CalibHyper: Chance-Corrected Relational Hypergraphs for Few-Shot Molecular Property Prediction

Molecular property prediction is central to drug development and materials discovery, but experiments are costly and labeled data are scarce. Context-aware methods use auxiliary assay labels to support few-shot prediction. However, label agreement is sensitive to class marginals and does not directly capture dependencies between properties. We propose CalibHyper, a chance-corrected relational hypergraph method based on the joint label distribution. CalibHyper subtracts an independence baseline from the ordered four-state label distribution and applies shrinkage based on the number of joint observations. It estimates residuals through a swap-equivariant relation head, then constructs signed hyperedges conditioned on each molecule, allowing calibrated relation signals to directly inform prediction. Experiments on multiple molecular property prediction datasets show that CalibHyper and its variants achieve competitive predictive performance.