Chai Discovery's Bitter Lesson: Drug Design Is Another Scaling Problem

20:05Matt McPartlon:So I think simplicity was one of the ones that Matt mentioned. It's this like bitter lesson pilness of like, you know, scaling compute and data and models. It's being really rigorous. That's something that's so important in this space. You can fool yourself so easily in biology. Like the error bars in the wet lab are actually quite large as well. So it's actually a little bit different than if you look at like cogeneration, for instance, if you look at Sweebench, people were like, oh, this is maybe like a year ago. People were like, oh, I got 16%, then 17%, then 18%. I mean, in biology, if you're like plus or minus 5 % in your lab, that might all be the same.

20:39Matt McPartlon:So it actually just means the bar is really high in terms of the step changes that you want to see with the models. But you also need to be really honest with yourself about whether you're making progress or not. So you could come up with some fancy model that looks like it works well in one or two new tasks, but it's very important to show that that works more generally if you're actually trying to build a product that can bring the field forward. Yeah. And that's, I think, pretty interesting because biology is one of those inherently not so verifiable domains. And you guys have been really good at sort of showing your progress to customers and to people like us who know very little about biology.

21:13Matt McPartlon:And so can we talk a little bit about the evals and the verifiable part of the model progress? How do you guys know that your models are getting better? Well, I would say, first of all, that I actually think this is one of the more verifiable domains. It's actually a very objective readout if you look at something even like CodeGen, right? Like maybe it's a verifiable task, like did my code compile? Did it solve these unit tests? But how do you think about the taste, right? Like did I write some really sloppy code that can't be maintained? Like what does that look like? When we think about designing a molecule in the lab, we can actually be quite specific about many of these properties, right?

21:46Matt McPartlon:So maybe we get a molecule that binds the target, but can I manufacture it, right? That might be your version of like some tech debt, but you can measure that. And I think those evals, again, actually make this domain more verifiable. Maybe it takes a little bit longer to validate it, right? It's not like five seconds to get a readout and run a unit test. You might have to spend a couple days, a couple weeks in the lab to get that readout. But at least you can be honest with yourself. Yeah. When we talk about progress and how good the models are, there's a domain of targets in biology that you can, as you mentioned, sort of address with traditional screening methods.

22:16Matt McPartlon:They take a long time. They're very slow and rudimentary. And then there are targets that just aren't addressable with existing methods. They're not druggable for whatever reason. And so where are we in terms of model progress, in terms of, you know, working on existing targets and generating molecules faster? That's one end of the spectrum. And then the other end of the spectrum is unlocking novel biology, new targets, and things that we couldn't drug before.

22:40Josh Meier:This kind of goes back to, like, kind of, like, our core modeling philosophy. So, like, there have actually been, like, plenty of times where, like, man, if we had a 24th module, like we can actually like unlock that new target. And we're like, is that really something that we want to maintain long term? Is this incremental or is this like actually a compounding improvement? Will this actually like help us generalize to the broader class of like these, this whole class of targets that we really can't hit? And so like our philosophy has been like, all right, let's just like continue to focus, like identify your scaling laws.

23:09Josh Meier:Like there comes a point where like if the model is able to push loss down even further, it has to understand something very intrinsic about the target that it's operating on. So one example we were talking about the other day, Paul and I, maybe the way that we're looking at certain glycosylation sites on proteins, we're like, oh, we might want to represent them differently or something like this. And we're like, well, even if we didn't represent them, there are certain sequence motifs that will tell the model there should be a glycosylation site here. And in order to drive Lost Down further, the model should just have to learn that.

23:40Josh Meier:So there are all these hidden features of targets where, if you really believe in scaling laws, you believe the models will get there, these types of targets should just unlock with better models. And of course, you still have to take this very seriously and you still need all the proper validation. You need to really challenge yourself and make sure that this is truly working. But I think our approach has always been with better models, we should be able to unlock a lot of these targets.

24:06Matt McPartlon:One of the other interesting things is if we look at, we talked about the interdisciplinary nature of this. If we look at the different teams at Chai, what people will call a hard target is actually different in literally every team. So on the science team, it might be, you know, like a undruggable GPCR target. No one's gotten something that has like, you know, modulated that in a functional way. On the ML research team, it'll be something like, oh, there's like the model just can't fold this thing up. It doesn't know what it looks like. And then on the product team, it might look like, oh, I've got all these like, you know, modifications, like my glycosylations and it's a membrane protein.

