GraphML News, April 16th edition - Generalist Medical AI, more diffusion papers

No particularly outstanding Graph ML event or announcement (that we hadn’t covered before) happened this week, so here is a collection of fresh papers you might want to have a look at:

Foundation models for generalist medical artificial intelligence - perhaps a landmark paper on using foundation models and many its exciting applications like generative models (eg, text-to-molecule or text-to-protein) in real world medicine.

DiffDock-PP: Rigid Protein-Protein Docking with Diffusion Models - extension of the famous DiffDock that translates and rotates unbound protein structures into their bound conformations.

Graph Generation with Destination-Driven Diffusion Mixture - the next version of the score-matching GDSS generative model (ICML 2022). Here, the model learns to “keep in mind” the final destination of the diffusion process at each time step - this trick greatly improves the performance in 2D and 3D tasks.

DIFUSCO: Graph-based Diffusion Solvers for Combinatorial Optimization - turns out discrete diffusion on graphs is able to generate very strong priors for combinatorial optimization tasks like Traveling Salesman or Maximum Independent Set when paired with a postprocessing solver.

GraphGUIDE: interpretable and controllable conditional graph generation with discrete Bernoulli diffusion - another take on discrete diffusion on graphs where authors define Bernoulli noising process as adding/removing/flipping edges instead of marginal transition probabilities mined from data (like in DiGress). Strength of that approach is that any intermediate state with added noise is still a legit graph retaining its sparsity instead of adding direct noise to node features or adjacency matrix.