Dr. Emilia Javorsky

So scientists were hard at work developing mRNA technology for over a decade before COVID started and doing the safety testing and doing the regulatory submissions.

And so when COVID hit, there was already a decade of science and investigation and inquiry to build on to actually take that forward quickly.

So the AlphaFold story is the poster child of AI for science and AI in biology, as evidenced by it being an incredibly significant breakthrough to solve protein folding, something that has stumped humans for decades.

But as much as it is an AI story, it is a data story.

And that is the piece that I think often gets lost.

It's thought of as an AI breakthrough, but what actually enabled intelligence to unlock insights?

And that's where we find the story of the protein databanks.

So this was a database curated by scientists all over the world, over decades, that as they started to figure out what the structure of a protein was, and you have your sequence and your structure, they started uploading all of those images, all of that data of what the structure of the protein looked like and its sequence.

And so when you went to solve the problem and say, like, where could, you know, increasing AI capabilities or my new AI techniques that I'm playing with to develop new models be significant?

It's areas where you have this, right?

I want to understand how a sequence results in a structure.

And then there's a database where there is curated sequences and structures over decades.

So one of the reasons that protein folding is so significant in terms of the science, what does that actually mean for patients, is when we design new drugs or develop new drugs, they're designed to target a specific protein in the body.

And so think of it a little bit like a lock and a key, right?

If you want to go home and put your key into a lock, it has to be open and the key has to be the right size and fit there and open up.

And so we don't really know when we look at new targets, whether that keyhole is blocked, whether it's open, whether it's the right shape and size.

And that's what protein folding and solving that problem has enabled us to do, is to understand in advance, okay, I have the key and I can get to that lock.

So the piece, I think, of the AlphaFold story that gets lost is like, yes, there were new AI techniques and models built specifically to solve that problem.

But what enabled AI to solve that problem was having that data, those two pieces of the puzzle that it needed to actually derive, well, what is the relationship between these two things, sequence and structure?

Correct.