With my wonderful lab, who mostly aren't on here (except @lukasbillera.bsky.social and @antonoresten.bsky.social ?) we've been tinkering in this space since the end of the summer, but we think this is just too cool to sit on any longer.

The manuscript should be up by tomorrow and I'll drop a link.

Eg. here is a protein example where the model designs two domains with an intervening linker (light blue chain in vid). A regular flow model would need to know, early on, exactly how many AAs are needed in the linker, but Branching Flows can decide on-the-fly and grow or shrink it.

The process can be seen clearly with a QM9-trained model (continuous atom positions, discrete atom types), starting from a single atom.

Another infix, this time a nanobody CDR3.

And this is "infix sampling", but where you let the model figure out how many amino acids are needed to span the gaps.

Another binder, which threads a tail down a groove.

For example, here is what "binder design" looks like when you start from a single amino acid.

And here is a small molecule example.

We figured out flow matching over states that change dimension. With "Branching Flows", the model decides how big things must be! This works wherever flow matching works, with discrete, continuous, and manifold states. We think this will unlock some genuinely new capabilities.

RFdiffusion, Chroma, and others also generate symmetric structures. But, as far as we know, they only do so if some sort of symmetry constraint is imposed during generation. We aren't doing anything like that. This model just does it spontaneously!

After checking that we weren't just mistakenly looking at the training PDBs, we realized: these are, at least approximately, exhibiting symmetry. We saw dimers, dimers of heterodimers, trimers (though the model isn't as good at these) etc.

We tried to set up a simple demo/tutorial model for the protein design ecosystem we've been developing, and it turned out a bit more interesting than we expected. 🧵

This was a team effort from a few people in my lab, including @antonoresten.bsky.social and others (not sure who is on this app)

My lab, at Karolinska, in Stockholm, is looking for a PhD student with a computational/quantitative background to work on probabilistic/generative models of proteins (structure and sequence). The research will involve methods development, and applications in vaccine design.