Paper:
With massive possible synthetic routes in chemistry, retrosynthesis prediction is still a challenge for researchers. Recently, retrosynthesis prediction is formulated as a Machine Translation (MT) task. Namely, since each molecule can be represented as a Simplified Molecular-Input Line-Entry System (SMILES) string, the process of synthesis is analogized to a process of language translation from reactants to products. However, the MT models that applied on SMILES data usually ignore the information of natural atomic connections and the topology of molecules. In this paper, we propose a Graph Enhanced Transformer (GET) framework, which adopts both the sequential and graphical information of molecules. Four different GET designs are proposed, which fuse the SMILES representations with atom embedding learned from our improved Graph Neural Network (GNN). Empirical results show that our model significantly outperforms the Transformer model in test accuracy.

Review:
This paper focuses on the retrosynthesis prediction problem which to my understanding is the factorization of a target molecule into simpler structures. Previously, retrosynthesis prediction has been tackled as a language translation problem by using a Seq2Seq algorithm called "Transformer". This sequential approach was possible because molecules can be expressed as strings using the following format: Simplified Molecular-Input Line-Entry System (SMILES).
Despite its good performance, language translation methods ignore the graphical structure of molecules. This paper proposes to add a graph neural network in front of the Seq2Seq/Transformer network to exploit the graphical structure, hence the name “Graph Enhanced Transformer (GET)”. The Graph Neural Network considered in this work also uses an attention mechanism similarly to Graph Attention Networks.
The main contribution is the addition of a Graph Neural Network to a Seq2Seq model for retrosynthesis prediction which provides state of the art results.
From a machine learning / representation learning perspective, I do not consider the paper is innovative enough. Even so, the paper shows strong results for retrosynthesis prediction, I guess it would be a good fit in a chemistry conference.
Things to improve:
There are plenty of works related to graph Autoencoders and Graph generative models that are not mentioned in the related work. It would be great to dedicate a section for them. Here some examples: 
- Li, Y., Vinyals, O., Dyer, C., Pascanu, R., & Battaglia, P. (2018). Learning deep generative models of graphs.
-Simonovsky, M., & Komodakis, N. (2018, October). Graphvae: Towards generation of small graphs using variational autoencoders.
-De Cao, N., & Kipf, T. (2018). MolGAN: An implicit generative model for small molecular graphs.
-You, J., Ying, R., Ren, X., Hamilton, W. L., & Leskovec, J. (2018). Graphrnn: Generating realistic graphs with deep auto-regressive models.
All experiments are compared to author’s re-implementations of other works. It would be interesting to directly report other work accuracies.