Relation-aware graph structure embedding with co-contrastive learning for drug-drug interaction prediction

TL;DR

This paper introduces RaGSECo, a novel relation-aware graph embedding method with co-contrastive learning for drug-drug interaction prediction, effectively handling new drugs and improving prediction accuracy.

Contribution

The paper proposes a new method combining relation-aware graph embeddings with co-contrastive learning to better predict DDIs, especially for new drugs.

Findings

RaGSECo outperforms existing methods on three tasks.

Effective in learning embeddings for new drugs.

Utilizes dual-graph and co-contrastive learning mechanisms.

Abstract

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.