Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 3: Weak Reject
review_content: This paper presents a graph neural network for drug-to-drug interaction (DDI) prediction, which explicitly models link type correlation. Basically, the drug-to-drug interaction prediction problem is a specific type of link prediction task, with drugs as vertices and interaction as edges, and the authors propose a graph neural network with an energy-based formulation where the link types are encoded as the graph edges. The authors validate their method against feedforward GNNs on two DDI prediction datasets, and achieve significantly improved performances.
 
Pros
- The drug-to-drug interaction prediction is a relatively less explored application of deep learning with growing interests.
- The proposed energy-based formulation that considers link type correlation intuitively makes sense and performs significantly better than some of the existing GNN approaches. 
 
Cons
- Using energy-based formulation for graph neural network is not novel, and thus the paper lacks novelty in methodology point of view. Thus this paper seems to fit better for a pharmaceutical journal or a journal rather than a top-tier deep learning conference such as ICLR. I do not think the paper will be of interest to a large audience.
 
- The experimental validation is limited. The authors only compare against GNN and GLENN, without even mentioning other relevant baselines such as GCN and GAT. Thus I suggest the authors to perform a more extensive comparative study.
 
- The qualitative analysis is insufficient, even as a domain-specific empirical study paper. It would be better if the authors included actual examples of drug-drug interaction predicted by the proposed model and an existing model, for further analysis and interpretation.
 
In sum, while I believe that DDI prediction is an interesting application of deep learning with growing interests, I do not believe the paper has sufficient novelty or experimental validation / qualitative examples to be a meaningful contribution to ICLR. I suggest the authors to work on the experimental validation and qualitative analysis part and submit it to a workshop or a pharmaceutical journal instead.  

review_title: Official Blind Review #1

final_score: rating: 3: Weak Reject
review_content: This paper proposes a framework to learn correlated drug-drug interaction based on structured prediction energy networks (SPEN). The core idea is to model the dependency structure of the labels (multi-label) by minimizing a designed energy function. The graph energy is designed as MLP over the mean of all nodes embeddings, where the nodes embeddings are obtained through a graph convolutional network. The edge information is included in the node embedding when aggregating neighborhood information. The proposed method also introduces an additional test inference network to jointly train with the cost-augmented training network under the semi-supervised setting. The authors tested on two DDI datasets and the result shows improvement compared to several baseline methods.

Strengths
- motivation: the authors consider the correlations between DDI labels (multi-label) which could potentially improve prediction of DDI. The proposed method uses structure prediction energy networks to model such dependency.

- method: the proposed method introduce an additional test inference network to fit the energy minimization framework into a semi-supervised setting.

Weakness
- The core part of the proposed method which differs from previous works needs to be elaborated more.
- The performance improvement seems to be marginal, especially on the second dataset.

Detailed comments
- The work is based on structured prediction energy networks (SPEN, Belanger, et al. 2016) and its follow-up works (eg., Belanger et al. 2016, Lifu Tu et al. 2018), the overall framework is very similar with previous work, where the feature extraction part is replaced by a graph convolutional network module. The core difference lies in the additional testing inference network which is jointly trained for adapting supervised SPEN into a semi-supervised setting. This part actually differs from all previous works and needs to be elaborated more. Why does such design make sense and what benefit can be brought through such design? The formulation for semi-supervised SPEN could be defined more clearly and worth elaboration.

- The experiments are run on 3 different random splits, based on the mean(std) of the evaluation metric, the performance of the proposed method does not vary much compared to baselines, especially on the second dataset, eg., GNN 0.25 +/- 0.02 compared with GENN 0.26. Also, GLENN < GNN seems to imply including energy is not the most important part for helping the task, but rather the semi-supervised joint training truly improves the performance. 
review_title: Official Blind Review #3

final_score: rating: 6: Weak Accept
review_content: Abstract:
Understanding drug-drug interactions (DDI) is an important task in drug development and prescription management. The authors proposed a new graph energy neural network (GENN) for DDI prediction. Comparing to the existing Decagon model (Zitnik et al. 2018), the proposed new model considered correlations between DDI types and used a new energy function to capture this information. By comparing to the previous baselines, the authors demonstrated their approach was able to achieve the SOAT performance in terms of prediction accuracy, be more robust to missing DDI data, and better capture the correlations between DDI types.

Major comments:
The authors succeed in combining graph neural networks and structure prediction to capture correlations of DDI types between drug-drug pairs.   They used an energy function based on graph neural networks to capture the dependencies among edge types in DDI graphs.  

However, the description of training cost-augmented inference network and test inference network is convoluted. The authors didn't elaborate on why trained cost-augmented inference network cannot be directly used for test inference, and why the prediction for unknown DDI type is different in test inference.  In addition, the notations used in the last sentence of the second paragraph of section 4.3 seem to be inconsistent with Eq. (4)

The authors compared their model (GENN) with multiple baselines and demonstrated superior performance only in PR-AUC. In the description for Table 1, the authors claimed: "GENN largely outperforms others with respect to all metrics in both datasets."  However, this conclusion can not be supported by Table 1, in which GENN only shows trivial improvement comparing to GNN for the most reported metrics.

Last but not least, the authors did not provide any limitations of their work. 

Minor comments:
In general, the paper is well written and easy to follow, except for sections 4.2 and 4.3.  As mentioned above, the notations used in the last sentence of the second paragraph of section 4.3 seem to be inconsistent with Eq. (4)
review_title: Official Blind Review #2

Meta-review: This paper studies the use of a graph neural network for drug-to-drug interaction (DDI) prediction task (an instance of a link prediction task with drugs as vertices and interaction as edges). In particular, the authors apply structured prediction energy networks (SPEN) and model the dependency structure of the labels by minimising an energy function. The authors empirically validate the proposed approach against feedforward GNNs on two DDI prediction tasks. The reviewers feel that understanding drug-drug interactions is an important task and that the work is well motivated. However, the reviewers argued that the proposed methodology is not novel enough to merit publication at ICLR and that some conclusions are not supported by the empirical analysis. For the former, the benefits of the semi-supervised design need to be clearly and concisely presented. For the latter, providing a more convincing practical benefit would greatly improve the manuscript. As such, I will recommend the rejection of this paper at the current state.
Decision: Reject