  Although substantial efforts have been made using graph neural networks
(GNNs) for AI-driven drug discovery (AIDD), effective molecular representation
learning remains an open challenge, especially in the case of insufficient
labeled molecules. Recent studies suggest that big GNN models pre-trained by
self-supervised learning on unlabeled datasets enable better transfer
performance in downstream molecular property prediction tasks. However, the
approaches in these studies require multiple complex self-supervised tasks and
large-scale datasets, which are time-consuming, computationally expensive, and
difficult to pre-train end-to-end. Here, we design a simple yet effective
self-supervised strategy to simultaneously learn local and global information
about molecules, and further propose a novel bi-branch masked graph transformer
autoencoder (BatmanNet) to learn molecular representations. BatmanNet features
two tailored complementary and asymmetric graph autoencoders to reconstruct the
missing nodes and edges, respectively, from a masked molecular graph. With this
design, BatmanNet can effectively capture the underlying structure and semantic
information of molecules, thus improving the performance of molecular
representation. BatmanNet achieves state-of-the-art results for multiple drug
discovery tasks, including molecular properties prediction, drug-drug
interaction, and drug-target interaction, on 13 benchmark datasets,
demonstrating its great potential and superiority in molecular representation
learning.
