Marc Tessier-Lavigne
speaker
407 appearances
1 recordings
1 series
first heard Mar 2026
last heard 12 Mar
Marc Tessier-Lavigne’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 1 in all, peaking in Mar 2026 with 1.
Appearances
Again, one of the key steps in making drugs.
So David showed how one could use AI to do that, and they shared the Nobel Prize.
So that gives you the backdrop.
There's this very large database that can be trained on to predict the shape of proteins and how proteins interact.
It can be used also with further refinements to create entirely new proteins.
And that is what has enabled this explosion of activity around applying AI to a particular aspect of drug discovery.
So here, Sam, if it's okay, I'm going to walk you through the drug discovery more generally and where this piece fits in.
Then I'll come back to your question about how what we're doing differs from what isomorphic and others are doing.
So that's great.
Let's take it in stages, Sam, if that's okay with you.
First, let's look at the drug development process as a whole.
You have to do three things to make a drug.
First, you have to choose the molecular target.
Typically, it's a protein in the body that has a particular function, and it might be a good actor, it might be a bad actor, and you're going to make a drug that will have an intended effect.
So, for example, the GLP-1 drugs that everybody's familiar with,
like Ozempic, they function to switch on a good actor, a protein called the GLP-1 receptor in the cells, in the body's cells.
So that's switching on a good actor.
In cancer, often there's a bad actor, a protein that's been gone awry through mutation, where what you want with the drug is to switch it off.
So you first have to say, what's the molecular target?
Then you have to make a drug that has the intended effect, typically of switching it on or switching it off.
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