Hierarchical Adaptation with Hypernetworks for Few-shot Molecular Property Prediction

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.