Jyunmi

Boltzgen is a generative model.

That means it does not just label the data.

It creates new examples that look physically realistic.

You feed it the 3D structure of the target.

Think of that as giving the AI a detailed map of the protein surface.

You can also give it a few design hints like stay small or attach here.

In return, Boltzten proposes brand new protein shapes and amino acid sequences.

An amino acid sequence is just the letter string that tells a protein how to fold.

The model tries to pick sequences that will fold up into a stable shape and hug the target at the right spots.

So the system takes, here's the thing we want to hit, and answers with, here's some new molecules that look like they could hit it.

This sits in the very front of drug discovery pipeline.

Instead of screening millions of random molecules in the lab and hoping something sticks, you let AI generate a small, smarter list of candidates first.

The key question is, does any of this actually work in the real world?

Well, the team at MIT didn't stop at simulations.

They picked 26 different targets and ran eight experimental campaigns in real labs.

That means they actually made some of the AI's designs and tested them in experiments.

On several easier, well-studied targets, the AI designs worked about 80% of the time.

And so that's a big upgrade over throw a huge library at the wall and see what happens.

More interesting are the hard cases.

On nine new, very challenging targets, they decided to be strict.