Graph Sampling-based Meta-Learning for Molecular Property Prediction
Xiang Zhuang Qiang Zhang Bin Wu Keyan Ding Yin Fang Huajun Chen
Molecular property is usually observed with a limited number of samples, and
researchers have considered property prediction as a few-shot problem. One
important fact that has been ignored by prior works is that each molecule can
be recorded with several different properties simultaneously. To effectively
utilize many-to-many correlations of molecules and properties, we propose a
Graph Sampling-based Meta-learning (GS-Meta) framework for few-shot molecular
property 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 episode
in 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 of
subgraphs are no longer independent of each other, we propose to schedule the
subgraph sampling process with a contrastive loss function, which considers the
consistency and discrimination of subgraphs. Extensive experiments on 5
commonly-used benchmarks show GS-Meta consistently outperforms state-of-the-art
methods by 5.71%-6.93% in ROC-AUC and verify the effectiveness of each proposed
module. Our code is available at https://github.com/HICAI-ZJU/GS-Meta.