  How to produce expressive molecular representations is a fundamental
challenge in AI-driven drug discovery. Graph neural network (GNN) has emerged
as a powerful technique for modeling molecular data. However, previous
supervised approaches usually suffer from the scarcity of labeled data and have
poor generalization capability. Here, we proposed a novel Molecular
Pre-training Graph-based deep learning framework, named MPG, that leans
molecular representations from large-scale unlabeled molecules. In MPG, we
proposed a powerful MolGNet model and an effective self-supervised strategy for
pre-training the model at both the node and graph-level. After pre-training on
11 million unlabeled molecules, we revealed that MolGNet can capture valuable
chemistry insights to produce interpretable representation. The pre-trained
MolGNet can be fine-tuned with just one additional output layer to create
state-of-the-art models for a wide range of drug discovery tasks, including
molecular properties prediction, drug-drug interaction, and drug-target
interaction, involving 13 benchmark datasets. Our work demonstrates that MPG is
promising to become a novel approach in the drug discovery pipeline.
