Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To augment the compound design process we introduce Mol-CycleGAN -a CycleGAN-based model that generates optimized compounds with a chemical scaffold of interest. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results.
The paper presents an approach for optimizing molecular properties, based on the application of CycleGANs to variational autoencoders for molecules. A recently proposed domain-specific VAE called Junction Tree VAE (JT-VAE) is employed. The optimization of molecules is an important problem for example in drug discovery or materials design, and the development of machine learning approaches for it is therefore an important application.
On the positive side, this reviewer would like to highlight the structural transformation task, which is an interesting addition. The presentation is mostly clear, and there is an improvement in benchmark scores compared to the baseline JT-VAE.
On the negative side, this reviewer would argue the paper is a bit incremental, as it appears to be “just” a combination of two models without a clear motivation from a molecular design perspective – why is the model e.g. better (motivated) than the model by You et al, the MOLGAN by DeCao and Kipf, or the CGVAE by Liu et al? 
 Also, the number of benchmark experiments is small compared to more rigorous evaluation studies, e.g. by Olivecrona et al. https://doi.org/10.1186/s13321-017-0235-x 
Furthermore, a lot of (labeled) training examples for the respective properties seem to be needed, whereas in practical drug discovery, often only very small datasets are available.
Questions:
How exactly is the similarity constraint enforced?
Why do the authors use the JT-VAE and not the SD-VAE or CGVAE?
Can the authors explain why they think penalized logP is relevant to drug discovery? While the optimization of physicochemical properties alone is certainly important in a multi-objective setting, this reviewer is not entirely sure that physicochemical property optimization alone is a problem that often occurs in drug discovery.
Why are the values for the RL model by You et al. 2018 not shown in Table 2, even though the authors state that they have done the comparison? If those molecules are not drug-like, it would be good to see the comparison, and show the respective molecules. Keep in mind that you are not asking the models to produce drug-like molecules here! The RL model therefore seems to generate exactly what it is asked (rewarded) for. Is it a fair comparison that you expect it to do something else?
Does the requirement to define the sets X and Y restrict the capacity of the model to binary class membership, as in “good” and “bad”? Does this imply the model cannot extrapolate into e.g. continuous parameter ranges unseen during training? 
Provocative statement this reviewer would be keen to hear the authors’ (and co-reviewers) opinion on: It seems that there are considerable efforts needed in getting VAEs for discrete data such as graphs to work, namely the drastic restriction of the molecular graph space via grammars, CGVAE, or the JT setting (which builds on a heuristically defined vocabulary), whereas RL models even get decent results with simple SMILES-RNNs (see e.g. the work by Merk et al, who have even successfully made and tested their results in the lab!  https://doi.org/10.1038/s42004-018-0068-1 and https://doi.org/10.1002/minf.201700153 ) Do the author have an intuition into why this might be the case?
Regarding the proposed application of GANs to text generation, this reviewer would suggest to have a look at this recent (as in yesterday) paper https://arxiv.org/pdf/1811.02549.pdf
Overall, this reviewer believes the paper would be a good ICLR workshop paper or, after a bit more technical details on the models, a decent chemoinformatics paper, but unfortunately this reviewer is not convinced that in the current form the results are significant or general enough in terms of ML to warrant acceptance in the main ICLR track.