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

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 textitinsufficient structural context modeling & textitredundant 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 textbfunderlineRelational and textbfunderlineCompact ctextbfunderlineontext textbfunderlineGraph, named method, to comprehensively exploit the context graph for expressive molecular property prediction. Specifically, the proposed method 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.