PACIA: Parameter-Efficient Adapter for Few-Shot Molecular Property Prediction

TL;DR

PACIA introduces a parameter-efficient GNN adapter with hierarchical adaptation for few-shot molecular property prediction, achieving state-of-the-art results while reducing overfitting risks associated with large adaptive parameter sets.

Contribution

The paper proposes PACIA, a novel GNN adapter that efficiently modulates message passing and employs hierarchical adaptation for improved few-shot MPP performance.

Findings

PACIA outperforms existing methods in few-shot MPP tasks.

Hierarchical adaptation enhances model flexibility and effectiveness.

PACIA reduces overfitting by using fewer adaptive parameters.

Abstract

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.