ReCoG: Relational and Compact Context Graph Learning for Few-shot Molecular Property Prediction

Abstract

Few-shot molecular property prediction (FSMPP) is essential in drug discovery and materials design, where high-quality labeled data are often scarce and expensive to obtain. Despite the promising performance of existing methods, especially context-aware methods, they still face two-fold severe challenges with ${\rm \textit{insufficient structural context modeling}}$ & ${\rm \textit{redundant auxiliary context learning}}$, leading to inadequate context graph exploration and ineffective information utilization for effective molecule representation learning. To address these, in this paper, we propose a novel framework by learning on ${\rm \mathbf{\underline{Re}}}$ational and ${\rm \mathbf{\underline{C}}}$ompact c${\rm \mathbf{\underline{o}}}$ntext ${\rm \mathbf{\underline{G}}}$raph, named ReCoG, to comprehensively exploit the context graph for expressive molecular property prediction. Specifically, the proposed ReCoG contains two core modules: a **(1) cross-property relational learning module** to better model the structural and relational context information, and a **(2) context graph information bottleneck module** to adaptively suppress irrelevant auxiliary signals for compact context information utilization, followed by a detailed theoretical demonstration regarding the importance of joint relational and compact knowledge extraction in context graphs.