Nick Myerberg, Partner & Head of AI at Braidwell on AI's Next Era in Drug Discovery

And I don't think it did.

I think biology really spent more than a century creating representations and doing calculations that made a difference.

You can go back to the early 1900s and look at people like Darcy Thompson, Fisher, Wright, Haldane, Lotka and Volterra doing modeling for ecology and see the early seeds of people trying to produce this computational revolution in, at that time, kinds of biology that were very far from the cell.

Even more recently, if you look to the 60s through the 80s, we had this massive focus in computational biology and drug discovery in particular.

You had quantitative structure activity relationships.

You had structure-based drug design.

People have been trying for quite some time to think about how they can take a process that is I think arguably one of the hardest things that human beings do and bring some mathematical tools to the fight.

The thing that was different in those days versus the deep learning era, let's say through today, was that in those periods of time, people, they relied on these human chosen representations.

The scientists had to sit down and Say, for the problem that I'm trying to solve, how do I craft something that the machine can understand? And that would be molecular descriptors, force fields, similarity assumptions.

It's easy to look back at that and say, oh, well, that was silly.

Why didn't they build a big deep learning model? But in a lot of ways, that was an epistemic virtue, not a weakness.

The idea was that you had real assumptions that were encoded in these models, and they were real scientific knowledge.

And I'm not sure exactly what to pinpoint this pivot.

Let's say in the early 2010s, this deep learning revolution did something really important to that representational labor.

All of a sudden, you started having these machines that could start developing their own pictures of the biological processes we're trying to understand.

And this was the context in which you saw this first generation really of AI for bio companies, call them Emerge.

They were combining the previous generalist physical models with representation learning, with deep learning.

They were trying to fuse those things to push towards better understanding of ligand design, better understanding of how to do classic structure-based drug design tasks, or doing things like large-scale phenotyping through microscopy across many different, a way to enhance how high-throughput screening already worked.

We saw a number of companies form and show really strong results in some cases in phase one, and then falter as those drugs went through the pipelines.

We can talk a little bit about why that's true.

But I think suffice it to say, there was a massive amount of progress made on just understanding how to measure things at scale, how to build models that are suitable for specific biological questions, and then how to build organizations that brought together computer scientists, biologists, people from the business side, and think about how to build an AI-native drug discovery organization.

In more recent years, this move towards generative modeling really just changes one important thing.

It shifts from predicting supplied objects, say, hey, what do we think the PK and PD of this creating objects that satisfy certain kinds of criteria.

There's nothing too unusual, at least at a conceptual level of what's happening there.

I think the biggest change and the biggest point of evolution is this move towards systems that are, I hate to use the word agentic because I think it's overloaded and it's a little bit of a marketing term, but systems that seek to be more of a co-pilot, seek to own more of the experimental loop that have integrations with the physical world.