  Molecular representation learning (MRL) is a key step to build the connection
between machine learning and chemical science. In particular, it encodes
molecules as numerical vectors preserving the molecular structures and
features, on top of which the downstream tasks (e.g., property prediction) can
be performed. Recently, MRL has achieved considerable progress, especially in
methods based on deep molecular graph learning. In this survey, we
systematically review these graph-based molecular representation techniques,
especially the methods incorporating chemical domain knowledge. Specifically,
we first introduce the features of 2D and 3D molecular graphs. Then we
summarize and categorize MRL methods into three groups based on their input.
Furthermore, we discuss some typical chemical applications supported by MRL. To
facilitate studies in this fast-developing area, we also list the benchmarks
and commonly used datasets in the paper. Finally, we share our thoughts on
future research directions.
