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
this https URL

该论文提出了一种新颖的元学习 FSMPP 框架（KRGTS），其中包括对分子和属性之间的多对多关系进行建模的知识增强关系图模块，以及用于进行元知识学习和减少噪声引入的任务采样模块。经过大量实验证明，KRGTS 相比其他最先进的方法具有更强的优势。