Graph Representation Learning for Interactive Biomolecule Systems
It synthesizes existing methods for a domain-specific audience in computational biology, but is incremental as a review paper.
This paper provides a comprehensive review of graph representation learning methodologies for representing biological molecules and systems as computer-recognizable objects, examining how geometric deep learning models can analyze biomolecule data for applications like drug discovery and protein characterization.
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.