  Relation-aware graph structure embedding is promising for predicting
multi-relational drug-drug interactions (DDIs). Typically, most existing
methods begin by constructing a multi-relational DDI graph and then learning
relation-aware graph structure embeddings (RaGSEs) of drugs from the DDI graph.
Nevertheless, most existing approaches are usually limited in learning RaGSEs
of new drugs, leading to serious over-fitting when the test DDIs involve such
drugs. To alleviate this issue, we propose a novel DDI prediction method based
on relation-aware graph structure embedding with co-contrastive learning,
RaGSECo. The proposed RaGSECo constructs two heterogeneous drug graphs: a
multi-relational DDI graph and a multi-attribute drug-drug similarity (DDS)
graph. The two graphs are used respectively for learning and propagating the
RaGSEs of drugs, aiming to ensure all drugs, including new ones, can possess
effective RaGSEs. Additionally, we present a novel co-contrastive learning
module to learn drug-pairs (DPs) representations. This mechanism learns DP
representations from two distinct views (interaction and similarity views) and
encourages these views to supervise each other collaboratively to obtain more
discriminative DP representations. We evaluate the effectiveness of our RaGSECo
on three different tasks using two real datasets. The experimental results
demonstrate that RaGSECo outperforms existing state-of-the-art prediction
methods.
