  Molecular machine learning bears promise for efficient molecule property
prediction and drug discovery. However, due to the limited labeled data and the
giant chemical space, machine learning models trained via supervised learning
perform poorly in generalization. This greatly limits the applications of
machine learning methods for molecular design and discovery. In this work, we
present MolCLR: Molecular Contrastive Learning of Representations via Graph
Neural Networks (GNNs), a self-supervised learning framework for large
unlabeled molecule datasets. Specifically, we first build a molecular graph,
where each node represents an atom and each edge represents a chemical bond. A
GNN is then used to encode the molecule graph. We propose three novel molecule
graph augmentations: atom masking, bond deletion, and subgraph removal. A
contrastive estimator is utilized to maximize the agreement of different graph
augmentations from the same molecule. Experiments show that molecule
representations learned by MolCLR can be transferred to multiple downstream
molecular property prediction tasks. Our method thus achieves state-of-the-art
performance on many challenging datasets. We also prove the efficiency of our
proposed molecule graph augmentations on supervised molecular classification
tasks.
