target. You don't necessarily know where, or whether they're even drug-like.

I think one big separator of Chai—and a thing that our partners definitely like to see—is that you can be really intentional with how you want to do this design process. You can say, “I want to bind this target in this particular area.”

You can even go back and look at the designs after we've validated them. You can go back and look and say, “Is this antibody engaging the target in the way that I expect? Do I think this will actually have the therapeutic effect that I'm going after?”

8. Selectivity and cross-reactivity

One of the cool things about knowing that you have the right binding pose is that you can now also design selectivity into it. Does your platform have some technique for selectivity?

Yeah, there's a nice mix of ideas that went both into the modeling side and especially into the product side for dealing with selectivity and cross-reactivity. In some cases, you want your molecule to bind 1 target and avoid another one. So you might have a healthy variant of a protein and a disease variant of a protein, and you want to avoid the disease variant. Or you might have some other similar protein that's not actually harmful in your body that you don't want to artificially block.

On the modeling side, we've come up with ways of doing that, but I think it's even more interesting on the product side. How do you enable customers or partners to actually intentionally design for these things?

Yeah. And maybe to back up and define cross-reactivity: it turns out that when you're developing a drug, you're not necessarily going straight to injecting that into a human. You might want to put it in monkeys first, for example. The monkey might have a mostly similar but slightly different variant of it, so your drug not only needs to bind to the human variant but also the monkey variant.

The way we've tried to approach this in the models and the product is to let you account for those very general cases where you say, “Hey, I'm trying to design something that can bind to both of these things so that I can actually go and develop the drug.” You can identify the region that's conserved and then target that. Conserved means it doesn't change much between the 2, and you target that exact region.

Similarly, with selectivity, there might be a very similar protein in the human that, if you accidentally bind that one, is very bad. You only want to bind the target protein. That's why a lot of drugs fail, are toxic, or have really bad side effects. You're kind of having this combinatorial problem of binding only these things and avoiding only these.

What's been really exciting with some of the progress recently is the improvements we've been able to make in the level of specificity we can get to with those models.

So you're not only designing the binder, but you're also making sure that it doesn't bind to another thing. Are there other ways that CAR-Ts have tried to tackle this by having some molecular or some sort of signaling pathway that says, “If I bind, I only fire if I bind this one and this one doesn't bind”? But you're saying you just design an antibody that will actually bind only to the thing that you care about.

We're getting to the point where, in some cases—it's nuanced, right?—you can actually try that.

Okay, that's amazing. So you're saying you essentially call it a counter-screen, or you have, as part of your platform, a way to reliably counter-screen against a large, diverse set of proteins that might be issues downstream?

9. Chai-1 and the MSA detour

Yeah. I would say the framing is more that you can be very specific about what you care about binding versus what you care about avoiding. But I think, for example, a lot of the money we're raising now will let us train bigger models that can maybe be even more general and start to account for even more things at the same time, right?

Maybe we should back up. Let's talk about the history of the Chai series of models.

Well, why don't you tell the story?

We started Chai around 2.5 years ago. For the first couple of months, we were like, “All right, we're going to work on protein design.” We were working on this and making some progress. We were like, “Oh, this is pretty interesting.” We had some ideas and models.

Then that was right when AlphaFold 3 came out. We'd been talking about, “Man, we really need an MSA pipeline. We need all this infrastructure.”

MSA is multiple sequence alignment.

Why is this? We've covered this before, but what is an MSA in 2 sentences, and why is it important?

If you want to predict the structure of a protein, it might be really useful to see a bunch of very similar protein sequences. What those really similar protein sequences tell you is which positions—or which amino acids—end up being conserved across many variants of this protein. If you see high levels of conservation, or high levels of mutation like correlated mutations, it typically gives you some indication that these amino acids are close in 3D space. So you kind of have this 2D view of a protein, which can then be used to help you predict this 3D structure.

So you're learning from evolution what was conserved because the things that weren't conserved probably broke the protein, and something died or didn't make it.

Exactly. It's pretty remarkable that this works, honestly. One of my favorite bio facts here.

We were thinking, “Oh man, it'd be nice to have a lot of infrastructure and whatever.” Then AlphaFold 3 came out. We were like, “Hey, we should open-source this model. We should just bunker down and build all the infrastructure that we need.” I think this will pay back in the long term, for sure, both as a forcing function to get us where we are and to contribute to the community as a whole.

So it's interesting that you chose—okay, what we're actually doing here is building a model, but what we're really doing is learning how to build the infrastructure. Is that kind of what you're saying?

10. Sitting in the OpenAI offices

Yeah, that's exactly right. I had built a lot of similar infrastructure in my PhD, but not at a production level for a company. At that point, I think there were 5 of us at Chai, and we were like, “All right, this is our forcing function. We have a clear goal to work toward. It's very direct. Let's get this thing going and see how fast we can do it.”

You guys were, at this time, sitting in the OpenAI office?

We were sitting in the OpenAI office, yeah—in the Mission.

Right. So what's the backstory on that? It's really interesting.

Two of our other co-founders, Josh and Jack, had a relationship with some of the OpenAI people, actually. OpenAI co-led our seed round. So we were thinking, “All right, should we get an office while we're only 5 people?” It turned out that office was mostly vacant, so we got to sit in the OpenAI offices for a while. We built Chai 1 open source and learned about infrastructure.

Yeah. So then after that, we really set our sights on protein design.

And it's worth pointing out: Chai 1 was a structure-prediction model, right? You have the sequence; what is the structure that it folds to? And then that was Chai 2.

Yeah, Chai 1. Chai 1's finished. One other crazy story there—let's see if we can actually share this. This is a hilarious one.

We were like, “Oh man, we really want to be the first to put this out.” We were like, “Okay, we're 1 week out. The model's almost done training. Should we build a web server?” Then we were like, “Oh yeah, maybe not.” But we ended up spinning up this whole web server so people could use it rather than just download the Git repo. It's kind of annoying, especially for biologists, and we actually wanted people to use this, so we thought, “Let's spin up a web server. Let's get the technical report out,” and all this stuff.

We ended up being up for 48 hours straight, just getting the paper over the line and getting the last things done on the web server. Then Josh was interviewing with Bloomberg TV or something that morning. We'd been up for 48 hours straight, so Josh ran into a room to do this interview on Bloomberg TV. I think it was 7:00 in the morning. Everyone was in the office, and we didn't want to be seen. The interviewer was like, “Interesting company. It doesn't look like there are any employees here.” [laughter]

Yeah, it was a really fun time. The early startup days were just super fun. After that, we set our sights on design, and what we were thinking is, we always had antibodies in mind. We thought of this as the most tractable problem. The nice thing with proteins is you have this beautiful sequence representation, and there's already a lot of research on how to autoregressively generate sequences. This sequence-generation problem is well studied.

So we were thinking, what's a nice area to apply sequence generation to in the bio space? It's pretty natural to do linear sequences of amino acids. So we started working on design. A unique thing about Chai is that we're not just designing antibodies—we're not an antibody company. We don't really pigeonhole ourselves into 1 therapeutic area.

We tried to tackle this problem very generally. We were thinking: Can we design many proteins? Can we design antibodies? Can we scaffold protein complexes? We wanted to take a holistic view of how you design proteins in general. That eventually led to Chai 2. That was our first flagship design model, and that's where the Chai 2 paper and our BOLD target discovery project came in.

We designed antibodies to 50 targets for that paper, got binders to about half of them, with, I think, on average, around a 20% hit rate for binding. Afterwards, we started working on Chai 3. That's our latest series of models. But let's take a break there.

11. Anatomy of a structure prediction model

Before we talk about Chai 3, can you tell us, especially for listeners who may not be familiar with structure-prediction models, what the model looks like? How does it work in general?

