  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.
