Academic computational drug discovery has gained a massive boost with the growth of publicly available data . One of the areas where this has led to improvement is the prediction of bioactivity, specifically ligand-protein affinity. Databases such as ChEMBL and BindingDB provide a wealth of information and relationships between ligands, proteins, and their interaction . However, public data has a diverse quality range and is subject to experimental error . In contrast to large datasets like ChEMBL, there are also smaller more focused datasets available. These typically focus on a single protein family and usually from a single set of literature such as the Klaeger clinical kinase drugs dataset . Such collections contain a trove of high quality data, but are limited in their scope and are usually not viable as sole data sources in a more general study.