Let's take a look at Chai 1. Chai 1 has, roughly, a tokenizer, a transformer, something that looks like a language model, and then something that kind of looks like an image-diffusion model, and they're all stitched together. The tokenizer is not your typical WordPiece-style tokenizer. This is like: I have a bunch of atoms in a molecule, and now I want to pull those into what I would call tokens for my LM-looking trunk. Then that conditions this big diffusion model, which will emit the image, which is some 3D structure.

So is it atoms or is it amino acids that are the input?

That's an interesting question as well. We have all these different input tracks. One thing about biology is that the data is inherently multimodal in a sense. You have this token-sequence representation. Each of these tokens has a set of atoms that kind of dangles off it. You also have some properties of the different atoms: an atom might have a different charge, or it might have a different element type—the periodic table of elements.

These all get bunched together into tokens. Once tokenized, you can process this in very standard ways. But ultimately, you have to get back to these 3D coordinates. In order to predict the structure, this is just some 3D object, and to emit that object, you go through what looks like an image-diffusion model, where you go back from tokens to the atom representation.

I see. So the tokens go in, the transformer establishes the relationship between the different tokens, and then the diffusion model turns that latent representation into a 3D structure.

That's exactly right.

Okay, great. So that's Chai 2. That was Chai 1.

Okay, okay. So Chai 1 was a folding model. Yeah. All this biology stuff sounds kind of scary: atoms, tokens, amino acids. My background, personally, is theoretical computer science. That's what I spent all my earlier years doing. I transitioned to this pretty late in my PhD.

12. Chai-2: all-atom diffusion and crossing into design

But I think the background that you need is really similar to the background that you need for any other field of machine learning. There are all these domain-specific things that you learn about. One analogy, or anecdote, I like to say is that people think you can't work on AI bio unless you're a biologist. But it's kind of like saying you can't work on video models unless you're a director or something. There are all these super domain-specific things, like understanding lighting in a video, but at the end of the day, these are just machine learning problems, and they're all solved the same way.

Okay. So then Chai-2—there's a jump in capability as well as an architectural change, right?

Yeah. What we've disclosed about Chai-2 is that it is an all-atom diffusion model. We're trying to predict atoms in 3D space still, but we're doing it in such a way that the model actually has the ability to design atoms, place them, and decide which atoms are actually there. One way to represent an amino acid, like a protein token, is by which atoms are present. So in the Chai-2 case, we were just predicting, all right, let the model pick what atoms it wants to keep, and then map that back to what amino acids there are.

What are you able to do with Chai-2 that you can't do with Chai-1? Is it just better, or are there new capabilities it brings?

It's design. Chai-1 lets you say, “Hey, I know the sequence of amino acids”—that text string—“and I know the structure.”

That you would get from the genome, exactly?

Chai-2 says, “Okay, I have a target structure that I want to design a binder to.” Chai-2 will then generate candidate molecules, candidate medicines, that bind to that target. This is a design model, or a design family of models. I think that's where you really cross the threshold of usefulness. Chai-1 is very useful because you can at least intuit and reason about the structure and see what you're looking at. But the ultimate goal here is to design medicines and new molecules, and I think Chai-2 really crossed the threshold of performance for doing that with antibodies a year ago.

One analogy here would be, back to the image domain, Chai-1 would be like, “There is a cat in this image.” Thanks, Chai-1. Chai-2 is, “I'll show you a background. Maybe I'll prompt you with some image information, like, hey, put a cat in a field,” and Chai-2 actually just gives you back an image of a cat in a field, and you're like, “That's a good-looking image,” or it's not. You might have some other model that ranks the image, but fundamentally, it's the generative problem.

13. Co-designing sequence and structure

Yeah. Taking that analogy a step further, it's maybe more like you show it a background, and then it generates that there is a cat. It generates an image of the cat at the same time, and it makes sense both that there is a cat in this field and that the cat works in the image. It's an interesting problem because you have to generate 2 things at the same time, both the sequence and the structure. I don't know if you can, but could you talk a bit about how that works? How do you do that? So you co-design the sequence—

—in a way that the structure also fits and makes sense. One way to think about it is the classic way of doing this. Let's talk about both in structure prediction: I know the sequence, and I can roughly figure out the 3D shape from that. Then there's the inverse-folding problem, which is: given a 3D shape, give me back a sequence that would fold into this. Now you need to do both things at the same time.

I think similar principles apply. You can have the model think a little bit about what the structure should look like, then have some other part of the model think about what sequence would support this. A nice thing with diffusion is that you can do this pretty slowly and iteratively. You can give the model a lot of time to think: if I change the structure like this, how should the sequence change? You can play this back and forth and eventually it converges on something that's self-consistent.

14. How do you know a design is any good?

It's almost like an EM algorithm. Yeah, exactly. So you have this model now, Chai-2, which is able to predict, or sample, a structure and a sequence that generates that structure. Just because you can generate a structure doesn't necessarily mean it's accurate enough to do something. Do you have other scaffolding on top of that? Are there additional problems? Are you one-shotting these things, or do you need to generate thousands of them and then have a ranking or scoring? Having a candidate is maybe not enough. What do you do once you sample a structure, or—

Traditionally, what's done—and when co-design in protein structure design started to become a thing, we were kind of at a loss for metrics—is ask: how do you know that your protein is legitimate after you design some sequence and structure? How do I know that this is legit? Even biologists are like, “I have no idea if this thing actually folds. Maybe some of it looks right.” Even our biologists are surprised by some of our designs that do end up working.

What was done at the time is that we came up with a bunch of metrics, and AlphaFold really enabled this. You'd take the sequence that you predicted and run that through a totally distinct structure-prediction method. This is completely independent of your model, and you say, if an independent model thinks that this sequence folds to a similar structure, then it has a higher likelihood of being correct than whatever the prior likelihood would be. You can take your sequence and measure how consistent this structure-prediction method is with the structure that you actually predicted for that sequence. You can now compare your design to an independent model's structure prediction, and that became a really good way of gaining conviction that your design model was correct. People kind of gamed these benchmarks for a while and kept pushing and pushing. It turns out it’s easy to get self-consistency, consistent design of structures, if all of your proteins look identical. There are a lot of problems that this creates, but then people started adding more and more on top of this.

It's totally out of domain now, right? By definition.

Yeah. As a human, you can look at this thing and be like, “I don't know. It checks out.” Even our biologists are surprised by some of our designs that do end up working.

That's an interesting point that I think some people have acknowledged in the community. How did you solve that? You can see that if you use your oracle and your sampler at the same time, you eventually converge. What do you do to stop that, or to convince yourselves that you're doing something valuable?

One of the nice things about structure-prediction methods is that usually you have some calibration in how confident the model is in its prediction. It turns out these models can give you a pretty well-calibrated confidence prediction. Rather than just saying, “This is what I think the structure looks like,” it'll say, “This is what I think the structure looks like, and here are the parts that I'm not really certain about.” You can aggregate this down to a single scalar.

15. Validation, cryo-EM, and the 0.33 Å result

Typically, people will look at not only how self-consistent they are, but also how much this independent folding model even likes the structure that it output. That was one way, early on, to gain confidence. Another thing that people often do is look at the diversity of their generations. Again, you could have a model that's perfectly consistent and gives you great confidence predictions back, but it might be the same structure every time, the same sequence every time. You also want to see how diverse the solutions are. How many of these new problems can I solve, in a sense?

If I had a whole lot of money to validate, how would you do that? Can I go and do cryo-EM or something like that and try to figure out the structure, to get some ground truth on that?

It's more that the feedback loop is really slow. You can validate a few structures like this, but it might take months, and it's just not a very scalable direction. I think that's a problem for the field as a whole. People are spending a lot of time—especially at Chai, I think—thinking about how to validate these problems at a bigger scale. How do we increase the throughput of our validation or decrease the cycle time? If you're waiting months to figure out, “Hey, was my model correct?” it's hard to iterate in a research environment that way.

