00:13:59 · Sebastian Johnson, Oliver Vince
[00:13:59] S0: Yeah. That'd be amazing. Right? It's just a a such game changing way of approaching disease. When you talk about it, do you see yourself becoming a drug company, or will it always be partnerships with pharmaceuticals? [00:14:15] S1: I think in the long time, it will be both. I think that we will always focus on concentrating where we believe we can contribute, where people have already built the infrastructure, so they've built manufacturing plants, they've built hospitals. We should not try and rebuild them, and we should try and work with them. But where we believe, you know, for these AI models to have the most impact, they need new infrastructure or they need new ways of interfacing with the patients or they need new data and that sort of thing, then I think it's our priority and our responsibility to build it. And I... You know, it's a bit of a... We've built quite a lot of different things already, and I'm more focused on sort of the end outcome, which is demonstrate AI can cure disease. And it might need other things to help it get there, but it's the premise of that actually working. And, you know, all these pieces, you need to build AI, you need it to focus on curing, not just treating, and you need to actually get to the point where you're treating disease. It doesn't, like... It almost doesn't get more complex than that. So the... You need to do all of it, and I don't know what you identify as when you do all of that, but it's definitely exciting.
00:15:18 · Sebastian Johnson
[00:15:18] S0: And when I look at the landscape of other companies in this space, whether it's isomorphic in London and is also some in The US, maybe like evolutionary scale. It feels like, and maybe I've got this wrong, you've taken a different approach to data and training the models and that yours... You seem to be betting really big on building this in this almost a unique proprietary dataset, was I think a lot of those are the ones that are kind of using public data. Firstly, is that right? And then why have you taken this this different approach to some of the other companies in this space?
00:15:51 · Oliver Vince
[00:15:51] S1: That is correct. I mean, we are, for want of a better word, like, trying to build, like, general biological intelligence. Like, we're trying to build models that that get generally smarter, and I think other people have approached the problem a bit more, like, linearly. So a bit more saying, like, look. Here's how the drug discovery process works today. We're gonna build a model for this and a model for that and a model for this, and we're gonna sort of treat each one like a bit of a game, and we're gonna play each one, we're gonna get really, really good at each phase, we're gonna stick them together. And what that means is in each of the individual things, they get very, very good at that, but they sort of, in our view, you know, I think that's very promising, I don't, you know, I don't want to be negative on any of it, but I think that has certain limitations in the fact that you're replicating the existing process. Now we've taken a bit more of a leap and said, look. What if you just train this thing to understand biology and you learn the ability to talk to it? What would it be able to do for you? Right? And so an example of that would be normally when you treat antibiotics, you would take the pathogen, would you do a bunch of modeling on the pathogen, you'd find the target, you'd find the mechanism of action, you'd find the thing, you do the docking, and you do all these steps, and then you design a peptide or an antibiotic that worked against that. And then you, you know, you test the sort of five steps in that process, and you build a model for each one. What we did is we just built this model, and we say... We showed it lots of pairs of pathogens and, like, antibiotics and just said, here's a new pathogen. Give me a new antibiotic. And it just jumps from that, and ninety seven percent of the time it works for... In the lab, and it works, you know, equivalent to last line in in mice and that sort of thing. And we've no idea in principle, like, how it did that jump, but we know that it's able to jump from a to b. Right? So in kind of in the way that you're not quite sure exactly exactly how ChatGPT took your prompt and jumped to something at the output, but you know that it didn't, you can check that it did. We... Is...
00:17:40 · Oliver Vince, Sebastian Johnson
Rather than saying, the equivalent with ChatGPT would be saying, we're gonna try and understand, you know, what what your sentence structure is, and then we're gonna try and, like, understand the meaning of each word, and then we're gonna, like, reason our way through all those steps. We've just said, look. Train it to be big enough, scale it, and then ask it a question and see what it can do. And quite funnily and quite hilariously, like, when it's smaller, it can't do a lot of these things. Right? So you ask it the question, it just... It spits out nonsense. Yeah. And so it's kind of very simplistic in some senses. Obviously, it's, you know, a bit more... The team wouldn't like me saying [00:18:14] S0: It is scary that it's like black box of, like, we're not quite sure quite sure how it works, but it's just almost magical. Like, what was that like for you and the team, I guess, when you had those great 90% results come back?
00:18:26 · Oliver Vince, Sebastian Johnson
[00:18:26] S1: It's it's it's a pretty... Like, it's hard to... When you exist in a world that requires all that scientific rationale and has previously built on, like, you know, very, very rigorous experimentation to just take a new approach and just say, look, we're gonna do this to see what happens. Like, you know, it's a high risk kind of... But, you know, it's a rewarding way to be because it's not even rewarding in the result that you get today. So it's not a... The nine seven is is good, but it's not the reward. Right? The reward is that you have a direction of something that has got there in a different way. So, you know, you can get the same result by putting block... Lego blocks together, but the fact that you get to it using something that is completely unsupervised means that you you have a a technically feasible path to giving it more and more complex questions. And then back to my earlier point on cancer being a personalized, complex, and a moving target, it gives you the potential for something that one day could understand all those different parameters, think about them in a way that we don't understand that's far beyond our current comprehension, generate a solution, and, hey, presto. It works. Right? And we're not there yet, to be clear, but, like, that's the sort of, like, landscape that we open up by having a sort of unsupervised scaling. [00:19:40] S0: Yeah. I mean, it it demonstrates a real level of intelligence versus just a sort of, like, training it to respond to certain questions, I guess, or information retrieval. [00:19:47] S1: Exactly. Yeah.
00:19:47 · Oliver Vince, Sebastian Johnson
[00:19:47] S1: Exactly. Yeah. [00:19:48] S0: And so what... Why wouldn't this work? I mean, let's just say three years time, it doesn't work. Where will be, do you think, you know, the areas where this would have failed? Is it the data itself maybe not ever... Not quite having the scale of Atlas that you really wanted it? Is it the intelligence that maybe the intelligence can only be scaled so far? Could it be in the application itself? Where do you see at this point the biggest chances of failure being?