  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.
