Cluster-Aware Few-Shot Molecular Property Prediction With Factor Disentanglement

Molecular property prediction plays a crucial role in drug discovery, but is always challenged by the limited number of effective labels. Compared with existing methods, we argue that the auxiliary properties of the molecule and the heterogeneous structure of different property prediction tasks have always been ignored. In this article, we propose a novel framework termed Meta-DREAM for few-shot molecular property prediction, which tailors to learning the transferable knowledge within different clusters of tasks. Specifically, we first construct a heterogeneous molecule relation graph (HMRG) with molecule-property and molecule-molecule relations to utilize many-to-many correlations between properties and molecules. The meta-learning episode can, then, be reformulated as a subgraph of HMRG. Next, we propose a disentangled graph encoder to explicitly discriminate the underlying factors of the task. In addition, we introduce a soft clustering module to group each factorized task representation into appropriate clusters and preserve knowledge generalization within a cluster and customization among clusters. In this way, each disentangled factor serves as a cluster-aware parameter gate for the task-specific meta-learner. Extensive experiments on five commonly used molecular datasets show that Meta-DREAM consistently outperforms existing state-of-the-art methods and verifies the effectiveness of each module.