Recently, few-shot molecular property prediction (FSMPP) has garnered increasing attention. Despite impressive breakthroughs achieved by existing methods, they often overlook the inherent many-to-many relationships between molecules and properties, which limits their performance. For instance, similar substructures of molecules can inspire the exploration of new compounds. Additionally, the relationships between properties can be quantified, with high-related properties providing more information in exploring the target property than those low-related. To this end, this paper proposes a novel meta-learning FSMPP framework (KRGTS), which comprises the Knowledge-enhanced Relation Graph module and the Task Sampling module. The knowledge-enhanced relation graph module constructs the molecule-property multi-relation graph (MPMRG) to capture the many-to-many relationships between molecules and properties. The task sampling module includes a meta-training task sampler and an auxiliary task sampler, responsible for scheduling the meta-training process and sampling high-related auxiliary tasks, respectively, thereby achieving efficient meta-knowledge learning and reducing noise introduction. Empirically, extensive experiments on five datasets demonstrate the superiority of KRGTS over a variety of state-of-the-art methods. The code is available in https://github.com/Vencent-Won/KRGTS-public.

翻译：近年来，少样本分子性质预测（FSMPP）受到越来越多的关注。尽管现有方法取得了令人瞩目的突破，但它们往往忽略了分子与性质之间固有的多对多关系，这限制了其性能。例如，分子的相似子结构可以启发新化合物的探索。此外，性质之间的关系可以被量化，高相关性质在探索目标性质时比低相关性质提供更多信息。为此，本文提出了一种新颖的元学习FSMPP框架（KRGTS），该框架包含知识增强关系图模块和任务采样模块。知识增强关系图模块构建了分子-性质多关系图（MPMRG），以捕捉分子与性质之间的多对多关系。任务采样模块包括元训练任务采样器和辅助任务采样器，分别负责调度元训练过程以及采样高相关辅助任务，从而实现高效的元知识学习并减少噪声引入。实验方面，在五个数据集上的大量实验证明了KRGTS相较于多种先进方法的优越性。代码可在 https://github.com/Vencent-Won/KRGTS-public 获取。