Marc Tessier-Lavigne

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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 over the last 12 months — 1 in all, peaking in Mar 2026 with 1.

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Vanguards of Healthcare by Bloomberg Intelligence · Xaira’s War on Trial-and-Error Drug Development · 12 Mar 2026 podcast

You know, choosing clinical trial sites, for example, or helping, you know, investigators write applications to the FDA or reports, things like that.

That's happening in a very rapid way.

People have adopted the tools, large language models and so forth.

to accelerate and improve operations and logistics, just as they are in every single industry, not just healthcare.

So that's terrific.

That will drive productivity gains.

And there's some great companies that are doing great work there.

That's not what we're focused on.

We're focused on those three steps, the scientific steps of target, drug, patient, identifying the right ones.

So how will AI help in all three of those?

For choosing molecular targets, what we need to do is develop

biology models, foundation models of biology that understand biology at a causal level.

Currently, if you have a cell in a diseased state and you want to bring it back to a healthy state, the way we go about trying to figure out what molecular changes to make to do that are typically involve a lot of trial and error.

Sometimes some kinds of molecular screening, but they're limited in impact.

And we believe, I think with a lot of justification, as does the field, that again, with the right kinds of data, by generating a lot of causal data, we should be able to set up a causal model of cells so we can address that question in silico on the computer.

What set of changes to make to bring the cell from a diseased state to a healthy state, which should improve target identification.

In terms of making drugs, there are actually some quite good methods for making drugs currently, typically that involve high throughput screening of various kinds.

I think your listeners will have heard of high throughput screening.

You have a protein, you want to find a chemical that attaches to it or an antibody, a biologic.

So you screen large libraries of compounds or large libraries of biologics, often in the form of

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