24:36Matt McPartlon:How do I represent that to the user? And actually the fact that it's different for each of these groups, I think is a feature rather than a bug. And it means that if we want to make broad progress over here, everyone is kind of pushing in parallel on these different ways. And that means that there's very smooth progress that we can make all the time. And there's usually not one bottleneck at Chai. It's not like, oh, if we only had that one extra module, things would work. Or if we only tried to push this into the product in some way, we could unlock it. We're trying to build this unified solution.

25:03Matt McPartlon:Because at the end of the day, the goal of the company is to build a computer-aided design suite for molecules. It's not to make one or two molecules. It's not to get a pipeline of like five interesting therapies that we bring to market, it's the change of the way that medicines are discovered. And if we're going to do that, we need to work on all the hard targets, regardless of how you define hard.

25:20Josh Meier:Say more about this idea of computer-aided design suite for molecules. What does that mean?

25:24Matt McPartlon:So at its core, it goes back to this point about making biology look more like an engineering discipline. So we're not going and fishing something out of a large library or doing a ton of trial and error. You want to be able to specify upfront the principles that go into designing your molecule. And then I have an engine that can actually realize that into some molecule that we're going to go and create in the lab. And look, you still might do some iteration on the lab and on your model because maybe your hypothesis was wrong. But what we want to speed up is actually, again, have that computer-aided design suite so that you can go from idea to testable hypothesis very quickly.

25:57Matt McPartlon:And if that loop right now takes something like nine months, I don't know, to go and discover your molecule versus if it takes nine weeks or it takes nine days, each order of magnitude just scales in a very big way the number of ideas you can really sort through. I think that's ultimately how the field is going to converge on better medicines. It really comes back to people sometimes talk about, do we care? And you kind of noted on it, it's not like, do we care about speed or do we care about the difficulty of the targets? At some point, they converge in this way as well. Because a hard target, if we can iterate through hypotheses a lot faster, then maybe it'll be easier to crack it.

26:31Josh Meier:So if we can maybe detach ourselves from the present reality and go far enough into the future that we're not thinking present forward, we're actually thinking future back, 2035, 2040, 2100, whatever you want. What does the industry look like? Like, let's imagine that computer-aided design suite for molecules has become a standard. Let's imagine a lot of innovation has flowed downstream into some of the wet lab parts of the process. Can you paint the picture of what the industry might look like 10 years from now?

27:03Matt McPartlon:Yeah, I think it's going to be a really exciting time. And we can look at this on a couple of angles. So first of all, the quality of the medicines that we develop will hopefully go up. There's a lot of molecules we put into the clinic today that it's really hard to discover a molecule. You find something that's like 80 % of the way there. Maybe I'm just going to advance it anyways to hit my timelines. It's probably going to benefit some patients. But then you get beat a year later by someone else. and it's really just not the most efficient spend of resources in the industry. You've got the kind of diseases that are just too hard to go after today.

27:33Matt McPartlon:People have been trying to drug Alzheimer's forever and unfortunately haven't made as much progress as we'd like. You have things that just aren't economical to go after today. Think about personalized medicines, rare diseases, things where maybe the patient population is going to be smaller. But again, if we can iterate through those ideas faster, if we can launch something faster, if we can do it in a less expensive way, then those probably come into reach as well. So I think there are just so many different axes that that we're able to push on. And I think that means that the future is really bright.

28:04Josh Meier:It used to be like you either want to be first in class or best in class. Yeah. Now it's like you want to be last in class because like you actually just want to like be the final answer. So I think like a lot of what you'll see is just like way more intentionality in the types of drugs that you're designing. Like this drug will be super specific to the disease of interest. It won't have like certain interactions, like certain negative interactions that a lot of drugs today do. A lot of this stuff is actually like you're able to model most of this compensation, like maybe not today, but there's definitely a path towards getting there.

28:34Josh Meier:And I think that's like one of the most exciting things for me. Very cool. Can we talk about a business model decision that you guys made? Because I think a lot of times folks think about chai and isomorphic and the same neighborhood. Isomorphic is developing drugs. You guys are enabling the existing industry to develop drugs more efficiently, better, faster, cheaper than they had before. Why did you decide to go down that path versus the isomorphic path?

