To learn accurate representations of molecules, it is essential to consider
both chemical and geometric features. To encode geometric information, many
descriptors have been proposed in constrained circumstances for specific types
of molecules and do not have the properties to be ``robust": 1. Invariant to
rotations and translations; 2. Injective when embedding molecular structures.
In this work, we propose a universal and robust Directional Node Pair (DNP)
descriptor based on the graph representations of 3D molecules. Our DNP
descriptor is robust compared to previous ones and can be applied to multiple
molecular types. To combine the DNP descriptor and chemical features in
molecules, we construct the Robust Molecular Graph Convolutional Network
(RoM-GCN) which is capable to take both node and edge features into
consideration when generating molecule representations. We evaluate our model
on protein and small molecule datasets. Our results validate the superiority of
the DNP descriptor in incorporating 3D geometric information of molecules.
RoM-GCN outperforms all compared baselines.

我们提出了一种基于图表示的全能且鲁棒的 Directional Node Pair (DNP) 描述符，该描述符能够结合分子的几何信息和化学特征，并构建了能够综合考虑节点和边特征的 Robust Molecular Graph Convolutional Network (RoM-GCN) 模型，在蛋白质和小分子数据集上验证了 DNP 描述符在融合分子的 3D 几何信息方面的优越性，RoM-GCN 模型优于所有对比基准模型。

使用图卷积网络学习通用且鲁棒的三维分子表示

Learning Universal and Robust 3D Molecular Representations with Graph  Convolutional Networks

Molecular graph representation learning is a fundamental problem in modern
drug and material discovery. Molecular graphs are typically modeled by their 2D
topological structures, but it has been recently discovered that 3D geometric
information plays a more vital role in predicting molecular functionalities.
However, the lack of 3D information in real-world scenarios has significantly
impeded the learning of geometric graph representation. To cope with this
challenge, we propose the Graph Multi-View Pre-training (GraphMVP) framework
where self-supervised learning (SSL) is performed by leveraging the
correspondence and consistency between 2D topological structures and 3D
geometric views. GraphMVP effectively learns a 2D molecular graph encoder that
is enhanced by richer and more discriminative 3D geometry. We further provide
theoretical insights to justify the effectiveness of GraphMVP. Finally,
comprehensive experiments show that GraphMVP can consistently outperform
existing graph SSL methods.

本研究提出了图多视角自编码（Graph Multi-View Pre-training）框架，使用自监督学习（self-supervised learning）通过 2D 拓扑和 3D 几何视图之间的对应和一致性提高了对分子图表示的学习能力，并通过实验证明其有效性高于现有的图自监督学习方法。