Title: Look in The Mirror: Molecular Graph Contrastive Learning with Line Graph. Abstract: Trapped by the label scarcity in molecular property prediction and drug design, graph contrastive learning came forward. A general contrastive model consists of a view generator, view encoder, and contrastive loss, in which the view mainly controls the encoded information underlying input graphs. Leading contrastive learning works show two kinds of view generators, that is, random or learnable data corruption and domain knowledge incorporation. While effective, the two ways also lead to molecular semantics altering and limited generalization capability, respectively. Thus, a decent view that can fully retain molecular semantics and is free from profound domain knowledge is supposed to come forward. To this end, we relate molecular graph contrastive learning with the line graph and propose a novel method termed LGCL. Specifically, by contrasting the given graph with the corresponding line graph, the graph encoder can freely encode the molecular semantics without omission. While considering the information inconsistency and over-smoothing derived from the learning process because of the mismatched pace of message passing in two kinds of graphs, we present a new patch with edge attribute fusion and two local contrastive losses for performance fixing. Compared with state-of-the-art (SOTA) methods for view generation, superior performance on molecular property prediction suggests the effectiveness of line graphs severing as the contrasting views.