text: Summary:This work is about model evaluation for molecule generation and design.  19 benchmarks are proposed, small data sets are expanded to a large, standardized data set  and it is explored how to apply new RL techniques effectively for molecular design. on the positive side: The paper is well written, quality and clarity of the work are good.  The work provides a good overview about how to apply new reinforcement learning techniques for sequence generation.  It is investigated how several RL strategies perform on a large, standardized data set.  Different RL models like Hillclimb-MLE, PPO, GAN, A2C are investigated and discussed.   An implementation of 19 suggested benchmarks of relevance for de novo design will be provided as open source as an OpenAI Gym.  on the negative side:  There is no new novel contribution on the methods side.   minor comments: Section 2.1. see Fig.2 —> see Fig.1 page 4just before equation 8: the the