The good news is that this is getting a lot better. There's a whole network now of wet labs that you can work with that will run these assays and experiments and tell you things like whether your protein binds to its target. Thankfully, we're not at years; we're down to weeks, which isn't as fast as LLM land, where you can scale up an eval, throw more compute at it, and get results back in hours, but it's fast enough that you can start to recursively self-improve.

And I think we also spend a lot of time figuring out what metrics we can compute in silico—on the computer—that are predictive, perhaps, of lab success. But, to your question about cryo-EM: yeah, you also have to measure the structure, and as you know, that's expensive because you have to freeze the protein and shoot these electron beams at it and see how they bounce off.

I remember there's this really funny anecdote. We'll see if I can share it. In the Chai-1 paper, we actually did that: we took some of the proteins that the model predicted and ran cryo-EM, and we got the results back and were like, “Wait, the results look wrong,” because we had overlaid the prediction over the electron density point cloud, and we didn't see any difference. The point being, we're getting to the point now where these structure-prediction models are within a few angstroms or less of the actual atomic positions.

And in this case, it was a 0.33-angstrom error, which is 1/3 the width of an atom.

And we were like, “This can't even be right. Clearly, they just sent us back the wrong design.”

They just sent us back our design.

Yeah, exactly. Yeah.

Did you check for data leakage?

Yeah, in this case, there were no known antibody binders. We actually chose these targets specifically to have no known antibody binder, so if we did get a hit, it was definitely the first antibody hit to this target.

16. Chai-2.5, Chai-3, and betting on the models

I think one of the things I didn't realize about biology was just how much of it is literally feeling around in the dark—and that's not even a metaphor. You literally can't see how these things look, right? So structure models are so huge, because now you can actually predict within an angstrom how these things look, and that enables you to do things like Chai-2 with the design models.

This, to me, is one of the cornerstone problems in AI for science, right? You don't know—

You fundamentally don't even know how to measure your problem in a lot of cases, so it's very difficult to validate.

Yeah. So you're getting these sub-angstrom predictions with Chai-2. Chai-3—why Chai-3? What's better, or what?

Yeah, I think with Chai-3—honestly, there was Chai-2, there was Chai-2.5, there was Chai-2.7, and eventually there was Chai-3, and each time we saw better and better performance. I think the main thing with Chai-3 is that we looked at Chai-2 and the targets it could solve. There was a lot of internal discussion after Chai-2: “Hey, we made successful binders for half of these 50 targets. What about the other 25? What can we do to make those better?”

Then we were split. We thought, “All right, should we study these targets that we missed and figure out exactly whether there are properties of these that we can look at, or should we just bet on the models? Will the models just get there if we put more time into them? Should we be a little less empirical in that sense and really bet on the models getting better?” We definitely took the latter approach: we bet on the models getting better, and we pushed as hard as we could on that front.

Scaling up the model, the data, whatever, to just build more accurate models.

Yeah.

Is it accuracy? Is that the main thing? Is it binding affinity?

So I think binding affinity is a big one. You can't just bind weakly. For this to be a useful tool, especially for our partners, we need to start producing molecules that are at or very close to therapeutic grade, which means they have to bind really tightly. They also have to be developable. They have to have all these nice therapeutic—

Properties. And developability—I think we talked about it; he mentioned Chai-2.5, right?—which we released a few months after Chai-2. There was a study we did on the developability of the molecule.

For the audience, obviously, the molecule has to stick well and stick tightly, but there are these other properties you care about. To use the non-biological terms: is it safe, is it stable, is it easy to manufacture, and does it self-aggregate? We've been pleasantly surprised at how much we've been able to climb and push the performance in those areas.

It seems like one of the reasons that you want to do antibodies is the developability.

Yeah, you get a lot for free there, right, with that antibody framework.

Yeah, it's interesting. To me, there are many structure-prediction models out there. I feel like these other ancillary factors are actually going to be the most impactful in the usefulness of a product, let's say.

Yeah. Right.

Yeah, absolutely. The nice thing about structure prediction is that there is a ground truth you can compare against. For design, you don't really have that. You're like, “Here's some new disease molecule. Give me a binder for that.” If you want to know if this thing really binds, you have to send it off to the lab and wait a while.

With structure prediction, you can be like, “All right, the model hasn't seen this sequence before. It's never seen anything close. Does it actually fold up into the correct shape?” We can just hold that out of the data set and check. I've always thought of structure prediction as this really nice speedrun benchmark to validate ideas on.

Right. Sorry, I didn't mean to say—I meant structural models in general. Yeah. Maybe we can talk a little bit more about getting into the product side of things. Thank you for coming. [laughter]

17. Building the product under pharma IP constraints

Actually, I really think this goes throughout, not only for structural models like this but also virtual cell and whatever. It's really all the other stuff around the drug-development process that is going to have the biggest impact. So can you talk a little bit about that?

Yeah, I think that's actually a good thing to talk about after Chai-2, because I think Chai-2 is where it started to get really fun from a product perspective. With Chai-2, we crossed the threshold of usefulness where, after we released that paper, a lot of pharmas and biotechs approached us and said, “Hey, this model might be able to do some stuff for us. Can we use it?” Then we thought, “Oh, man, we should build a product. We should build something to let you use that model.”

That's right around when I joined, and there was this mad buildout to both build the product—which we can talk about the shape of—and secure the compute so we could serve those models to our partners.

Another third piece there that was really interesting is security and IP. We want to be a very neutral platform that anyone can design medicines on, but, as you guys know, pharma is a notoriously IP-sensitive industry. When I joined, a lot of people told me this couldn't be done: they're not going to put their data in a platform and have all their new medicines generated out of it.

Having a bit of a background in security helped a bit. Actually, if you're really aggressive about how you segment data and set up single tenancy—where you're almost deploying a separate version or separate account in the product per customer—you can actually build a platform and ship it to them.

Through the summer of last year, we started doing that. We'd been working with—or talking to—Eli Lilly, and they were one of the first partners to really work with us closely on that. That kind of made the V1 of the design suite that you can use to engineer some of those molecules. Maybe it's worth talking a bit about that design suite.

I think we have these really powerful models now that can do all of these crazy things if you condition them in the right way. If you give them the right context about the structure you're going after or maybe the constraints around the model—like, “Hey, I want to design an antibody that hits this GPCR protein, but doesn't collide with the cell membrane and also targets the specific epitope on that as well”—

And we looked at it and thought, “I guess we could put a chatbot around it. That'd be really easy to talk to.” But you're trying to build something almost very visual, right? You can finally build something really visual with some of these structure-prediction models.

If you look at the Chai product, it looks a lot less like ChatGPT and a lot more like Autodesk, SolidWorks, or Figma, if you've used those things, where you can load up your molecule. There's this almost Photoshop-esque design suite. You have the equivalent of a paint tool to paint your epitope. There's an equivalent of a content-aware fill tool to get your binders generated from Chai.

18. Convincing scientists who hate AI tools

You, of course, have a lot of the scientific analysis and plotting and whatever to understand the results of the models. But we've just been surprised at how much complexity is actually in doing that, so that you don't shoot yourself in the foot when you're prompting these models to give you advice. So, are you sitting with people who are designing these antibodies and then complaining to you or whatever?

Yeah. How do you convince med chemists to use your tools? Med chemists hate AI tools—it's notorious. They're like, “I don't want to touch this thing,” or, “I don't understand it.” They will not touch things they do not understand.

Well, it helps a lot to have the models working really well, right? When we had the results of Chai 2 and Chai 2.5, I think that's enough of an activation energy where pharma companies and the scientists within these companies are like, “Oh, let's try it. Actually, Chai, can you guys just try running the model against a few of these targets and let's look at the results?” And then we do that, and the results are good, and they're like, “Okay, let me try to get on that product and let me try to use it.”

