Abstract: The
abundance of macromolecular structure data, driven by advances in
experimental and structure prediction methods, has transformed life
science research. With more than 230,000 experimentally determined
structures in the Protein Data Bank (PDB) and millions of predicted
models from resources such as the AlphaFold database, researchers have
unparalleled access to structural insights. However, this wealth of data
presents significant challenges in validation, integration,
interpretation, and usability.
In
this presentation, I will discuss key challenges such as quality
assessment of predicted models, functional validation, and the need for
advanced computational tools. I will also explore emerging
opportunities, including the role of innovative tools, knowledge graphs
and large-scale annotations to maximise the impact of structural data
across drug discovery, molecular biology, and biotechnology in
transforming basic and translational research in life sciences.