29:00Matt McPartlon:First of all, I think both of these paths are great and they can create tremendous value. We've always been really excited about building infrastructure for the industry. Our bet when we started to go back to those results in Matt's house a few years ago was that this is how most future drugs are going to be discovered. And if that's the case, somebody needs to go and build that infrastructure to make it happen. I think part of this, too, is a lot of our founding team, including Matt and I, had worked in companies before where we had built these full stack drug pipelines. And we were building AI models.

29:31Matt McPartlon:We were putting those drugs into the clinic. Again, we're pretty bitter lesson-pilled as well. And our thinking was, as the models get better, we want to be spending more of the money making better models as opposed to diverting those resources into clinical trials and things like that. And one of the interesting things about our business models, as the models get better, it actually wins us the right to continue investing more in them. Right. And you have partnerships with with these pharma companies that are paying off today and allow us to invest in it. So it's a much more scalable business in that way.

29:58Matt McPartlon:And I just think about the ultimate impact that we can create for the world is a lot larger. We've we've always wanted to just partner broadly with the ecosystem. It goes back to this point about fooling yourself in biology. If you work on a small number of drug targets, you might come up with the most exquisite molecules, creating a ton of value for the world by doing that. But you might miss the forest for the trees because maybe your model doesn't generalize to the other 500 molecules that people are going to make that year. And if you go and partner with people, you just can't fool yourself.

30:26Matt McPartlon:Like you look at our partners, Eli Lilly, Novartis, Argenix, Pfizer. These are not companies that are, you know, they take this stuff for granted. Like you have to really deliver on these partnerships for them to take you seriously. And that means it can't just work like some of the time. Like when we ship models at Chai, they really have to work. They have to deliver value to our partners. and it's not like, oh, we made some molecule. It doesn't fully work. We're going to have our chemists like clean it up a little bit. So it's almost a harder business to pull off. I think that's one of the reasons why you haven't seen it pursued many times.

30:55Matt McPartlon:If you work on a drug pipeline, again, like there might be ways to fix things up later. There will be the proof of like what happens in the clinic, of course. But when you have this partnering based model, you have to be really rigorous about your models. Your models have to work really well because otherwise those partners are not going to come easily. So it's made our life harder, I think, in many ways. But I think it's also the more rewarding path if we can get it to work.

31:14Josh Meier:Well, and what have you learned? I mean, you know, you're out of the lab, so to speak, and in the real world, you know, delivering real value for actual pharma companies. What have you learned as you started to work with these partners in terms of any surprises in terms of how their needs might have differed from what you expected or how their level of sophistication around this might be different than what you expected? Have you any surprises or any learnings from working with these partners?

31:35Matt McPartlon:Yeah, so when we went into these partnerships, I think a lot of people told us that like pharma doesn't know how to use AI. These are not like a tech forward industry and things like that. And to be honest, that hasn't really been our experience. I think that these are, again, they're very rigorous partners, very rigorous customers, right? And they're going to test every one of our claims right before they start to deploy these things. But when they see the data, they go all in, right? Because pharma is an innovation industry. And it's interesting just to think about even the whole economics of the pharma industry.

32:04Matt McPartlon:If you build a product in pharma, right, like a drug, right, you only have exclusivity on that drug for a certain period of time, right? And you have to continue to reinvest. Eli Lilly is a trillion dollar pharma company right now. If they don't get more blockbuster drugs, they will not be a trillion dollar pharma company forever. And I think that forces these companies to really be on their game of adopting new technologies and deploying them and trying to stay ahead. Pharma is a very competitive arena. There's a lot of people trying to bring these drugs to patients. By the way, it's great for patients, but it means that you have to be on top of your game here.

32:36Matt McPartlon:And I think that means that once you're through that door and your models are working, we've seen an upscale this adoption very quickly and people thinking about how to use the models in incredibly creative ways.

32:47Josh Meier:I really like the incentive structure that it creates as well, like taking more of the partnership model. As our models get better, we get better results to our customers and so on. So I think that's just a nice side effect for us as well. Chai has been like incredibly focused. And like really as the models get better, you can kind of like iterate on a better model with better data and so on. So like a lot of what we see and like a lot of what our partners are using the model for, like generating binders, antibodies, so on. Like we're also like doing a lot of dog fooding in house. Like we have a whole science scene that's using the models and just trying to understand what they can do.

