The prediction of molecular properties is a crucial task in the field of
material and drug discovery. The potential benefits of using deep learning
techniques are reflected in the wealth of recent literature. Still, these
techniques are faced with a common challenge in practice: Labeled data are
limited by the cost of manual extraction from literature and laborious
experimentation. In this work, we propose a data-efficient property predictor
by utilizing a learnable hierarchical molecular grammar that can generate
molecules from grammar production rules. Such a grammar induces an explicit
geometry of the space of molecular graphs, which provides an informative prior
on molecular structural similarity. The property prediction is performed using
graph neural diffusion over the grammar-induced geometry. On both small and
large datasets, our evaluation shows that this approach outperforms a wide
spectrum of baselines, including supervised and pre-trained graph neural
networks. We include a detailed ablation study and further analysis of our
solution, showing its effectiveness in cases with extremely limited data. Code
is available at this https URL

通过使用可学习的分层分子语法，我们提出了一种数据高效的性质预测方法，可以从语法生成规则中生成分子，并利用次级空间的几何信息进行性质预测，从而在有限数据情况下优于其他方法。