  Molecular property prediction (MPP) plays a crucial role in biomedical
applications, but it often encounters challenges due to a scarcity of labeled
data. Existing works commonly adopt gradient-based strategy to update a large
amount of parameters for task-level adaptation. However, the increase of
adaptive parameters can lead to overfitting and poor performance. Observing
that graph neural network (GNN) performs well as both encoder and predictor, we
propose PACIA, a parameter-efficient GNN adapter for few-shot MPP. We design a
unified adapter to generate a few adaptive parameters to modulate the message
passing process of GNN. We then adopt a hierarchical adaptation mechanism to
adapt the encoder at task-level and the predictor at query-level by the unified
GNN adapter. Extensive results show that PACIA obtains the state-of-the-art
performance in few-shot MPP problems, and our proposed hierarchical adaptation
mechanism is rational and effective.
