Review for NeurIPS paper: Guiding Deep Molecular Optimization with Genetic Exploration

Weaknesses: One of the strengths of generative algorithms for molecules is to capture a hard-to-describe statistical distribution of plausible molecules that can be made, paid for, stored in a vial, etc. There are many graphs that are formally valid according to valence rules (and their rdkit implementation) that could not exist as molecules because they are not stable. The premise of using generative models for molecular design is sampling natural-looking molecules. Just like generative models for faces, one just needs to look at this to judge whether the model has learned a richer chemistry than the hard-coded rules of RDKit. Very few molecules are shown from what the model produces.