33:20Josh Meier:And a lot of that comes back to like, okay, now that like the model, now that we've unlocked use case X, like what type of new data can we generate? How can we make the model better that way? And really like that's been the long-term vision of Chai is like, we never thought of like, we're going to build this one model that's going to solve all of this. Like we know that they're just like in other fields, like they're going to be multiple iterations of this model. And as you get better and better models, like you get this flywheel effect, I guess. And you mentioned what data can we generate? Can we touch on data for a minute?

33:48Josh Meier:You guys can't exactly just go scrape the internet and have all the data you need to build your models. Where does the data come from? How does it kind of compound over time? Can you just say a word about data as an input to your models? Yeah. So I think the primary source of data or the gold standard source of data is this protein database. So this is like, it's actually just like legit lab scientists who since 1970 have just been depositing crystal structures of proteins and other molecules over time. And really without that, structure prediction and design wouldn't have been a thing. So there are these, so that's like one source of structural data.

34:30Josh Meier:When I started in the field, I really took like a structure-pilled approach. I was like super interested in predicting structure. Like, how do you think about a machine learning model that can output 3D coordinates? Like that's, that's not, doesn't look like an LLM. It doesn't look like an image model. This is really in its own class. Josh, interestingly, was taking like the exact opposite approach. So he, he was like one of the original authors on ESM and that was like a really like a seminal work and understanding how to apply language models to protein sequences. What's really cool about that work is if you can train a language model to understand protein sequences, what ends up happening is it ends up kind of like representing the 3D structure internally.

35:04Josh Meier:And there's like a really interesting reason why this happens. So like in order to predict like missing amino acids, like same way that you'd predict like next word in a sentence, I want to predict next amino acid in a protein. In order to do that effectively, like you really need to understand like, okay, what does amino acids like immediate microenvironment look like? because that kind of tells you, okay, what are like the compatible amino acids with everything surrounding it? And in order to do that well, you need to understand the protein's 3D shape. So I was taking like this really structure-based approach and Josh was even more bitter lesson to me.

35:34Josh Meier:He's like, we're just going to like look at the sequences and this is just going to emerge. So yeah, the two main sources of this data, again, like protein database for structures and then like these massive, massive, maybe even like order of like trillions of tokens sequence databases. And what you can do once you have really good models is just run them on the sequence database to get new structures out. So again, you have this compounding effect. As your models get better, they get more and more accurate at predicting these structures. And then you have more and more training data for the next series of models.

36:03Matt McPartlon:Sometimes people ask us at CHI about, these days at CHI, like which paradigm are you actually going after? I think one of the things I love about our team is that it's actually just like neither. We're very pragmatic. We want to solve the problem. We don't really care is it a sequence approach, is it a structure approach. In practice, it's going to end up being both, of course. I think you'd be surprised, but there might even be more like biological sequence tokens on the internet than like English language tokens. Now, a lot of that data is not that useful. It might not be redone. It might be very noisy, but there's a lot of data out there.

36:31Matt McPartlon:There's a lot of art in bringing it together. I think also the exciting thing is as the models have gone into a point now where we can design things in the lab, write to new targets, for instance, we can actually use the models to generate data as well. So there's a lot of exhaust from all the experiments that we're doing at CHI, which also helped to make the models even better. So it's a similar kind of takeoff that we saw with LLMs. Like when I was at OpenAI, we worked on reinforcement learning of like a GPT-1 architecture. Like did not work because the models weren't like good enough. But once the base model got good enough, then you could start to do those kinds of experiments.

37:01Matt McPartlon:And I think there's a similar analogy that's starting to happen in our world now as well, where the models have reached a point where there's actually a renewed interest in data. And like, how do we actually bring the models into the loop on like making that happen? I think that creates another really like interesting cycle on compounding improvement of the models. Yeah. We're talking a bit about compounding improvement in the models. And you said something about how pharma as an industry is an incredibly competitive landscape. And it's interesting because I think there's been a renowned interest in using AI and using ML to generate proteins and molecules.

37:30Matt McPartlon:And so your arena has actually become quite competitive. And you guys have obviously done an amazing job at staying at the frontier. It's been the year of deployment for you. You've locked up a number of pharma partnerships that are making your models better. But how do you guys think about the competitive landscape and staying at the front of the frontier? Well, first of all, I think it goes back to looking at the results and not fooling yourself and being rigorous. So one of the reasons why we do a lot of evaluation of the models, we mainly do it just to hill climb in the models itself. I think if you look at many of the capabilities that we've brought in have kind of been like first in the field, you look at our CHI 2 model, right?

