Scaling Europe
00:07:47 · Sebastian Johnson
[00:07:47] S0: Oh, yeah. Absolutely. This is, yeah, an order of magnitude more complicated and more regulated and a whole other host of issues I imagine. But when you talk about specific diseases, spoke about cancer, autoimmune problems, do you think there are certain health issues, whether it's bacterial infections or cancers or degenerative brain diseases, where this type of technology that you're developing can have the impact first?
00:08:14 · Oliver Vince
[00:08:14] S1: I... So the... What we are at a very simple level trying to do is learn to read and write DNA. And so it will be applications where DNA has a primary driving cause and that the solution can be found by designing DNA. So examples of that include genetic medicines. So this is something that, you know, Anthropic has talked about going after, which is their sort of rare disease or neglected disease piece. But what that basically means is a disease where a piece of the DNA has gone wrong or maybe not gone wrong, but it's it's sort of changed in a way that is adversarial or causes, you know, some form of illness in the patient. And, effectively, you know how to fix it, but it... You have to do a very specific fix for that person, and that's sort of normally, at this point, expensive to do. And if you can get an algorithm to figure out how to fix that piece of broken DNA, then you have a solution. In cancer, it's sort of similar, slightly different, slightly more challenging. But in cancer, it's still a disease of the DNA very fundamentally. You know, your d... Your DNA has gone wrong, and your body has not recognized that mistake, and so your body has not recognized to shut that down. And what you're trying to do is teach the body to recognize that. And so when we say curing cancers, it's it's less about we're gonna develop another chemotherapy, and it's gonna hit it even harder. It's saying everybody gets cancer all the time because every day, you know, your your cells mutate and your cells divide in ways that are not great, your your immune system shuts it down most of the time. And so in a scenario where you get the disease like that, what you need to do is teach the immune system to say, hey. Look. Look. This is bad, and go after it. And then you really can just, like, restore your body effectively back to its healthy state, right, which is what you're trying to do. So that's all... Not all. A very simplistic way you can think of it is all just recoding the DNA. And then, you know, you've got things like the antibiotic model we've already produced, which is where the pathogen is in DNA and then the the the antibiotic is also in DNA, so you can sort of figure out how to get it to work on that... On those sorts of problems. There's a whole host of things you can do.
00:10:04 · Oliver Vince, Sebastian Johnson
It doesn't mean that DNA is the only thing you ever need to do or it's the only thing you'll ever want to do, But if you can figure that out, there's a long way to run. [00:10:12] S0: And can you talk about where, I guess, where you're at in this process? So I know that a large part of this announcement and this this money is gonna take you, I guess, towards more clinical development, But you've been gathering this data, you've been training the models. The models I understand have also been used to some extent within cloud science. So where are you? You know, where do you at? Where where are you at today? Where have you seen people taking and running with some of these models or designing things? And where do... What what do you need to do to get to clinical development?
00:10:39 · Oliver Vince
[00:10:39] S1: Yeah. So we we kinda have three parts of the the business. So we have the data expansion piece, that's basically all data collection piece. We're still doing that. We're still working building partnerships around the world. These different companies recently signed some in some very exciting locations that we're gonna talk about soon. We have also... You know, that's what the Trillion Gene Atlas is based on, and that's grown... And that's on track. That's growing very, very quickly. It's on track to be completed by the end of twenty twenty seven. Then the sort of... We're still scaling the models. We're still building newer models, getting better at those models, and evaluating them on tasks that people previously haven't done, and that includes tasks that we... What we call long horizon. So rather than saying to the model, can you design me one molecule that does this? It's saying, can you think about the patient? Can you then, you know, analyze, you know, what needs to be done? Can you then design a molecule? Can you then assess safety? And then can you do all these things? And what you're working towards is not a model that just does one task at a time, but in the similar way to, like, you know, these these frontier models today, you you input a patient file on one side, and out comes something that's on the validation on other side. Now to be clear, we've got versions of that such as the antibiotics and Claude Science, but we're at the point where it can do the whole thing yet, there's a lot of work to do, understand that. But then there's sort of validation that we are actually at the point where we can start treating or we think we start getting close to treating patients. We have this pipeline of medicines that we are working on, we are developing, and that, you know, we'll... We will work with pharma companies on to to develop further. And and there... There's all this sort of stuff to be built. So back to sort of realistic and optimistic. Like, realistic is kind of like, you know, we've got a very, very strong validation of all the technologies, but we've not yet really lent into it at the scale that will be required to to deliver the impact of this technology to those who need it.
00:12:26 · Sebastian Johnson
[00:12:26] S0: And is that a point in time that you look forward to is like the the very first patient? You know, is that something where where you have that milestone? What does that look like working backwards? Like, how far off is that?
00:12:38 · Oliver Vince
[00:12:38] S1: It's hard to say exactly, and it's also difficult... You don't want to promise things because it... You know, it's people's lives. Right? So you you don't want to... But but it's it's... You know, in the next couple of years, I expect we'll be there. And, you know, my hope is that when we are doing this, it's medicines that that are curative. So they're curative for diseases that previously or currently cannot be treated. And they start to demonstrate the potential of AI in this space, which is that, you know, in my view, even if your first medicines sort of equal or match what can be done today by humans, the fact that an AI model has got there means that if you continue to work on it, it will go through that. And And again, realistic and optimistic, right? Realistically, we don't know exactly how that's going to scale, how that's going to work, but optimistic that there is a better way of treating disease than we have today, and that if we can get that and we can invest in this technology, you know, we can get to a point where, you know, certain diseases which are very commonplace today and are absolutely disastrous are kind of not a thing anymore. As in, the people will still get them, but in the same way that most infections in the West, at least, they're not a thing. As in if you get an infection, you go to the doctor, you get some antibiotics, whereas previously it would have killed you. We hope to move more diseases into that space, basically.