Venue: ICLR 2020 Conference

Reviews:

final_score: rating: 3: Weak Reject
review_content: The paper presents a model to classify pairs of graphs which is used to predict sided effects caused by drug-drug-interactions (DDI). 

The contribution of this work is to add attention connections between two graphs such that each node operation from one graph can attend on the nodes of the other graph. The paper shows good results in DDI prediction, although the performance gap with previous works (Zitnik et al., 2018) is modest.

In the related work they mention some works from Graph Neural Networks literature. But works from the benchmark experiments are not explained. I think they could also explain which are the similarities and differences of the proposed method vs these works they are comparing to.

Another way of improving the paper could be running more experiments beyond the QM9 dataset to corroborate the good performance of the algorithm.

In equation (2), a message that goes from node “j” to node “i” does not include node “i” as input into the edge operation. I think the GNN would be more powerful if both nodes "i" and "j" are input into the edge operation.

In summary, the main contribution of the paper is to add attention connections between two graphs. I do not feel it is innovative enough.

review_title: Official Blind Review #4

final_score: rating: 3: Weak Reject
review_content: In this paper, the authors proposed a method to extend graph-based learning with a co-attentional layer. Feeding graphs pairwisely into the model allows nodes to easily exchange information with nodes in other graphs as well as within the graph. This method outperforms other previous ones on a pairwise graph classification task (drug-drug interaction prediction).
This model is generalized from Neural Message Passing for Quantum Chemistry (Justin Gilmer et al.) and Graph Attention Networks (Petar Velickovic et al.), but most ideas are directly from the two previous papers. Combining the two methods do provide insights into understanding the interactions between graphs and get really good results on DDI prediction, but the novelty is limited.
Questions:
1 Are atoms encoded as only atom numbers, charges and connected hydrogen atoms? Because some atoms might have much larger atom numbers than others, e.g. carbon (6) and sulfur (16), will there be some scale problems? Will one-hot encoding of atom type help (like in Neural Message Passing for Quantum Chemistry)?
2 According to the paper, bond types will be encoded as e_{ij}. But in molecules, bond type is way more complex than only single/double/triple bonds, especially for drug molecules which are enriched for aromatic systems. For example, bonds in benzene or pyridine rings are between single and double (also not necessarily 3/2). Are there other possible methods to encode graph edges?
3 In result table 2 of Section 4 (quantum chemistry), I didn’t see a principle of choosing K value and choosing neighbors because different properties reaches the lowest MAE at different K values. This might cause some confusion in real application. Moreover, the authors should compare the performance with previous methods.

review_title: Official Blind Review #3

final_score: rating: 3: Weak Reject
review_content: This work injects a multi-head co-attention mechanism in GCN that allows one drug to attends to another drug during drug side effect prediction. The motivation is good with limited technical novelty. The paper is well-written and well organized.


For MHCADDI, it is performing binary classification for all side effect labels. It is different from Decagon’s setting, hence not comparable. Maybe also include Decagon-Binary?


Missing baseline: as its main innovation is using co-attention, it should compare with concatenated embedding generated from Graph Attention Network so that we know co-attention is better than independent attention on each drug (seems the authors have already attempted to do so but did not report it). Current baselines such as Decagon only use GCN with no attention mechanism. It could be also benefited by including VGAE. 
review_title: Official Blind Review #1

Meta-review: The paper proposes combining paired attention with co-attention. The reviewers have remarked that the paper is will written and that the experiments provide some new insights into this combination. Initially, some additional experiments were proposed, which were addressed by the authors in the rebuttal and the new version of the paper. However, ICLR is becoming a very competitive conference where novelty is an important criteria for acceptance, and unfortunately the paper was considered to lack the novelty to be presented at ICLR.
Decision: Reject