  Recently many efforts have been devoted to applying graph neural networks
(GNNs) to molecular property prediction which is a fundamental task for
computational drug and material discovery. One of major obstacles to hinder the
successful prediction of molecule property by GNNs is the scarcity of labeled
data. Though graph contrastive learning (GCL) methods have achieved
extraordinary performance with insufficient labeled data, most focused on
designing data augmentation schemes for general graphs. However, the
fundamental property of a molecule could be altered with the augmentation
method (like random perturbation) on molecular graphs. Whereas, the critical
geometric information of molecules remains rarely explored under the current
GNN and GCL architectures. To this end, we propose a novel graph contrastive
learning method utilizing the geometry of the molecule across 2D and 3D views,
which is named GeomGCL. Specifically, we first devise a dual-view geometric
message passing network (GeomMPNN) to adaptively leverage the rich information
of both 2D and 3D graphs of a molecule. The incorporation of geometric
properties at different levels can greatly facilitate the molecular
representation learning. Then a novel geometric graph contrastive scheme is
designed to make both geometric views collaboratively supervise each other to
improve the generalization ability of GeomMPNN. We evaluate GeomGCL on various
downstream property prediction tasks via a finetune process. Experimental
results on seven real-life molecular datasets demonstrate the effectiveness of
our proposed GeomGCL against state-of-the-art baselines.
