Reviews: Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules

I think the task is original and important for computational chemistry. The underlying generative model is a variant of SchNet that expands the molecule one atom at a time along with its distance to previous atoms. In that regard, the model is similar to GraphRNN (You et al., 2018), but operating over point clouds instead of graphs. The related work is mostly complete, but I think the author should discuss how is their method different from Mansimov et al., 2019, which is also a generative model for 3D molecular geometry. AFAIK, Mansimov et al.'s model only generates 3D geometry, while GSchNet learns to generate both the molecule (atoms and bonds) as well as their 3D geometry (distances).