Jyunmi

So it's still fragile and young, but very promising.

So let's zoom out and take a look at how does this fit in the larger AI and science picture.

So at the micro level, inside drug discovery, this is another attack on a very specific pain point, finding first generation binders for hard disease targets.

At the missile level, across biology and chemistry, it joins a wave of models that do not just read the data, they propose new things.

I believe in the previous weeks, highlighted materials science and work for fusion reactors in magnetic fields and those kinds of rare earth materials and testing for other materials.

And at the micro or macro level, this points to shift to how we're going to be doing science.

So for decades, a lot of lab work's been brute force, right?

Try a big library, screen everything, then narrow it down.

So the pattern here is a little different.

We ask the model what is the most promising thing to test, then spend that lab time on that shorter list.

And that changes who or what decides what to test next.

There's also quite an economic story underneath this, right?

So tools like Boltzgen raise the question, if strong open models for molecule design keep arriving, what does that do to companies built on selling this as a proprietary service?

Because there's been quite a few announcements over the last few months where their whole proposition is to

build AI scientists and get through this design and testing part of the system.

And what does this do for smaller labs that suddenly get access to this kind of design power?

We should also take a look at possible friction and risk, right?

So there's data and bias.

So whichever targets were well represented in the training data will get better AI help.

Targets tied to neglected diseases may lag behind, which could amplify existing health gaps.