No, I think pharma is incredibly pragmatic, actually. I've been very impressed with everyone that we've worked with so far. They're very pragmatic about this, and they're willing to be proven wrong. I don't blame them for not trusting the models. I have used these models, and rightly so, I'm pretty skeptical when I see a new release. I always have been.

You really just need to show them the proof. They can give you a target that they're interested in, or maybe it's more something they've worked on in the past. They probably don't want to share IP right out of the gate, but they can be like, “Hey, I've had trouble with this particular target in the past. Let's see how you guys can do on this.” And then once you show them the proof, they're almost overwhelmingly willing to accept that.

I come from a cybersecurity background and have worked on security products before, and those were dark, dark years because you spend a lot of your time selling to people who are surprisingly not that technical. You think cybersecurity people are very technical; in many cases, they're not. It is this kind of uphill enterprise slog to a very unsophisticated customer.

I think I've just been pleasantly surprised by how much I enjoy working with our partners and our customers. These are scientists who have been spending 5, 10, 20 years of their lives working on one target, often in some cases, and they've studied everything about it. They're very sophisticated. They're very smart. Getting to collaborate with them is just a gold mine, and we learn a lot about how to make the product better.

19. The ten-year target

There's this anecdote. A few months ago, we were showing some of the results from a target through a pharma partnership, and one of the scientists in the room started tearing up and crying.

Oh, wow. [laughter]

“You really hit the nail on the head with that one.” And she was like—we were like, “What's wrong?” She's like, “No, I've just literally spent 10 years trying to get an initial binder to this thing, and you guys were able to help me do it.”

Oh, that's—

And that feels really special. To answer your question, there's, of course, the teams of scientists and computational biologists that we're working with within each of our partnerships. There's also the people we have within the building, right?

20. Battle-testing without your own pipeline

I think one of the things that I really appreciate about Chai is how cross-disciplinary it is. We have people who are maybe engineering experts and less bio-experts, like myself. We have great AI scientists, or ML scientists, but we also have a bunch of scientists that we work with and have joined Chai to help us test the limits of the models—see what Chai 2 is actually capable of, what targets it can do, what it can't do—and inform some of the research direction there.

I want to add to that. In the Chai 2 days, we started with a bunch of engineers and people who had AI-bio experience. We didn't have a hardcore lab scientist, and one of our first hires in that realm was Nathan Rollins. He started working in the Baker lab at 14, graduated from Harvard at 18, and got his PhD by 21 or something like this, in the Marks lab.

He was super skeptical about Chai at first. Then the results started to come in, and he was like, “Okay, this is kind of interesting. This could work.” And then once the Chai 2 results came back, he was like, “I need to bulletproof this. Nobody celebrate yet. All this—” [laughter]

I think it's been really nice to have that level of rigor: to have people who have spent the time in the lab, who have designed proteins themselves, who have literally, in the case of Andy, led several therapeutic programs and brought drugs to the clinic themselves. We have all these people internally at Chai using the product and really battle-testing that.

So if you don't have your own platforms—right? I mean, you don't have your own programs, right? You're a pure platform or partnership model, right?

Yeah.

How do you battle-test something if you basically don't have a use case where you have to continuously push it forward? Or if you are just pushing things forward, you end up with your own candidates if you're successful, and then what do you do about that?

We have benchmarks of our own internal cases, right? There's a set of targets that have known therapeutics against them. There's a set of targets that we pick to push ourselves, and we're constantly refining that set and adding to it.

21. Waterfall to loop

That's what the internal science team helps with: expanding that and almost running the experiments to try to get initial binders there. We don't care about developing those drugs; we just do that in service of validating and making our models better.

And then, of course, there's a loop with our partners, too.

Would you consider yourself hit discovery, or are you using some jargon like hit-to-lead or lead optimization? Where do you live in this? Hit discovery might be one part of it, which you can do, but the later parts are often much more bespoke and special. How do you balance that? It seems much more difficult to me to be general—to solve general lead optimization—than it does to solve discovery.

Ideally, we really want to be able to, rather than think of this as a bunch of stages, produce drug-like molecules straight out of the models. I think part of the reason why we think of it that way is because the initial molecules are usually not good enough to be drugs. We're kind of—

At the inflection point now. We're really seeing this internally at Chai, where the models are getting pretty close to producing molecules that could eventually be drugs, or are very close to being drugs.

We try not to make too much of a distinction between hit discovery, lead optimization, and all the different parts of this preclinical pipeline. Our north star is to just really produce drug-like molecules straight out of the models.

22. Levels of abstraction, and throwing the product away

Of course, this is going to be hard, and there are going to be tons of roadblocks. You need to be able to actually prompt the model to do this. You need the whole RL stack to learn different properties and things along those lines. But I think it's very achievable.

And I think, to add to that, this notion of target discovery, hit discovery, and optimization—where each of these has a gate and takes a few months to a few years—is this very waterfall model, where the cost of trying things and getting things early is very expensive.

But I think, to what Matt's saying, if you start to get into a regime where you can have models give you really promising candidates, you can start to make that look a lot more like a loop. It's akin to becoming more agile in software development.

Internally, we have two north stars. At first pass, they almost sound contradictory, but the north star in research is to start to de novo, one-shot, better and better medicinal candidates that are as close to being ready for the next phase as possible.

But within product, we do want to expand into whatever these iterative workflows look like, where maybe I get a binder, I get some results from the lab, and I'm using that to condition my next run of the model. I think they sound contradictory, but they're actually not, because I think what's going to happen is that the research is going to get better at identifying a de novo candidate for a specific class of drugs. Say, antagonists—blocking things—might be a little bit easier. Maybe we can get to a state where we can one-shot pretty good drugs there.

But now the next problem is agonists, right? How do you reliably one-shot a switch on a cell? Or bispecifics or ADCs? I think there are these levels of abstraction that we're going to have to climb with the product as the models get better.

One of the things I got very existential about a few months ago was that I thought, “Man, all this stuff we're building in the product to visualize molecules and do this—maybe I'm just going to have to throw it all away when Matt ships Chai-4.” But I think that's the reality of building products now. You're using them less as an end in and of themselves. Maybe you'd have built software that was supposed to last 20 years; now it's supposed to last maybe 1 year, but it is the bridge to deliver value and enable the research that gets you to the next thing.

I'd imagine we're probably going to rewrite our product at higher and higher levels of abstraction. Right now, we have something a little more akin to Cursor, where you're inspecting the molecule in the same way you're inspecting the code, because you really need to verify the bonds that are forming and the properties of the things that you're getting. But then you get to a point where that stuff is solved enough that the product is actually just helping you orchestrate these campaigns of hypotheses. Maybe you have 1 target and you're orchestrating a bunch of different epitope choices or whatever against that. Then maybe you're going up 1 level of abstraction, where you're now doing a whole campaign against all of the targets within a pathway.

23. Epitope prediction: the harder problem

I think what's really exciting about that is that if you have really good primitives for structure prediction, binding, and design, and you can compose them, then you can start to grow into the outer loop of science. Maybe the thing runs itself, and you start to get to some really, really cool drugs at the end of it. I actually want to push on what you just said about epitope prediction, because I think a lot of people in the field would argue that this might be the much harder problem than finding antibodies and binders. Where do you think the state of the art is in general, and also with regard to Chai, in terms of epitope prediction? Is this a problem that has a reasonably solvable time horizon? Also, can you define epitope prediction?

I'll think of this at a few different levels. The most basic level is: I have some disease that I want to target, and what proteins are actually responsible there? Actually figuring out biologically what's going on—what should I be targeting in the first place with the drug? I guess once you figure that out, it's kind of a structural biology problem at that point. You're like, “All right, this set of proteins is responsible, and what's going on there?”