38:04Matt McPartlon:Like getting to success rate of the models that you didn't have to do large library screening anymore to see results. A couple of months later, showing how we could bring in a lot of these developability properties we've talked about before, like the manufacturability of the molecules. So in many cases, we are pushing the model forward and trying to see these capabilities emerge. And then we try to quickly lock those in. How do we make those capabilities even more pronounced so that they become production ready and we can ship them to our customers? I think one of the things I like to tell the team is that it's not like we are head to head with other model providers or something like that, where all of us, I think, are working against nature.

38:39Matt McPartlon:Nature has actually been a pretty good baseline. People have done drug discovery a certain way for a long time. And Matt talked about how maybe you don't want to add module 24 to the CHI model, but people have added module 240 to the existing wet lab protocols, and they have been tuned quite considerably. One of the scientists on our team, Andy Young, he was one of the first people working on yeast display at MIT actually two decades ago. And he's got 20 years experience in Pfizer and Genentech, really honing in these methods, has a drug approval to his name and antibodies. And I think you look at someone like that and like, you know, Andy knows how to make a good antibody with existing tools.

39:15Matt McPartlon:And that is actually the bar that we need to clear. Now, of course, I think the ceiling on AI is going to be a lot higher than what we've managed to do before. Otherwise, what would be the point of doing this? We didn't start the company just to make, you know, a 10 times faster mouse, right? We started this to make breakthrough medicines that weren't possible before. But ultimately, that is the bar that we needed to clear in order to get adoption. I think we hit that inflection point a couple months ago. That's why you've seen a lot of these big pharma announcements. But now we just need to continue to hone in on making these things even better.

39:44Josh Meier:For somebody who's listening who thinks, wow, this sounds pretty cool. I wonder what it would be like to work at CHI. What is the best thing about working at CHI? And what is the worst thing about working at CHI? Yeah, I can speak to some of this. I'll think of this on the fly, the worst thing. So I think like the best thing is just like how actually like mission driven everybody is. Like everyone is so dedicated to what they're doing. I've worked at other companies, like the closest I've ever seen to this is like maybe some of the guys in my PhD lab. But like everyone is just like incredibly, incredibly motivated.

40:20Josh Meier:We all work really hard. There's like an obvious shared goal. And I think that's really rare to see. And I think this goes back to just like the focus that we've had since the beginning and like we've always had like kind of a clear philosophy a clear plan on how things are going to get there how things are going to get better and really like every night chai is very bought into this it's pretty amazing just to see like the amount of dedication that that everyone's putting in uh least favorite thing about chai uh not directly on top of dandelion chocolate maybe no maybe the next office yeah

40:51Matt McPartlon:Yeah.

40:53Josh Meier:Actually, so probably the least favorite part now is just like, I guess it's getting things to work at scale. And really, I didn't even know what that meant. Actually, when we started HCI, we had 128 GPUs. And I was like, this is like the most scale possible for a lab. Like, this is crazy. I came from like, you know, my PhD group where there were four of us sharing eight GPUs. And I was like, I just felt GPU rich. It was crazy. So now, at CHI, we have a lot more infrastructure to maintain. We have a lot more compute resources. Luckily, GPUs are parallelizable, but that also brings up its own set of problems.

41:29Josh Meier:So just how do you keep a cluster healthy over time? How do you get that large training run? So how do you keep that running for months on end? And even when it does die, how do you automatically resume these things? How do you keep all of your communication down? How do you optimize the models and make the best use of the resources that you have? So I think these are a lot of problems that they continuously pop up. They're good problems to have, but I think they're also really difficult to solve. Yeah, and I'm just excited to work on this. Yeah, I remember one of the CEOs that we've worked with a couple times, Frank Slootman, had this line about, you either have the pain of failure or the pain of growth.

42:03Josh Meier:You'd much rather have the pain of growth. Yeah, that's exactly right.

42:06Matt McPartlon:I think my favorite part is probably the results. And that sounds a bit cliche, but there's nothing. That's working. It's working, right? And just knowing that you're – I think many of us in the company, we've been working in AI for a long time. There's all this experience you've built up. And to know that you're applying it to something that really matters, like just even – I think just take Matt and – like we've been working on this problem for like 10 years, right? And a couple of years ago, I'm looking at something like, yeah, we're writing some cool papers. Like everyone is like celebrating this.

