  Illuminating the interconnections between drugs and genes is an important
topic in drug development and precision medicine. Currently, computational
predictions of drug-gene interactions mainly focus on the binding interactions
without considering other relation types like agonist, antagonist, etc. In
addition, existing methods either heavily rely on high-quality domain features
or are intrinsically transductive, which limits the capacity of models to
generalize to drugs/genes that lack external information or are unseen during
the training process. To address these problems, we propose a novel
Communicative Subgraph representation learning for Multi-relational Inductive
drug-Gene interactions prediction (CoSMIG), where the predictions of drug-gene
relations are made through subgraph patterns, and thus are naturally inductive
for unseen drugs/genes without retraining or utilizing external domain
features. Moreover, the model strengthened the relations on the drug-gene graph
through a communicative message passing mechanism. To evaluate our method, we
compiled two new benchmark datasets from DrugBank and DGIdb. The comprehensive
experiments on the two datasets showed that our method outperformed
state-of-the-art baselines in the transductive scenarios and achieved superior
performance in the inductive ones. Further experimental analysis including
LINCS experimental validation and literature verification also demonstrated the
value of our model.
