Molecular property prediction plays a fundamental role in drug discovery to discover candidate molecules with target properties. However, molecular property prediction is essentially a few-shot problem which makes it hard to obtain regular models. In this paper, we propose a property-aware adaptive relation networks (PAR) for the few-shot molecular property prediction problem. In comparison to existing works, we leverage the facts that both substructures and relationships among molecules are different considering various molecular properties. Our PAR is compatible with existing graph-based molecular encoders, and are further equipped with the ability to obtain property-aware molecular embedding and model molecular relation graph adaptively. The resultant relation graph also facilitates effective label propagation within each task. Extensive experiments on benchmark molecular property prediction datasets show that our method consistently outperforms state-of-the-art methods and is able to obtain property-aware molecular embedding and model molecular relation graph properly.

翻译：分子属性预测在发现具有目标特性的候选分子的药物发现中起着根本作用。然而,分子属性预测基本上是一个小问题,因此很难获得常规模型。在本文中,我们提议为微粒分子属性预测问题建立一个有财产意识的适应关系网络(PAR),与现有的工程相比,我们利用以下事实,即考虑到不同的分子特性,分子之间的子结构和关系是不同的。我们的分子属性预测与现有的基于图形的分子编码器相容,并且进一步具备了获得有财产意识的分子嵌入和模型分子关系图的能力。由此产生的关系图还便利了每项任务中的有效标签传播。关于基准分子属性预测数据集的广泛实验表明,我们的方法始终优于最新工艺方法,并且能够恰当地获得有财产意识的分子嵌入和模型分子关系图。