42:33Matt McPartlon:Are we actually making the world better? Like is this actually going to impact some patients? And I think now the answer is like actually yes. Like we have reached the point where this is going to make a big difference in the world. And every time you get one of these breakthrough results, anytime there's a new feature on the product that makes our lives of our customers easier, whenever there's like new lab results coming back from the science team, it's just always so honestly exhilarating to realize like this is actually going to change the world in a pretty profound way. I think that's also then maybe comes to the least favorite side, right?

43:03Matt McPartlon:Like, you know, we're running the company and it's like we have real partners that are relying on this and like things have to work, right? And you ship a new model generation. How do you make sure that there's no bugs in that? How do you make sure you don't have regressions, right? This is no longer just like a, again, the blue sky research problem of like, oh, we got some cool results and we move on. We've had to have really high priorities on like, you know, having production level code bases as the team grows. How do we make sure that the code base is in a state that more people can contribute to this?

43:28Matt McPartlon:So something that one of our other co-founders, Jack, likes to say is that if you want to move fast in the long term, you sometimes have to just move a little bit slower in the short term, right? and make sure that you are building something, again, that goes back to that compounding idea. It goes back to, we don't add module 25 to make the next thing work. So sometimes, you know, you're like so excited to get to the next result and you just want to jump into it. But we have real partners, some of the biggest companies in the world that are now relying on us. And it's important that we realize that, we take that responsibility to heart and we make sure we're building systems that, you know, continue to work.

44:03Matt McPartlon:I have a burning question. Why are you guys called Chai Discovery? Chemistry and AI. But we love Chai-Chi as well. It's a lot of Chai-themed stuff in the offices.

44:12Josh Meier:That's a good question.

44:13Matt McPartlon:I didn't know that either.

44:14Josh Meier:Josh is the visionary. That was all him. It is a very user-friendly name.

44:18Matt McPartlon:Yeah. We also wanted a name that like biotech companies have such complicated names. Yeah. We wanted something that's going to be much simpler. Yes. We're trying to make this whole thing simpler, right? Yeah. So we need a simple name to go along with it.

44:28Josh Meier:Awesome. What are you guys most excited about in the next six to 12 months?

44:33Matt McPartlon:I think for me, it's just the deployments that are happening. So we've announced a couple of these partnerships, and I'm really excited just to hear about the results that our partners are bringing online. It goes back to this point of making a real difference and also why I'm so happy to see how these partnerships are going, even posting the agreement. And as we're working with these folks, the models are not just sitting on a shelf somewhere. They're actually being used on real programs, people trying to approach devastating diseases where, you know, if CHI could give them a molecule that works, it could really change the lives of patients.

45:06Matt McPartlon:So I'm really excited to see how that goes. Just the pace of progress here is incredible, but also just like the pace of the models. Like a year ago, you could not zero shot a molecule and like, you know, have a good sense that your program was going to work. Like now that's changed. Someone might zero shot a molecule and be like, I think we're going to bring this program to the clinic now. And then a year later, they might even have some of those first molecules going into patients. And just the speed of that is just incredible. And sometimes you get some shivers thinking about this, that like, okay, my model is going to...

45:36Matt McPartlon:Matt has some patents from our last company about just generating the molecule on the computer, and these things are now in patients. And just to think about the scale, I don't know, a few years from now, do we have dozens? Do we have hundreds of chai molecules going into people? It's a bit mind-blowing to think about what that might look like for patients.

45:53Josh Meier:I think one of the things that, again, what makes Shai unique, the dedication, people are like, man, you work a lot. And aren't you burnout or whatever? It's actually really easy and it's very motivating when you're making progress. Seeing the progress they were making and just thinking, man, the next model is going to be even better than the last. We identified this new thing, so on. That's so incredibly motivating. For me, it's more of just like what what can we unlock next and like how do we make these things more controllable and like when someone comes to us with a certain target instead of just like hoping we get good affinity or something like that you know can we actually control this can we say like we want exactly a 10 nanomolar binder things like that like there are a lot of technical things that i think are we're like kind of right on the brink of solving um and for me it's like it's really motivating just to like pin those things down and just like get all of this over the line and see kind of where that leads to next.

46:43Josh Meier:Awesome. Matt, Josh, thank you for engineering biology and thank you for sharing your story with us today. Thank you guys. Thank you. Thanks for having us.

47:12Thank you.