A GENERATIVE MODEL FOR ELECTRON PATHS
Chemical reactions can be described as the stepwise redistribution of electrons in molecules. As such, reactions are often depicted using "arrow-pushing" diagrams which show this movement as a sequence of arrows. We propose an electron path prediction model (ELECTRO) to learn these sequences directly from raw reaction data. Instead of predicting product molecules directly from reactant molecules in one shot, learning a model of electron movement has the benefits of (a) being easy for chemists to interpret, (b) incorporating constraints of chemistry, such as balanced atom counts before and after the reaction, and (c) naturally encoding the sparsity of chemical reactions, which usually involve changes in only a small number of atoms in the reactants. We design a method to extract approximate reaction paths from any dataset of atom-mapped reaction SMILES strings. Our model achieves excellent performance on an important subset of the USPTO reaction dataset, comparing favorably to the strongest baselines. Furthermore, we show that our model recovers a basic knowledge of chemistry without being explicitly trained to do so.
The paper presents a novel end-to-end mechanistic generative model of electron flow in a particular type of chemical reaction (“Linear Electron Flow” reactions) . Interestingly, modeling the flow of electrons aids in the prediction of the final product of a chemical reaction over and above problems which attack this “product prediction problem” directly. The method is also shown to generalize well to held-out reactions (e.g. from a chemistry textbook).
General Impressions
+ For me the biggest selling point is that it improves performance in predicting the ultimate reaction outcome. It should do because it provides strictly more supervision, but it’s great that it actually does. 
+ Because it models the reaction mechanism the model is interpretable, and it’s possible to enforce constraints, e.g. that dynamics are physically possible.
+ Generalises outside of the dataset to textbook problems :-)
+ Well-founded modeling choices and neural network architectures.
- Only applies to a very particular type of reaction (heterolytic LEF). 
- Requires supervision on the level of electron paths. This seems to inhibit applying the model to more datasets or extending it to other types of reactions.
- Furthermore the supervision extraction does not seem take advantage of symmetries noted in the section(s) about difficulty evaluating inference. 
- It would be nice to margin out the electron flow model and just maximize the marginal likelihood for the product prediction problem.
Novelty
I’m not an expert on the literature of applying machine learning to the problems of reaction {product, mechanism} prediction but the paper appears to conduct a thorough review of the relevant methods and occupy new territory in terms of the modeling strategy while improving over SOTA performance.
Clarity
The writing/exposition is in general extremely clear. Nicely done. There are some suggestions/questions which I think if addressed would improve clarity.
Ways to improve the paper
1. Better motivate the use of machine learning on this problem. What are the limitations of the arrow-pushing models? 
2. Explain more about the Linear Electron Flow reactions, especially:
- Why does the work only consider “heterolytic” LEF reactions, what other types of LEF reactions are omitted?
- Is the main blocker to extending the model on the modeling front or the difficulties of extracting ground-truth targets? It appears to be the latter but this could be made more clear. Also that seems to be a pretty severe limitation to making the algorithm more general. Could you comment on this?
Questions
1. Is splitting up the electron movement model into bond “removal” and “addition” steps just a matter of parameterization or is that physically how the movements work? 
2. It appears that Jin et al reports Top 6/8/10 whereas this work reports Top 1/3/5 accuracy on the USPTO dataset. It would be nice if there was overlap :-). Do your Top 6/8/10 results with the WLDN model agree with the Jin et al paper?
Nits
Section 2.3, first paragraph “...(LEF) topology is by far the most important”: Could you briefly say why? It’s already noted that they’re the most common in the database. Why?
Section 3.ElectionMovement, first paragraph. “Observer that since LEF reactions are a single path of electrons…”. Actually, it’s not super clear what this means from the brief description of LEF. Can you explain these reactions in slightly more detail?
Section 3.ElectionMovement, second paragraph. “Differently, the above distribution can be split…”. Awkward phrasing. How about “In contrast, the above distribution can be split…”. 
Section 3.Training, last sentence “...minibatches of size one reaction”. Slightly awkward phrasing. Maybe “...minibatches consisting of a single reaction”?
Section 5.2, second sentence. “However, underestimates the model’s actual predictive accuracy…”. It looks like a word accidentally got deleted here or something.
Section 5.2, paragraph 4. “To evaluate if our model predicts the same major project”... Did you mean “the same major product”?