Review for NeurIPS paper: Goal-directed Generation of Discrete Structures with Conditional Generative Models

Weaknesses: The improvements over Maximum Likelihood are very moderate and no comparisons are made with more computationally expensive RL approaches (at least on the small QM9 dataset it would be interesting). It would be very interesting to see the performance tradeoff between the proposed approach and the Monte Carlo estimation of the expectation term in Eq 11. One of the promising features of generative algorithms for molecules is their supposed ability to capture a complex statistical distribution of plausible molecules that can be made, paid for, stored in a vial, etc. The approximately 100 million molecules that have been made and the couple of billions that can be confidently said to be makeable are samples from that distribution. It is not clear how much of chemical space is in that manifold.