Well, this is interacting with some other protein that it shouldn't be interacting with. Conventionally, you'd just want to block that interaction or something with an antibody. But that's where these proteins interact, and the type of interactions that you want to disrupt—that's typically the epitope. It's the actual site on the protein that you want to block. This is a ridiculously hard problem. I'm with you on this. This is the harder problem—the amount of context and global understanding you need in order to figure out what's interacting and how.

But maybe let's take a few specific cases. What about when SARS-CoV-3 comes around, or the new flu or whatever? What would you do there? Is that something you think you could reasonably tackle?

In that case, yeah, you could just run a structure-prediction model and see where the model thinks this thing will bind. If it's highly confident in that, you might say, “Okay, here's the site that we want to block.” I think in general it's still very hard. Even structure prediction is getting really good, and a lot of people think AlphaFold 2 solves structure prediction. Not really. AlphaFold 2 got, I think, 11%; the multimer version of this got, like, 11% of antibody–antigen prediction cases correct. That means 90% of the time it's wrong.

Yeah, but AlphaFold 2 solved a certain class of monomeric proteins with MSAs.

Right. And just to clarify—I had to understand this myself, so maybe I can help listeners who aren't familiar. An antibody—the whole point of an antibody—is that it can identify new things that the body hasn't encountered before. The design of antibodies, as opposed to other types of proteins, is such that the system is designed so that you can quickly recombine different components in order to match proteins from unknown pathogens, more or less. This is why it's not conserved in evolution the way that other proteins are.

24. The economics question

Yeah. So, back to the epitope prediction problem: I think it's still hard. There are a lot of cases that are maybe tractable, but in general, if you want to discover this for a new target, it's still a really difficult problem. Maybe Virtual Cell would be the closest thing to the state of the art there, but that's still a ways out.

I wanted to dig in a little bit on the product, because there's something I don't understand about the economics of basically all the structural stuff that's happening right now. Obviously, a lot of people think it's very valuable, so I'm not grokking something. When you look at the cost of developing an antibody, it maybe is a couple million dollars, right? You identify a target somehow and then say, “Okay, I need an antibody to match this.” Then you optimize it in various ways and maybe try it in animals—with antibodies, you typically get to animals faster.

If you look at how much it costs to bring a drug all the way to market, if you're prescient and pick the right target and the right technology, it might be half a billion dollars. Typically, that $2.6 billion number is advertised over all the failures as well. If you look at just the cost of that 1 success, depending on the disease, maybe it's less, but half a billion might be a good median number or something. You're saving a couple million dollars in a half-billion-dollar campaign. Why is this so attractive?

I would maybe challenge the premise in a few ways. Sure, if you're trying to get an antibody for a very simple target, maybe. But what we've been most excited by is our partners using antibodies in more sophisticated ways. For example, in Chai-2, we showed GPCR agonist activity, where you can really hit the switch on a cell—a doorbell protein, so to speak—in a very precise way.

That's very, very, very hard to do with antibodies if you can't be that precise, right?

25. Modalities you can't get from immunization

So you're unlocking a new capability. I would think about it as less like, “I'm taking the existing drugs I can make and making them faster.” There is some of that too, right? But it's more like, “No, there are better targets to go after that are maybe more precise and more effective.”

There are also drug modalities that you just can't discover with immunization. You're not going to design your crazy multispecific, four-headed, super-intense formats. These are things where you kind of have to design them from first principles. Even with bispecifics in particular, both arms now need to bind different targets, and you have this multiplicative effect on your binding rate. If you have a 1-in-a-billion chance of finding a binder in arm 1 and a 1-in-a-billion chance in arm 2, you're not—this just isn't going to work with the traditional approach.

Exactly. I think the other thing I'd think about is that you're not just helping your partner with maybe 1 drug. There might be a portfolio of targets that they're going after, or a portfolio of drugs that they're trying to make. The nice thing about the platform approach, rather than developing individual drugs, is that we can scale with them as they pursue more targets, in addition to more ambitious targets.

Right. So it lets you concentrate your learning in a subdomain, and everybody benefits from that.

Okay. So what are some of these capabilities? You mentioned a few. Are there more that are really interesting that you guys are chasing?

Yes. We talked about cross-reactivity and selectivity, and some of these really interesting additional modalities with bispecifics. There's a set of things that our partners have been asking us for that we've been working on, but I can't get too into them because that starts to reveal some of the targets that they're going after.

But I think the point being, once you get precise, you can start to do some really, really cool drugs.

It's a new technology, right? Technology in pharma means, how do you deliver your therapeutic? CAR-T is a technology, right? And so this is maybe a new technology in the sense that you can have these highly, highly engineered therapeutics.

26. What's really blocking science-to-engineering

Right, and that comes from the mission of the company, which is to really turn drug discovery from a scientific experiment into an engineering discipline, right? How do you get to the precision-engineering phase for biology, where you can start with, almost declaratively, defining the thing you're trying to get and have the model fill in the gaps and get you that?

So what is the biggest blocker to going from science to engineering?

Oh, man, there are so many things. That's the thing about Chai.

Yeah. [Laughter] Yeah.

I don't even want to talk about the amount of headaches.

Too late. You already know.

Okay, just when you're actually parsing—first of all, file formats for biologists. They just don't care. There are standardized file formats. Are they the best? I don't really know. But there's also a lot of information that you want to pack in: I have this structure; here are the people who solved it; this is the method I used to solve it. There's a lot of stuff going on.

Depending on the method that you use to actually figure out what this 3D structure is, you might have multiple copies of that structure. Part of it might not have really been resolved, or you're thinking, “It could be here; it could be there. I'm just going to give you both options.” So the actual parsing problem on the engineering side of working with this type of data is really difficult.

This seems like something that LLMs can excel at, though.

They don't know all the edge cases often, right?

This is more back to just a simplicity approach. LLMs are very good; I will absolutely give you that. Then you're thinking: Should this function have 20 special cases, or should we be really principled in how we approach this? How opinionated should we be in how we do this?

We want a strategy that's easy enough for humans to understand. When we're reading through the codebase, we really need to know what's going on here and what the potential problems are. Sometimes that just comes down to looking at examples.

Once you've figured out all the infrastructure work and how you get data into the models, there's then scaling the model, and then scaling the infrastructure around the model to train bigger and bigger versions of this. That's a lot of work that Neil and the product team actually do.

27. Buying compute is a terrible job

Yeah, I mean, that would have been my answer: the infrastructure part. I mean, not to beat a dead horse, but compute—getting the compute and using it in the right way—is such a challenge, especially for startups.

This has been such a—yeah. Anthropic is single-handedly holding back science.

Totally. One of the things that I help a lot with at Chai is buying compute for the company.

Worst job, man. I would not recommend it. It is very stressful.

Back to you—the hardware job.

Yeah, yeah. I know. Exactly. In the wrong way. But in September of last year, we started to really notice that things were getting tight, right? We were doing a lot of our inference on spot and on-demand markets, and we'd have these days where you would get these capacity crunches. We thought, “Okay, we should probably start to get ahead of buying some compute for ourselves.”

I think everyone probably says this, but, man, it was hard. I didn't realize how much of a power law this is, where there are, say, 10,000 B300 units shipping everywhere, and the hyperscalers and the biggest AI labs are buying 95%+ of it. Then you have the startups fighting over the scraps.

The other thing that's really interesting, especially if you look at these later compute versions, the Vera Rubin systems or the B300s, is that a lot of this stuff has been built very LLM-forward. You have systems with huge KV caches where you have 72 GPUs that are all required to talk to each other. Obviously, some performance gains there help us, but it's interesting just how much the compute market has gotten LLM-pilled.

I think there's probably a whole set of compute-stack and inference optimizations and things that need to be made for this class of models. I think this class of models is going to be just as big and just as impactful as LLMs, but the compute market doesn't realize that yet, both in the capacity sense and in the software-stack sense. We actually spend a lot of our time, even just doing basic optimizations of compute, to get it to work better for the types of models that we have.

