How　to　produce　expressive　molecular　representations　is　a　fundamental　challenge　in　artificial　intelligence-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　poor　generalization　capability.　Here,　we　propose　a　novel　molecular　pre-training　graph-based　deep　learning　framework,　named　MPG,　that　learns　molecular　representations　from　large-scale　unlabeled　molecules.　In　MPG,　we　proposed　a　powerful　GNN　for　modelling　molecular　graph　named　MolGNet,　and　designed　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　chemical　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,　on　14　benchmark　datasets.　The　pre-trained　MolGNet　in　MPG　has　the　potential　to　become　an　advanced　molecular　encoder　in　the　drug　discovery　pipeline.