Lex Fridman: belief

Transcript context

…Yes, that’s right. So when you do real Blue Sky research, there’s no such thing as failure really. As long as you are picking experiments and hypotheses that meaningfully split the hypothesis space and you learn something. You can learn something kind of equally valuable from an experiment that doesn’t work. That should tell you if you’ve designed the experiment well and your hypotheses are interesting, it should tell you a lot about where to go next. And then you’re effectively doing a search process and using that information in very helpful ways. So to go to your dream of modeling a cell, what are the big challenges that lay ahead for us to make that happen? We should maybe highlight that in AlphaFold, I mean there’s just so many leaps. So AlphaFold solved, if it’s fair to say, protein folding. And there’s so many incredible things we could talk about there, including the open sourcing, everything you’ve released AlphaFold 3 is doing protein, RNA, DNA interactions, which is super complicated and fascinating. It’s amenable to modeling. AlphaGenome predicts how small genetic changes if we think about single mutations, how they link to actual function. So it seems like it’s creeping along to sophisticated to much more complicated things like a cell. But a cell has a lot of really complicated components. So what I’ve tried to do throughout my career is I have these really grand dreams and then I try to, as you’ve noticed, but I try to break them down. It’s easy to have a kind of crazily ambitious dream, but the trick is how do you break it down into manageable, achievable, interim steps that are meaningful and useful in their own right? And so Virtual Cell, which is what I call the project of modeling a cell, I’ve had this idea of wanting to do that for maybe more like 25 years. And I used to talk with Paul Nurse, who is a bit of a mentor of mine in biology. He runs the founded the Crick Institute and won the Nobel Prize in 2001. We’ve been talking about it since the nineties, and I used to come back to it every five years. It’s like, what would you need to model the full internals of a cell so that you could do experiments on the virtual cell and what those experiment in silico and those predictions would be useful for you to save you a lot of time in the wet lab. That would be the dream. Maybe you could a hundred x speed up experiments by doing most of it in silico the search in silico, and then you do the validation step in the wet lab. That’s the dream. But maybe now, finally, so I was trying to build these components, AlphaFold being one, that would allow you eventually to model the full interaction, a full simulation of a cell, and I’d probably start with a yeast cell. And partly that’s what Paul Nurse studied because the yeast cell is like a full organism, that’s a single cell. So it’s the kind of simplest single cell organism. And so it’s not just a cell, it’s a full organism. And yeast is very well understood. And so that would be a good candidate for a kind of full simulated model. Now AlphaFold is the solution to the kind of static picture of what does a 3D structure protein look like? A static picture of it. But we know that biology, all the interesting things happen with the dynamics, the interactions, and that’s what AlphaFold 3 is, the first step towards is modeling those interactions. So first of all, pair wise proteins with proteins, proteins with RNA and DNA. But then the next step after that would be modeling maybe a whole pathway, maybe like the tour pathway that’s involved in cancer or something like this. And then eventually you might be able to model a whole cell.…

Stored transcript either side of the excerpt. The highlighted words are the published quote; the surrounding text is unedited source, never generated.