Denoising Diffusion Is Still All You Need (Part 2)

4️⃣ DiffLinker from Igashov, Stärk, and EPFL / MSR / Oxford co-authors is the diffusion model for generating molecular linkers conditioned on 3D fragments. While previous models are autoregressive (hence not permutation equivariant) and can only link 2 fragments, DiffLinker generates the whole structure and can link 2+ fragments. In DiffLinker, each point cloud is conditioned on the context (all other known fragments and/or protein pocket), the context is usually fixed. The diffusion framework is similar to EDM but is now conditioned on the 3D data rather than on scalars. The denoising model is the same equivariant EGNN. Interestingly, DiffLinked has an additional module to predict the linker size (number of molecules) so you don’t have to specify it beforehand. The code is available, too!

Even more: SMCDiff for generating protein scaffolds conditioned on a desired motif (also with EGNN). Generally, in graph and molecule generation we’d like to support some discreteness, so any improvements to the discrete diffusion are very welcome, eg, Richemond, Dieleman, and Doucet propose a new simplex diffusion for categorical data with the Cox-Ingersoll-Ross SDE (rare find!). Discrete diffusion is also studied for text generation in the recent DiffusER.

We’ll spare your browser tabs for now 😅 but do expect more diffusion models in Geometric DL!