Experimental methodological developments in measuring protein-ligand interactions for small molecule (<900 Da) drug discovery have led to an influx of data. In some instances this data overload has been overwhelming and can complicate rather than inform decision-making during drug discovery. The focus of this thesis is thus to develop novel methods to contextualise and extract useful information that helps medicinal and computational chemists make sense of available data and improve the productivity of drug discovery. Specifically I have developed computational tools to structure and analyse protein-ligand interaction data. I generated 48 novel ligand-bound protein structures for the bromodomain of BAZ2B and the JmjC domain of KDM4 using experimental fragment soaking. This quantity of structures required time consuming and subjective analysis. A 3D interactive visualisation tool, WONKA, was therefore developed. WONKA displays interesting and unusual features (e.g. residue motions) within ensembles of protein-ligand structures and allows for sharing of observations between scientists. WONKA does not consider protein-ligand activity data, so I developed OOMMPPAA. OOMMPPAA is an interactive 3D visualisation tool that incorporates protein-ligand activity data with protein-ligand structural data using 3D matched molecular pairs. OOMMPPAA highlights nuanced structure activity relationships (SAR) and summarises available protein-ligand activity data in the protein context. WONKA and OOMMPPAA form a data model and platform to analyse structural and activity data. The extensibility and utility of this data model are demonstrated by the development of two further tools. The first, GLOOP, suggests ligand modifications from large datasets, and provides quantification of the importance of putatively important moieties. The second, LLOOMMPPAA, designs synthetically tractable molecules that explore a diverse range of protein-ligand interactions. LLOOMMPPAA has been shown experimentally to provide useful SAR. The tools described in this thesis provide novel analyses of and a framework for investigating protein-ligand interaction data.