28. Triangle attention and why GPUs hate it

Yeah, I know that some structure models are more recursive than LLMs, for example, and that changes maybe the compute-to-memory ratio that you need and things like that. What are some of the cool or interesting optimizations that you've done there, depending on the type of model?

So we can go back to a Chai-1-type model. In that case, we're following the AlphaFold 2/3 architecture, and rather than doing attention over a normal sequence representation, you're loosely doing attention over this pair representation. You can think of this as a sequence of length L² rather than, typically, length L. If you're doing attention over that, the way that you actually batch this up ends up being L³. Now you're in a pretty heavy compute regime.

The amount of FLOPs that you're putting into every token is pretty high. The memory-bandwidth overhead of just transferring that from SRAM to whatever is a real bottleneck in these architectures. Even something as simple as layer normalization can take a long time; that can be a significant amount of the compute that you're using.

29. Durable execution and Temporal

On our side, we've spent a lot of time optimizing it and taking engineering very seriously so that these operations are at least better. We're always looking at how new chips perform compared to the older versions. Sometimes that's even different for training versus inference, and of course Neil knows this really well.

Well, there's what you're doing on the individual GPU, and then there's how you orchestrate fleets of GPUs, right? You basically shard your computation. When you're designing a molecule on Chai, it's not necessarily 1 call; it's a lot of GPUs being thrown at the problem across a lot of compute. Actually, I would say that one of the hardest things to get right in software engineering is durable execution. Are you all familiar with that term? Can I go on a little?

Ultimately, if you're computing a lot of data—model calls across a very wide set of infrastructure—you always run into problems where some part of the infrastructure is flaky. Maybe the bucket you're grabbing your data from goes down, your database has a blip because there are too many transactions against it, or your GPU errors out.

I've been at companies before where you spend so much of your time just dealing with this. You're basically putting all these queues and retries in place, duct-taping things together, and it becomes this mess where what used to be a pretty simple computation that's just distributed turns into spending 95%+ of your time on all of this queuing and retry stuff.

We're huge fans of a company called Temporal. There's this idea: if you're trying to get a really long-running job to run, at the end of the day, what do you need? You need a queue, your flaky thing pulling off the queue, retry logic to put things back on the queue if they fail, and a whole orchestration system to tie all the queues together and monitor them.

What's really cool about Temporal is that it's a tech company that's invented a framework for doing this. One technical decision we made early on that was very helpful was to run as much stuff as we can on Temporal. That includes calls out to the database from the app, so the database transaction goes through without failing; side effects can sit on Temporal and get retried smartly without us having to write our own queue logic; model calls; and the orchestration of really long data pipelines.

Point being, one of those primitives is that you need to get durable execution right so that you're not stuck in retry hell. A really deep engineering thing that you wouldn't realize unless, like me and Jack, you've been burned by this many, many times before.

30. Complexity vs. the bitter lesson

I think we're at this state now, right, where we've raised another $40 million. I have to go buy another compute cluster. We're going to have really, really, really large runs, inference, and training sets. Getting those foundations right is what's actually going to let us do more ambitious things. To kind of answer your question, I actually think that's a lot of the bottleneck to making biology more like engineering: just having the right engineering primitives.

I have an analogous tangent on the model side. Actually, one of the things that's nice about those problems is that they're super visible. At least, this crashed, this failed; we just see that the loss curve didn't go down, or we see weird gradient behavior, or whatever. I think a lot of these same principles—engineering first—also apply on the research team.

One thing that I like to say is that complexity and being Bitter Lesson-pilled are fundamentally at odds. For example, I think AlphaFold 3—I might get this number wrong—had 23 submodules, and at that point, that's a really difficult system to optimize and study. You're like, "All right, what happens if I tweak this thing in submodule 30 or 21? What happens to the whole system?"

You can always think, "Hey, we can make this better by adding submodule 24," but should you? Or should you think about just removing things and lowering that complexity down? I think that's a pretty fundamental thing at Chai: the engineering culture and being very simplicity-biased.

31. Inductive bias and data efficiency

Have you all seen the picture of the SpaceX engines? Raptor 1 has a bunch of pipes, and Raptor 2… We have a picture of that on our office wall because it's just true, right? How do you delete, delete, delete more things?

Yeah.

But the only way you can accomplish that is—I mean, the reason AlphaFold 2 and AlphaFold 3 worked is that they were small models, relatively speaking. They were very compute-intensive, but they were very data-efficient.

Yes.

And there was inductive bias after inductive bias—

It was brought in by human intuition and probably hard-fought experience. Mm-hm.

They were incredibly efficient. If you try to knock down those things, they're not like a house of cards. Everything is an incremental improvement on top of it. In order to get beyond that, it seems to me like you really just need new sources of data. You need to at least treat data fundamentally differently, in a way that is much more efficient.

I'm actually kind of surprised to hear that you have scale to that degree, because I suggest that you're doing something very different from the way the community is thinking about it. I don't know if you can comment about that, but—

We're pretty first-principles people. The whole research team at Chai—except for me and Kevin, really—we're the only people with a bio background. Even still, we're pretty far removed.

We try to look at every problem as a core ML problem. We try to think of what's the analog in other spaces. Even for image models, CNNs were built to process images, so images should be looked at in patches. That was the nice inductive bias there.

Then people were like, "Well, you can just tokenize this thing, throw it into a transformer, and it's going to work," and it did end up working, even on a relatively small data set. But for proteins in particular, it is really hard. There's not as much structural data. There's a ton of sequence data, and that's one of the unlocks for ESM working. You can get that to just run on a transformer. If you try to do the same thing with experimental structure data, good luck. You need—

I mean, there was that Apple paper where they distilled on ESMFold, which was actually really cool: you could distill on a very large data set and get good signal, but it didn't generalize at all because it wasn't reasoning. It was really pattern matching.

One of the things these triangle layers you were talking about, for example, is that they do have a very nice inductive bias. Maybe it's not the triangle inequality, like the paper originally proposed, but it's a clean inductive bias, and it unambiguously is one of the things that made it work. It just comes at a huge cost.

Yeah.

Yeah. No, I think that's definitely true. These layers are pretty costly, and that kind of limits what you can do with the architectures. They're not only costly in terms of compute; they're just not efficient on modern GPUs either. You have small hidden dimensions and large sequence dimensions—it's exactly the opposite of what GPUs are designed to process.

One takeaway from triangle layers is that you're just trading off parameters for compute in that sense. That's one mental model for thinking about this. I might want to throw more compute at the problem and trade that off for parameters, because I won't be able to hold as many parameters. I can't literally store these large pair representations and still do normal attention.

I think there are fundamental things you can abstract from ideas like AlphaFold, but you can tweak these and start building off of them in your own way. It sounds like you have quite a bit of fundamental research going into this direction. For, I guess, an audience looking for a nerd snipe in ML engineering for new problems, it's a very different research direction than a lot of the community is going in.

Yeah. Yeah. I think what we built at Chai is very unique in a lot of ways, but also very tied to what core ML is good at, kind of what I was saying before. We try to map every problem into a core ML problem. We think, how would you approach this if it were an LLM or something like that?

At the end of the day, we really, really value simplicity. We really encourage people who don't have a bio background not to be scared of this stuff.

I think that extends into the product too, where there's a balance to be had here, right, between how general you make the product. Do you build a cross-reactivity workflow, a selectivity workflow, and a bispecifics workflow? Or do you say, "No, let's make the model general enough to say, 'I'm going to condition on arbitrarily binding or avoiding something,'" and then you just have a very general screen in your CAD suite where you can say, "Hey, I just want to avoid or bind to these parts of these different structures"?

I think, kind of like the ML team, I don't have a formal bio background. Most of the product and platform team doesn't have a formal background either. There is some amount of regretting my words that I'm going to have, right, because I'm sure there are a million nuances, and I don't want to come off as too brash or naive there.

