Every year an increasing number of articles showcase improvements in machine learning and artificial intelligence (AI/ML) bioactivity modelling in the form of novel model architectures or chemical and protein descriptors, among other innovations. Still, previous literature shows that one of the main bottlenecks in bioactivity prediction is the amount and quality of the available data for model training and testing . Several databases, such as ChEMBL and PubChem, aim to compile as much data as possible by extracting it from the literature or accepting deposited datasets, which on its own can introduce errors . Certain annotations like assay cell type, tissue, or genetic variants are not present in all articles or are described differently. In turn, this can result in inconsistencies in information content that affect the quality and comprehensiveness of the data .