Hierarchical Adaptation with Hypernetworks for Few-shot Molecular
  Property Prediction
Shiguang Wu Yaqing Wang Quanming Yao
Molecular property prediction (MPP) is important in biomedical applications,
which naturally suffers from a lack of labels, thus forming a few-shot learning
problem. State-of-the-art approaches are usually based on gradient-based meta
learning strategy, which ignore difference in model parameter and molecule's
learning difficulty. To address above problems, we propose a novel hierarchical
adaptation mechanism for few-shot MPP (HiMPP). The model follows a
encoder-predictor framework. First, to make molecular representation
property-adaptive, we selectively adapt encoder's parameter by designing a
hypernetwork to modulate node embeddings during message propagation. Next, we
make molecule-level adaptation by design another hypernetwork, which assigns
larger propagating steps for harder molecules in predictor. In this way,
molecular representation is transformed by HiMPP hierarchically from
property-level to molecular level. Extensive results show that HiMPP obtains
the state-of-the-art performance in few-shot MPP problems, and our proposed
hierarchical adaptation mechanism is rational and effective.