LEARNING MULTIMODAL GRAPH-TO-GRAPH TRANS- LATION FOR MOLECULAR OPTIMIZATION
We view molecular optimization as a graph-to-graph translation problem. The goal is to learn to map from one molecular graph to another with better properties based on an available corpus of paired molecules. Since molecules can be optimized in different ways, there are multiple viable translations for each input graph. A key challenge is therefore to model diverse translation outputs. Our primary contributions include a junction tree encoder-decoder for learning diverse graph translations along with a novel adversarial training method for aligning distributions of molecules. Diverse output distributions in our model are explicitly realized by low-dimensional latent vectors that modulate the translation process. We evaluate our model on multiple molecular optimization tasks and show that our model outperforms previous state-of-the-art baselines.
Update:
The score has been updated to reflect the authors' great efforts in improving the manuscript. This reviewer would suggest to accept the paper now.
Old Review Below:
The paper describes a graph-to-graph translation model for molecule optimization inspired from matched molecular pair analysis, which is an established approach for optimizing the properties of molecules. The model extends a chemistry-specific variational autoencoder architecture, and is assessed on a set of three benchmark tasks.
While the idea of manuscript is interesting and promising for bioinformatics, there are several outstanding problems, which have to be addressed before it can be considered to be an acceptable submission. This referee is willing to adjust their rating if the raised points are addressed. Overall, the paper might also be more suited at a domain-specific bioinformatics conference.
Most importantly, the paper makes several claims that are currently not backed up by experiments and/or data. 
First, the authors claim that MMPs “only covers the most simple and common transformation patterns”. This is not correct, since these MMP patterns can be as complex as desired. Also, it is claimed that the presented model is able to “learn far more complex transformations than hard-coded rules”. The authors will need to provide compelling evidence to back up these claims. At least, a comparison with a traditional MMPA method needs to be performed, and added as a baseline. Also, it has to be kept in mind that the reason MMPA was introduced was to provide an easily interpretable method, which performs only local transformations at one part of the molecule. “Far more complex transformations” may thus not be desirable in the context of MMPA. Can the authors comment on that?
Second, the authors state that they “sidestep” the problem of non-generalizing property predictors in reinforcement learning, by “unifying graph generation and property estimation in one model”. How does the authors’ model not suffer from the same problem? Can they provide evidence that their model is better in property estimation than other models?
In the first benchmark (logP) the GCPN baseline is shown, but in the second benchmark table, the GCPN baseline is missing. Why? The GCPN baseline will need to be added there. Can the authors also comment on how they ensure the comparison to the GPCN and VSeq2Seq is fair? Also, can the authors comment on why they think the penalized logP task is a good benchmark?
Also, the authors write that Jin et al ICML 2018 (JTVAE) is a state of the model. However, also Liu et al NIPS 2018 (CGVAE) state that their model is state of the art. Unfortunately, both JTVAE and CGVAE were never compared against the strongest literature method so far, by Popova et al, which was evaluated on a much more challenging set of tasks than JT-VAE and CGVAE. The authors cite this paper but do not compare against it, which should to be rectified. This referee understands it is more compelling to invent new models, but currently, the literature of generative models for molecules is in a state of anarchy due to lack of solid comparison studies, which is not doing the community a great service.
Furthermore, the training details are not described in enough detail. 
How exactly are the pairs selected? Where do the properties for the molecules come from? Were they calculated using the logP, QED and DRD2 models? How many molecules are used in total in each of these tasks?