Venue: ICLR 2017 workshop

Reviews:

final_score: rating: 5: Marginally below acceptance threshold
review_content: The paper proposes using auto-encoder models to learn molecular representations and generate novel molecules. The inputs to the autoencoders are the smiles strings representing the molecular graphs. The paper explores various autoencoder architectures with convolutional/RNN encoders and RNN decoders, and two variants of RNNs: sequence to sequence and character-level. The networks are trained using the denoising criterion, as opposed to using the variational free energies as in Gomez-Bombarelli et al (2016b).

The experiment involves sweeping over many hyperparameters and scoring the generated molecules based on the metric used in Gomez-Bombarelli et al (2016a). I found the conclusion from the experiment is misleading, or perhaps less promising than what the authors state:

i. as with other approaches in the literature, the generated molecules are *very often* invalid, and this work provides no attempt to address or quantify the scale of this problem. The trained model is probably not very useful if the selection operators have to reject most of the generated molecules, whether some of them might have higher scores. 

ii. it's not clear from the comparison what is the major factor contributing to more high-score molecules: whether it's the training objective, the RNN/CNN or just luck in hyperparameter tuning? Do you reckon the variational approach of Gomez-Bombarelli et al can achieve similar results if similar encoders/decoders are used?

Overall, this work provides an exploration of using RNN/CNN autoencoder architectures in modelling and generating molecules. The result is somewhat interesting but lacks justification or theoretical developments to be considered ground-/late-breaking. 
review_title: interesting experimental exploration, but limited contribution

final_score: rating: 6: Marginally above acceptance threshold
review_content: This paper presents an approach to generating novel small molecule structures (SMILES representations) with drug-like properties. Rather than generating random molecules using a set of hand-crafted rules (the space of possible small molecules is astronomical), the authors aim to learn common characteristics of drug-like molecules from a training set of known molecules. This is a reasonable approach to an important problem.

Previous work has proposed variational autoencoders for this application. Here, the authors explore other generative models. I think the proposed generative methods are a little messy, but not unreasonable.

Generated samples (molecules) are evaluated based on their drug-likeness. This is analogous to how realistic a generated image appears to a human observer, in that this objective is not explicitly optimized by the model. In experiments, the drug-likeness scores of the top candidates from the proposed approach are higher than those of the top candidates from the variational autoencoder. However, this improvement seems somewhat incremental; there is little justification for why this approach should be better, and surely some of the improvement is due to parameter tuning. 

Another important evaluation metric is the diversity of the samples that are produced --- after all, the goal is to propose new molecule structures. However, this is difficult to measure. The authors show that the drug-likeness distribution of their samples has higher variance than the baseline, but this again is likely a result of tuning parameters.

review_title: Good work, but somewhat incremental

Decision: Accept