M-GLC: Motif-Driven Global-Local Context Graphs for Few-shot Molecular Property Prediction

TL;DR

This paper introduces M-GLC, a novel graph-based framework that leverages global motif information and local subgraph encoding to significantly improve few-shot molecular property prediction accuracy.

Contribution

It proposes a comprehensive motif-driven global-local context graph that enhances structural guidance in few-shot molecular property prediction tasks.

Findings

Outperforms state-of-the-art methods on five benchmarks.

Effectively captures long-range compositional patterns.

Enables knowledge transfer among molecules with shared motifs.

Abstract

Molecular property prediction (MPP) is a cornerstone of drug discovery and materials science, yet conventional deep learning approaches depend on large labeled datasets that are often unavailable. Few-shot Molecular property prediction (FSMPP) addresses this scarcity by incorporating relational inductive bias through a context graph that links molecule nodes to property nodes, but such molecule-property graphs offer limited structural guidance. We propose a comprehensive solution: Motif Driven Global-Local Context Graph for few-shot molecular property prediction, which enriches contextual information at both the global and local levels. At the global level, chemically meaningful motif nodes representing shared substructures, such as rings or functional groups, are introduced to form a global tri-partite heterogeneous graph, yielding motif-molecule-property connections that capture long-range compositional patterns and enable knowledge transfer among molecules with common motifs. At the local level, we build a subgraph for each node in the molecule-property pair and encode them separately to concentrate the model's attention on the most informative neighboring molecules and motifs. Experiments on five standard FSMPP benchmarks demonstrate that our framework consistently outperforms state-of-the-art methods. These results underscore the effectiveness of integrating global motif knowledge with fine-grained local context to advance robust few-shot molecular property prediction.