But I think it's sometimes helpful not to be burdened by all of the nuance and nonsense, and you can get to be maximally general because that's kind of what we're seeing in the research. The models are very general; that lets the product be very general.

I'm thinking back to my CS theory days. My first adviser was like—we were working on some problem, and we needed a polynomial-time algorithm for something. He would always tell me, "Never underestimate the power of polynomial time. This is basically like you're allowed to choose whatever exponent you want."

My first paper was an n^20-time algorithm for this problem, and I was like, "Andy, I did exactly what you said."

He's like, "Wait a minute, I didn't mean it like that."

32. Will protein design get commoditized?

Yeah, but I think you can really help yourself—you can free yourself a lot when you're like, "All right, I can kind of do whatever I want and then simplify it later." I think that's really a pretty fundamental way of thinking about things that we leverage a lot at Chai.

The space of binders in protein design, and binders in general, is actually a fairly crowded space. I'm curious about your general outlook of the field and the industry. I mean, I can go back to some anecdote. I was at maybe NeurIPS 3 or 4 years ago, right? The one right after RFdiffusion came out.

I was talking to someone in the Baker lab, and they were like, "Man, I just one-shotted." I don't think they even used "one-shot"—one-shot wasn't even a term back then—but they were like, "I just got picomolar binders out of RFdiffusion and just threw in the cryo." Great, right? It didn't seem like that just solved the problem. It's not like, "Oh man, now every—"

Yeah. But there are lots of people who I think have seen that you can actually do protein design, at least in some categories, quite well.

Is it mini proteins or mini binders? Ironically, nanobinders are actually smaller than mini proteins, or maybe a little bit harder. Antibodies are typically considered even harder. But is this something that can and will be commoditized, at least in some part? How do you compete? Where does the field go from here?

I think the answer is that it's kind of all of the above. There probably will be some commodity layer for certain types of modalities or drugs, right? At the same time, we're going to be able to do more and more ambitious drugs, and it's just like what's happened in LLM land. You have your open-source models that are maybe general and helpful for some things, but people are still buying frontier models, right?

Actually, if you look at the amount of value captured, it's the closed-source frontier models. The whole pie is growing, but it's growing so fast that even as the share of open-source models expands, the frontier models are still able to capture the majority of the value.

Right. And what are the reasons for that?

One, if you have more intelligence, you're going to go after harder tasks, right? If we have more intelligent biomodels, we're going to go after more crazy bio tasks. But also, a lot of the reason I don't use the open-source model is because I don't get Claude Code, right? I don't get Claude. I think there's a product layer to be built that's just as important as the model layer.

We learn a lot from our partners and the people in the building as well. What are the really tough things that they get stuck on using the models? Some of them are the dumbest things, right? I want to be able to better visualize this piece and focus on that. Some of them are actually very sophisticated things that we then have to build a pretty vertical product for.

Look, maybe in the fullness of time, AGI one-shots everything and it doesn't matter, but I think there's quite a bit of time until we get there. The product makes a huge difference for that. That'd be my answer. You probably have a more model-forward answer.

No, I think biology is slow, which is one nice thing, and there's not that much labeled data. You could take all the publicly available sequence information out there, and that might give you a good base model, but you still need some measurements on that data. That's still pretty time-consuming, and then you need to iterate on it.

I think there are even data blockers to unlocking this. If we really want to do zero-shot design and start generating candidate molecules that are almost ready to go into the clinic, I think there's more to that than just AGI. AGI might not solve that right away. There are definitely some technical blockers there.

33. No pipeline, no data moat?

But even in the space of specialist companies, there are probably 10 or 15 protein design startups. The 2 things that it sounds like Chai has gone in on are, 1, an all-in-one product, and 2, you're not trying to do your own platform if you don't have your own data moat. Is that going to help you win out in the end, or is that going to be a blocker? I'm just curious.

Yeah, that's a great question. Chai definitely has no plans to start a pipeline. We take the partnership model pretty seriously. From a personal standpoint, I love the incentive alignment: We make the models better, the partners succeed more, and that iterates on itself.

I think that's a pretty unique part of Chai. One, we're able to partner with a lot of people. Two, we get feedback on the product, so we know that it's very real. This is in the hands of legitimate big pharma companies, and they're actually running campaigns on this stuff.

We really have to be model-forward and model-focused. We need to keep delivering value, and that puts a lot of pressure on the research and product teams. First of all, the product team has to serve these needs, while the research team is always shooting for better and better versions.

The way I think about this is, if you're a bit more model-forward kind of thinker or company, then there comes a certain point where there's a lot to do on both the model and data side, but I don't think either is exhausted. It would be stupid to say we don't need any more data, but it would also be stupid to say the models are stuck and we can only use data to solve these problems. There's tons of room to grow on both sides. We're taking both very seriously.

I would also push back on the no-data-moat premise. That'd be kind of like saying, “Hey, all the enterprises that work with Anthropic, you're not letting Anthropic train on your data, so they can't build models that are good at enterprise workflows,” right?

We are investing in this. There are ways to turn compute into data and get more, and we're doing those. But also, what is the kind of data that you're trying to get? What's cool about working so closely and supporting so many of these partners is that we get to really learn what would be helpful in research.

34. Per-partner fine-tuning

Rather than doing research in a vacuum based on what would hypothetically be cool, we're able to do informed research based on what our partners have been very organically asking us for help with.

I see. I assume that you aren't allowed to train general models based on your partners' data. Do you train specialized models? Is there an AstraZeneca model and a Pfizer model?

Yeah. In a lot of these deals, we're working with them to train or fine-tune a version of our model for them, and I think there's probably so much more we can do there over time.

My brother started a company called Applied Compute. Great company. They're kind of doing this thing for LLM design and helping enterprises really understand the value of their language data and do that for specialized tasks. I think there's a whole world where we could potentially do that for biological data.

What is the value there? What is the lift that you get from using their data? Is it just that it's more data, or is it more that it's specialized to a problem?

They have a lot of scientific data that they're getting from experiments that can maybe help our models do better in particular classes of candidates or targets that they care about.

35. Are all AI companies consulting companies?

Yeah. Even something as simple as they might just have some preferred way of doing things that's not native to the Chai model, and they can ask the product team, in a sense, “Hey, our designs have property X. Can you make sure that they have those?” Even things as simple as that actually have a pretty big impact for them.

This goes along with a pet hypothesis I have that all AI companies, and especially bio and scientific ones, are actually consulting companies. Pharma is particularly the case because you're developing a new drug. It's almost by definition new, so the existing stuff has to be customized in many cases, unless you're doing something that's just a reiteration of old stuff. A lot of the big pharma companies are pushing the boundaries of science.

Certainly, we aim to make the models very general, and we aim to make the product very general and powerful. But there is integration work with every customer. To answer your question, you do get some defensibility just by doing that.

36. The highest-value token in any domain

What's nice about building trusted relationships with these partners is that, hopefully, if we execute really well over the first year, they'll continue working with Chai to do more ambitious and more drugs beyond that.

I mean, it's going to be hard to switch, right?

I hope so. Yeah.

Just getting the security.

Yeah, yeah. Maybe one other interesting point is that, if you think of this on a per-token basis, I don't know if there's another domain where the downstream value of a token is as valuable as it is for pharma. Think about the actual drugs that come out: These can be multibillion-dollar assets. In the case of GLP-1s, I think the 2 GLP-1 drugs combined are maybe a trillion-dollar asset.

37. Pharma as VC: Genentech and Eroom's law

Yeah. Until, I think, 3 months ago, GLP-1s' total revenue was more than all of the AI labs put together.

I don't think people realize that. I didn't realize that. It's crazy, right?

But yet the market cap is way lower.

It’s crazy how relatively speaking the market is.

