Latent Space: The AI Engineer Podcast
00:37:43 · Fabian Theis, RJ Hanaki
They do realize that there's a whole bunch of opportunities at the moment, particularly with with this sort of computational angle getting... Yeah. I I would say pushed even more on the forefront that we can now start actually guiding experiments. We haven't really spoken so much about it. Right? But once you have these perturbation world models, you can actually go back and drive the experiments. Right? So I do think computational startups can be very good also data generation, and I think this is what will be also turned not just into nice papers, which is sort of mostly our business, but also I think it's next generation of startups. And you do see a few of these popping up where you actually get the lab and the loop really implemented. [00:38:18] S1: What do you see as some of the the the most promising areas for commercialization of sort of these Virtual Cell adjacent ideas?
00:38:28 · Fabian Theis
[00:38:28] S0: It's interesting, right? Like, if if you looked at earlier, and of course, this is what people always reflect to what OpenAI, Anthropic, and so on wanted to do. Like, they didn't really know how to make money, and some people might argue they don't even know nowadays, right? And there's a few startups that, you know, got really quite strong investment that also said we built a foundation model of biology, we decode biology, and so without actually saying what you wanna do with it. And I've been seeing this again and again that these platform type of ideas are super attractive. You know, they sort of fit into what we know. Mean, you spoke about some of the tools and platforms that we build like ScanPy and SEvers and so on, right? But turning this actually into a monetary thing is not as obvious. I mean, there might be some sort of consulting business, but if you say you build a Virtual Cell platform, typically what investors understand, and this is maybe even more so in the more conservative conservative VC VC ecosystem ecosystem in in Europe Europe than than in in US, is that at some point you develop a drug, and then you know it's a clear thing. So I saw so many of these platform companies pivoting at some point to we make a drug, and yeah, decide, of course, we have our our sort of Virtual Cell, whatever business, but, you know, we have two drugs that we found and now this... So if we put a lot of money in, maybe you have some other accounting assumptions, there's a few companies that sort of still stick with the scaling up of of the AI business, but it's not as clear how you would sort of directly turn this into a viable sort of really scalable business model, and if there's even room for many of these. And of course, then there's also this angst that at some point, like Astra two point zero might be doing parts of this already anyway.
00:39:54 · RJ Hanaki, Fabian Theis
[00:39:54] S1: Yeah. Mean, I think that it's actually this has been a thesis of mine that up until recently, every single company that builds a model and builds something ends up just building drugs, because that's the path to monetization over time. And that, like, whereas you you have to, like, land sales with pharma that's very difficult or some other big big pocket spender. If you're a tools company, if you say I'm gonna build a port... Like a... An asset, and then I can sell that off. That's really attractive to an investor. [00:40:27] S0: Yeah. [00:40:27] S1: But we've seen actually the opposite happening this year. Right? So you have like, you have instructional, you have a couple, you have bolts, you have Brandon will know them better, but there's a couple of those. And then, Tai is one that we talked to. And then we've interviewed a few that are doing this, doing reinforcement learning environment plays as well. I would love to see the activity level in structure that that we see in structural also doing things like what Noetik is doing.
00:40:27 · RJ Hanaki, Fabian Theis
[00:40:27] S1: But we've seen actually the opposite happening this year. Right? So you have like, you have instructional, you have a couple, you have bolts, you have Brandon will know them better, but there's a couple of those. And then, Tai is one that we talked to. And then we've interviewed a few that are doing this, doing reinforcement learning environment plays as well. I would love to see the activity level in structure that that we see in structural also doing things like what Noetik is doing. [00:40:56] S0: I mean, I I I think Noetik is is is a good example in the sense that they also have, I think, a rather nice scale of data generation. And a big part of that is also access to data. So, you know, we talked before about these organoid perturbation type of things, and we've been setting up actually in collaboration with a whole bunch of pharma, a consortium which we call the Cancer Organoid Perturbation Atlas, where then pharma actually buys in because they wanna get early access to some of these sort of perturbation type of datasets, Right? And I think some of these deals sort go in these these directions. So I think if you couple it with data generation, not just with the modeling part, I think this is where sweet spot hits where then also actually pharma gets buying. I think more and more of the companies I see, they sort of build their moat not just around a Virtual Cell model, but in combination with sort of unique sort of specific data. This is then, of course, also more value creation.
00:40:56 · Fabian Theis, RJ Hanaki
[00:40:56] S0: I mean, I I I think Noetik is is is a good example in the sense that they also have, I think, a rather nice scale of data generation. And a big part of that is also access to data. So, you know, we talked before about these organoid perturbation type of things, and we've been setting up actually in collaboration with a whole bunch of pharma, a consortium which we call the Cancer Organoid Perturbation Atlas, where then pharma actually buys in because they wanna get early access to some of these sort of perturbation type of datasets, Right? And I think some of these deals sort go in these these directions. So I think if you couple it with data generation, not just with the modeling part, I think this is where sweet spot hits where then also actually pharma gets buying. I think more and more of the companies I see, they sort of build their moat not just around a Virtual Cell model, but in combination with sort of unique sort of specific data. This is then, of course, also more value creation. [00:41:41] S1: I don't know if this is exactly correct, but I read that you said somewhere that you have your students follow what what you I think called a why model, in which you have to have a methods and a wet lab adviser. That sounds super interesting, super relevant to the conversation we're having right now about data being so important, and all the attention on lab work and that being moat for a lot of companies. Can you talk a little bit about that?
00:42:07 · Fabian Theis
[00:42:07] S0: Yeah. Thanks for raising this. So I've been setting up, and since then, it's been sort of become quite a big thing, what's called the Munich School for Data Science. Right? This is beyond just just bias. We had a whole bunch of other disciplines, and there we had this supervisor model that is always two PIs needed to come together to make a proposal, then sort of proposals to the students and a graduate student type of model, which has this this y. So there's like a method, more domain one, right, and then you supervise student. And sometimes we had two students under the y model, so y plus y equals h. You get it? But but I I think this idea is... It's somewhat even nowadays a bit trivial. Right? But I I thought it was good to bake this in so hard so that students really ideally go to two lab meetings, one lab meeting where they need to really sort of learn and understand some deeper biological problem, understand the language maybe of a medical setup or something like that, and then they go to computational. Typically, they're all computation data science type of students, right, But then they go to the computation one, they sort of get pushed the method aspect of it, and thereby sucking up a a whole bunch of of problems that are sort of not exactly precisely yet formulated in in that bio lab that sort of can't directly put that in a function, but then because they are sort of method y, right, and they're quantitative, they can actually put this into a metric or whatever that then turns into a model and in a paper. And I I think this is a way how... I mean, we've been doing this now for maybe eight years or so, this this type of school. I really... A whole bunch of papers came out where I I I learned about systems that I had no idea about this before. Right? You know, people approached me, you know, could we do something like this? And then, you know, I learned something about the lung or the brain that, you know, I had no idea about. But then I said, oh, hey. Cool data. I think our model types could fit. Let's build a project around it. And then those students became the experts in my lab for that sort of detailed biological thing together with a partner, and then we we drive this forward. And this actually nowadays extend also across the data types.