  Molecular property prediction plays a fundamental role in drug discovery to
identify candidate molecules with target properties. However, molecular
property prediction is essentially a few-shot problem which makes it hard to
use regular machine learning models. In this paper, we propose Property-Aware
Relation networks (PAR) to handle this problem. In comparison to existing
works, we leverage the fact that both relevant substructures and relationships
among molecules change across different molecular properties. We first
introduce a property-aware embedding function to transform the generic
molecular embeddings to substructure-aware space relevant to the target
property. Further, we design an adaptive relation graph learning module to
jointly estimate molecular relation graph and refine molecular embeddings
w.r.t. the target property, such that the limited labels can be effectively
propagated among similar molecules. We adopt a meta-learning strategy where the
parameters are selectively updated within tasks in order to model generic and
property-aware knowledge separately. Extensive experiments on benchmark
molecular property prediction datasets show that PAR consistently outperforms
existing methods and can obtain property-aware molecular embeddings and model
molecular relation graph properly.
