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Molecular graph representation learning is a fundamental problem in moderndrug and material discovery. Molecular graphs are typically modeled by their 2Dtopological structures, but it has been recently discovered that 3D geometricinformation plays a more vital role in predicting molecular functionalities.However, the lack of 3D information in real-world scenarios has significantlyimpeded the learning of geometric graph representation. To cope with thischallenge, we propose the Graph Multi-View Pre-training (GraphMVP) frameworkwhere self-supervised learning (SSL) is performed by leveraging thecorrespondence and consistency between 2D topological structures and 3Dgeometric views. GraphMVP effectively learns a 2D molecular graph encoder thatis enhanced by richer and more discriminative 3D geometry. We further providetheoretical insights to justify the effectiveness of GraphMVP. Finally,comprehensive experiments show that GraphMVP can consistently outperformexisting graph SSL methods.

Pre-training Molecular Graph Representation with 3D Geometry