The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize coherent multi-resolution representations by interweaving the encoding of substructure components with the atom-level encoding of the original molecular graph. Moreover, our graph decoder is fully autoregressive, and interleaves each step of adding a new substructure with the process of resolving its attachment to the emerging molecule. We evaluate our model on multiple molecular optimization tasks and show that our model significantly outperforms previous state-of-the-art baselines.
The authors present a heirarchical graph-to-graph translation method for generating novel organic molecules.
Working from the model of Jin et al. (2019), the authors introduce a three step heirarchy - the model first determines where a substructure should be generated, what is the substructure, then the attachments to the existing molecule.
All steps of this uses embeddings generated from a message passing network - these embeddings are input into a few bilinear attention layers to obtain the heirarchical generation scheme.
The model is trained with molecular pairs (X, Y), and a VAE loss - a hidden z vector controls the way to modify X to improve its properties.
The encoder is just a MLP over the difference between sum of embeddings at a atom level and at the substructure level.
The model is evaluated on accuracy and diversity, in both conditional and unconditional settings.
The experiments show a small improvement over previous SOTA algorithms.
This is a borderline paper, and I'm leaning towards a weak reject, because I don't believe the model is well motivated enough:
- Sec 3.1 it's unclear how the substructures are generated - they provide a lot of inductive bias for the algorithm. 
  Are they automatically generated or built from a database of substructures?
- Variational decoding does not seem well motivated enough - would a stochastic decoding procedure not work as well as having a latent vector that essentially adds noise to the training?
- The experiments seem interesting and comprehensive - it seems that the model learns to exploit the biases and increase logP, as well as showing the ability to conditionally turn off DRD2-active properties of the molecules.
Some questions:
- Why not use a Transformer instead of an LSTM or GRU? The cell naturally acts over sets of neighbors and transformers are a natural model to tackle this problem.
- Sec 3.1 Topological Prediction, the attention is over c_{X}^{S} but the text claims it should be over c_{X}^{G}? Is ^G the attention substructure?
- Sec 3.1 Attachment Layer MPN: the A_i seem to be a tuple (S_i, {v_j}). The set of attaching atoms is limited to 2 right? It might be more clear to simply enumerate them here if so.
- Sec 3.1 Substructure Tree: Since tree decompositions are not unique, does this work use the different tree decompositions and DFS traversals as data augmentations?
- Table 2b: What is a "two-layer" and "one layer" encoder? Is it the size of the MLP or the removal of the attachment MPNs?
- Ablation study: Since the Attachment Layer has all the substructure information, this ablation should ideally make sure the models all have a similar number of parameters, and the decrease in performance isn't due to the decrease in parameters.
Nits:
- Sec 3.1 "bi-linear" should not have a dash, bilinear is one word.