Graph Data Modeling: Molecules, Proteins, & Chemical Processes
It provides foundational knowledge for applying graph methods to chemical sciences, but it is incremental as it serves as an introductory primer rather than presenting new research.
This primer introduces graphs as mathematical objects in chemistry and shows how learning algorithms, particularly graph neural networks, can operate on them to tackle problems in chemical discovery, such as describing molecules, proteins, and reactions.
Graphs are central to the chemical sciences, providing a natural language to describe molecules, proteins, reactions, and industrial processes. They capture interactions and structures that underpin materials, biology, and medicine. This primer, Graph Data Modeling: Molecules, Proteins, & Chemical Processes, introduces graphs as mathematical objects in chemistry and shows how learning algorithms (particularly graph neural networks) can operate on them. We outline the foundations of graph design, key prediction tasks, representative examples across chemical sciences, and the role of machine learning in graph-based modeling. Together, these concepts prepare readers to apply graph methods to the next generation of chemical discovery.