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。

分层语法引导几何用于高效的分子属性预测

Hierarchical Grammar-Induced Geometry for Data-Efficient Molecular  Property Prediction

We present Graph Neural Diffusion (GRAND) that approaches deep learning on
graphs as a continuous diffusion process and treats Graph Neural Networks
(GNNs) as discretisations of an underlying PDE. In our model, the layer
structure and topology correspond to the discretisation choices of temporal and
spatial operators. Our approach allows a principled development of a broad new
class of GNNs that are able to address the common plights of graph learning
models such as depth, oversmoothing, and bottlenecks. Key to the success of our
models are stability with respect to perturbations in the data and this is
addressed for both implicit and explicit discretisation schemes. We develop
linear and nonlinear versions of GRAND, which achieve competitive results on
many standard graph benchmarks.

用连续扩散过程作为图上深度学习的方法，将 Graph Neural Networks (GNNs) 视为潜在 PDE 的离散化，从而得到了一种新的 GNN 模型，该模型采用时空算子离散化，解决了图学习模型中的深度、平滑过渡和瓶颈等问题。通过稳定性分析，可以得到线性和非线性版本的 GRAND 模型，这些模型能够在许多标准图标准测试中取得很好的效果。