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Molecular property is usually observed with a limited number of samples, andresearchers have considered property prediction as a few-shot problem. Oneimportant fact that has been ignored by prior works is that each molecule canbe recorded with several different properties simultaneously. To effectivelyutilize many-to-many correlations of molecules and properties, we propose aGraph Sampling-based Meta-learning (GS-Meta) framework for few-shot molecularproperty prediction. First, we construct a Molecule-Property relation Graph(MPG): molecule and properties are nodes, while property labels decide edges.Then, to utilize the topological information of MPG, we reformulate an episodein meta-learning as a subgraph of the MPG, containing a target property node,molecule nodes, and auxiliary property nodes. Third, as episodes in the form ofsubgraphs are no longer independent of each other, we propose to schedule thesubgraph sampling process with a contrastive loss function, which considers theconsistency and discrimination of subgraphs. Extensive experiments on 5commonly-used benchmarks show GS-Meta consistently outperforms state-of-the-artmethods by 5.71%-6.93% in ROC-AUC and verify the effectiveness of each proposedmodule. Our code is available at https://github.com/HICAI-ZJU/GS-Meta.

Graph Sampling-based Meta-Learning for Molecular Property Prediction