Demis Hassabis

And I think we could reduce down drug discovery from taking years, sometimes a decade to do, down to maybe weeks or even days over the next 10 years.

We're building up the platform right now, and we have great partnerships with Eli Lilly.

I think you had the CEO speaking earlier, and Novartis, which are fantastic, and our own internal drug programs.

And I think we'll be entering sort of preclinical phase sometime next year.

That's right, and we're working on cancers and immunology and oncology and we're working with places like MD Anderson.

Yeah, it's a great question.

Actually, for the moment, and I think probably for the next five years or so, we're building what maybe you could call hybrid models.

So AlphaFold itself is a hybrid model where you have the learning component, this probabilistic component you're talking about, which is based on neural networks and transformers and things.

And that's learning from the data you give it, any data you have available.

But also, in a lot of cases with biology and chemistry, there isn't enough data to learn from.

So you also have to build in some of the rules about chemistry and physics that you already know about.

So, for example, with AlphaFold, the angle of bonds between atoms.

And make sure that AlphaFold understood you couldn't have atoms overlapping with each other and things like that.

Now, in theory, it could learn that, but it would waste a lot of the learning capacity.

So actually, it's better to kind of have that as a constraint in there.

Now, the trick is, with all hybrid systems, and AlphaGo was another hybrid system where there's a neural network learning about the game of Go and what kind of patterns are good.

And then we had Monte Carlo Tree Search on top, which was doing the planning.

And so the trick is, how do you marry up

a learning system with a more handcrafted system, bespoke system, and actually have them work well together.

And that's pretty tricky to do.