  Molecular representation learning has attracted much attention recently. A
molecule can be viewed as a 2D graph with nodes/atoms connected by edges/bonds,
and can also be represented by a 3D conformation with 3-dimensional coordinates
of all atoms. We note that most previous work handles 2D and 3D information
separately, while jointly leveraging these two sources may foster a more
informative representation. In this work, we explore this appealing idea and
propose a new representation learning method based on a unified 2D and 3D
pre-training. Atom coordinates and interatomic distances are encoded and then
fused with atomic representations through graph neural networks. The model is
pre-trained on three tasks: reconstruction of masked atoms and coordinates, 3D
conformation generation conditioned on 2D graph, and 2D graph generation
conditioned on 3D conformation. We evaluate our method on 11 downstream
molecular property prediction tasks: 7 with 2D information only and 4 with both
2D and 3D information. Our method achieves state-of-the-art results on 10
tasks, and the average improvement on 2D-only tasks is 8.3%. Our method also
achieves significant improvement on two 3D conformation generation tasks.
