  Latent representations of drugs and their targets produced by contemporary
graph autoencoder-based models have proved useful in predicting many types of
node-pair interactions on large networks, including drug-drug, drug-target, and
target-target interactions. However, most existing approaches model the node's
latent spaces in which node distributions are rigid and disjoint; these
limitations hinder the methods from generating new links among pairs of nodes.
In this paper, we present the effectiveness of variational graph autoencoders
(VGAE) in modeling latent node representations on multimodal networks. Our
approach can produce flexible latent spaces for each node type of the
multimodal graph; the embeddings are used later for predicting links among node
pairs under different edge types. To further enhance the models' performance,
we suggest a new method that concatenates Morgan fingerprints, which capture
the molecular structures of each drug, with their latent embeddings before
preceding them to the decoding stage for link prediction. Our proposed model
shows competitive results on two multimodal networks: (1) a multi-graph
consisting of drug and protein nodes, and (2) a multi-graph consisting of drug
and cell line nodes. Our source code is publicly available at
https://github.com/HySonLab/drug-interactions.
