Graph Sampling-based Meta-Learning for Molecular Property Prediction
This work addresses the problem of limited data in molecular property prediction for researchers in drug discovery or materials science, offering a novel approach that is incremental by building on meta-learning with graph structures.
The paper tackles few-shot molecular property prediction by proposing a Graph Sampling-based Meta-learning (GS-Meta) framework that leverages many-to-many correlations between molecules and properties, achieving state-of-the-art performance with improvements of 5.71%-6.93% in ROC-AUC across five benchmarks.
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.