  Predicting drug-drug interaction adverse events, so-called DDI events, is
increasingly valuable as it facilitates the study of mechanisms underlying drug
use or adverse reactions. Existing models often neglect the distinctive
characteristics of individual event classes when integrating multi-source
features, which contributes to systematic unfairness when dealing with highly
imbalanced event samples. Moreover, the limited capacity of these models to
abstract the unique attributes of each event subclass considerably hampers
their application in predicting rare drug-drug interaction events with a
limited sample size. Reducing dataset bias and abstracting event subclass
characteristics are two unresolved challenges. Recently, prompt tuning with
frozen pre-trained graph models, namely "pre-train, prompt, fine-tune"
strategy, has demonstrated impressive performance in few-shot tasks. Motivated
by this, we propose an advanced method as a solution to address these
aforementioned challenges. Specifically, our proposed approach entails a
hierarchical pre-training task that aims to capture crucial aspects of drug
molecular structure and intermolecular interactions while effectively
mitigating implicit dataset bias within the node embeddings. Furthermore, we
construct a prototypical graph by strategically sampling data from distinct
event types and design subgraph prompts utilizing pre-trained node features.
Through comprehensive benchmark experiments, we validate the efficacy of our
subgraph prompts in accurately representing event classes and achieve exemplary
results in both overall and subclass prediction tasks.
