  Designing accurate deep learning models for molecular property prediction
plays an increasingly essential role in drug and material discovery. Recently,
due to the scarcity of labeled molecules, self-supervised learning methods for
learning generalizable and transferable representations of molecular graphs
have attracted lots of attention. In this paper, we argue that there exist two
major issues hindering current self-supervised learning methods from obtaining
desired performance on molecular property prediction, that is, the ill-defined
pre-training tasks and the limited model capacity. To this end, we introduce
Knowledge-guided Pre-training of Graph Transformer (KPGT), a novel
self-supervised learning framework for molecular graph representation learning,
to alleviate the aforementioned issues and improve the performance on the
downstream molecular property prediction tasks. More specifically, we first
introduce a high-capacity model, named Line Graph Transformer (LiGhT), which
emphasizes the importance of chemical bonds and is mainly designed to model the
structural information of molecular graphs. Then, a knowledge-guided
pre-training strategy is proposed to exploit the additional knowledge of
molecules to guide the model to capture the abundant structural and semantic
information from large-scale unlabeled molecular graphs. Extensive
computational tests demonstrated that KPGT can offer superior performance over
current state-of-the-art methods on several molecular property prediction
tasks.
