PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction
This work addresses data scarcity in biomedical applications by improving few-shot learning efficiency, though it is incremental as it builds on existing GNN methods.
The paper tackles the problem of few-shot molecular property prediction by proposing PACIA, a parameter-efficient GNN adapter that reduces overfitting and achieves state-of-the-art performance in this domain.
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.