  Advances in deep learning models have revolutionized the study of biomolecule
systems and their mechanisms. Graph representation learning, in particular, is
important for accurately capturing the geometric information of biomolecules at
different levels. This paper presents a comprehensive review of the
methodologies used to represent biological molecules and systems as
computer-recognizable objects, such as sequences, graphs, and surfaces.
Moreover, it examines how geometric deep learning models, with an emphasis on
graph-based techniques, can analyze biomolecule data to enable drug discovery,
protein characterization, and biological system analysis. The study concludes
with an overview of the current state of the field, highlighting the challenges
that exist and the potential future research directions.