And I didn’t realize how much of a VC business pharma is. They’re, in some sense, taking really ambitious bets. If you study the history of Silicon Valley, obviously people think of Silicon Valley as software, but in the 1980s, one of the biggest venture outcomes—and one of the first ones—was Genentech. It’s such a VC model: you get this string of tokens that can then give you so much value downstream.

Just a general shout-out to Outpost’s blog series about finance and funding. Really fantastic.

Before that, I knew a lot of those points, but I didn’t realize just how deep that rabbit hole went.

Yeah. Maybe the single biggest problem in biopharma is actually just the funding model.

Have you heard of Eroom’s law?

Yeah. Oh, yeah.

Yeah, yeah.

Moore’s law backwards.

Yeah, Moore’s law backwards. In compute, it kind of scales, so you have this nice exponential, log-linear scaling of compute. You have almost the exact opposite in pharma: the cost of actually making a drug is increasing exponentially. The amount of money put into each drug is growing at an exponential rate, which is pretty interesting to see.

Which guarantees that, at some point, the marginal return on new drug development will be negative.

Exactly. So unless someone—maybe Chai—figures out how to fix this, I think we might be on the verge of flipping some of these—

Bending the S-curve.

Yeah. Just to double down on the point, pharma and VC fundamentally are both optimizing a portfolio.

38. Everyone is a capital allocator

Yeah. Thinking of pharma as sophisticated capital allocators, where they have this portfolio of targets and they’re allocating between them, was a big reframe for me. I think we’ll just see more of that in the future. Hopefully, pharma can take riskier bets and pursue really, really cool drug targets.

That analogy—the VC-type investor model—is actually how we think a lot about research at Chai as well. Our research team is relatively small, definitely compared to a lot of the Isomorphic Labs and DeepMind. Our research team is around 10 people, so we’re a relatively small team, but we think of it almost like an investing job, where you’re investing ideas into compute. In the same sense, you’re really just capital allocators in that respect.

I actually think—maybe this is too cute—but I would make the broader point that we kind of think of everyone at Chai as a bit of a capital allocator. One of the things that surprises people is that we’re pretty small. We’re only 30 people, and that’s because everyone we hire onto the research or engineering teams, especially now that they’re, in some ways, very empowered with AI, is mostly allocating their attention to the right ideas and allocating their compute.

This is, I think, a characteristic to some extent of machine-learning and AI projects, and also of science. If you’re building an API for some B2B SaaS company that’s not building foundation models, whatever your limit is, it’s mostly people. The resource you’re allocating is almost entirely people, whereas if you’re building hardware, AI models, or something scientific, your constraint is those resources. The bottleneck is the lab, the compute, or other things. You have to really be in the mentality of, “I have these limited allocations. I have some shots on goal. How do I allocate those shots?”

Well, I would say yes and no. Going back to the example of building an API for a B2B company, that API has incremental costs. You have to support it. It adds complexity to the product. It’s another thing you have to take to market and sell. Maybe you should actually be allocating that to a different bet—a different thing on your product roadmap that you should be prioritizing instead of the other thing.

In a world where building things just gets really cheap and increasingly free, the scarce thing is your attention—both what you can put into it to keep your product simple and grokkable, and what your customer can put into it to really understand how to use it. I see it less as a binary thing and more as all of us, as engineers, becoming a little bit more like allocators of attention.

Yeah, which is what executives are. We’re all just becoming—

39. One bottleneck removed by fiat

Everyone—well, I mean, I was listening to a podcast with Satya Nadella. He says Microsoft wants to make everyone a manager of infinite minds. If you really take that to its extreme, everyone’s going to be an executive. I certainly feel like an executive, and I talk to Claude every day.

A little suite of interns who are all going out and eagerly solving problems you may or may not have actually wanted, but they’re solving the problems.

So, we have 2 typical questions that we ask. We’ve already kind of asked one, but I’m going to ask it again, maybe more directly: If you could remove a bottleneck from your problem space by fiat, what would that be?

That’s an interesting question. One thing that would be really nice—I’m always in research land; it’s very hard to turn off—is probably just the validation loop of protein design in general. Just being able to say instantly, “Hey, this thing works; this thing doesn’t.” There’s still a bit of walking around in the dark that you’re doing. At Chai, we’ve taken this very seriously, but it’s probably along the lines of just validating hypotheses and knowing for certain that things work.

Yeah, that’s an unsolved problem for sure.

Unsolved problem.

Yeah, and it would be hugely valuable.

Hugely valuable. I’m going to take a much more abstract answer to that, which is actually talent scarcity. There are a lot of smart people going and working on LLMs. There are a lot of people working and becoming software engineers for SaaS, but not that many smart people go and work on bio.

I didn’t work on bio in high school because I thought, “Oh, I could pick up my computer and program apps,” but if I wanted to work on bio, I had to study, get good grades in school, and maybe get a PhD or whatever. Maybe that’s one reason for it. I think another reason is that a lot of this stuff is really obscure. We threw around a lot of big words during this podcast, and you can’t really visualize the things.

It’s one of the things we care a lot about at Chai: how do we make the whole thing feel visual on our website and in the product? Part of the reason we’re here is that more people should realize you don’t need a super, super, super-specialist bio background to contribute to this computationally.

I think a lot about talent flows and where talent goes in the economy. In the 1990s, everyone was flowing to finance, and since the 2000s, people have been flowing to tech. Big tech ate up a lot of the talent until a few years ago, and now maybe LLMs and the big AI labs are eating up a lot of the good talent. At the meta level, how do you allocate talent better? Selfishly, I want more talent going into bio. We probably want more talent going into manufacturing and physical-world things, and these other problems that the US has. If I had a megaphone to talk to everyone, that’s what I would try to do.

40. Takeaways

Okay, so that leads to the second question. Maybe the answer is the same, but what is the 1 takeaway that you would like people to have from the episode?

Yeah, I think biology has been this somewhat obscure-feeling field where you’re stumbling around in the dark. You don’t know what you’re looking at. You’re dealing with non-determinism in your experiments, and you’re having to do a very long and iterative trial-and-error loop across a very long amount of time.

At some point, you cross the threshold of what you can do computationally—when you can get folding models down to being within an angstrom, and when you can get design models to give you hit rates north of 50%. Now you can put them in a 96-well plate and actually have 48 interesting binders. You start to get to the point where you can declaratively precision-engineer what you want, rather than betting on nature or trial and error to get you there.

I think we had the same thing happen in software, where you can write code and deterministically get an outcome, or in electrical engineering, where instead of your schematic being drawn out, you can put it in Cadence Design Systems and get it made in silicon, or CAD for mechanical engineering, where you can precision-engineer your part and get it printed or manufactured. The same thing is happening in bio, and it's happening very quickly.

Yeah. And that really opens the door for a lot of really interesting people for whom it maybe wasn't as scrutable or accessible before, right? Software engineers like myself, researchers like Matt. Obviously, we're still going to want the specialists, but the generalists can often really accelerate the precision engineering happening in the domain.

Yeah, I think for me, the biggest takeaway is that the field is actually working. Not only does it have commercial traction, but the research is actually showing signs of life. It's not even just showing signs of life—the signs of life have been shown. We're actually in a place where the models work. They're delivering value, and there are still tons of really interesting research problems to solve.

So I think there's a lot more low-hanging fruit in this field than there would be in other fields. And I think the amount of impact that you can have, especially as a researcher, is just unmatched in this field. For us, we're all very mission-driven. But even if you're not, there are a lot of fun puzzles to solve.

There's this kind of 3D geometry angle. If you like diffusion models, there's a million problems to solve in that regard. We have this LLM-like trunk in Chai-1. There's just so much of core machine learning that's touched by these problems. Although we've made a ton of progress, there's still a lot to be done. I think it's just one of the most interesting fields to be working in, while also having some of the largest impact on humanity.

Thank you so much for making the long journey. It's been a great—

22-minute walk.

Yeah. And we look forward to tracking Chai's progress. Awesome. Thank you guys. Thank you very much.