发表机构
Peking University; Wuhan University; Tibet University（北京大学; 武汉大学; 西藏大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对少样本分子性质预测中标签稀缺且依赖关系难以捕捉的问题，提出机会校正关系超图方法CalibHyper，基于联合标签分布校正偏差，在多个基准上达到与最强方法相当的ROC-AUC。
AI 中文摘要
分子性质预测是药物开发和材料发现的核心任务，但实验成本高昂且标记数据稀缺。上下文感知方法利用辅助测定标签来支持少样本预测，近期工作通过标签一致性来监督性质关系。然而，标签一致性对类别边际分布敏感，且不能直接捕获性质之间的依赖性。我们提出CalibHyper，一种基于联合标签分布的机会校正关系超图方法。CalibHyper从有序四态标签分布中减去独立性基线，并根据联合观测数量对残差进行收缩。一个交换等变关系头估计这些残差，这些残差为每个分子选择辅助性质并设置其超边消息的符号和权重。在来自五个基准的十三个数据集上，在1-shot和10-shot设置下，CalibHyper及其消融设置取得了与最强报告结果相当的ROC-AUC。
英文摘要
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, and recent work supervises property relations with label agreement. However, label agreement is sensitive to class marginals and does not directly capture dependence 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 shrinks the residual according to the number of joint observations. A swap-equivariant relation head estimates these residuals, which choose the auxiliary properties for each molecule and set the sign and weight of their hyperedge messages. On thirteen datasets from five benchmarks, in both 1-shot and 10-shot settings, CalibHyper and its ablation settings achieve ROC-AUC competitive with the strongest reported results.
