Still looking at the data-rich proteins, IDHC (O75874) showed poor extrapolation, which could be traced back to the very similar bioactivity profiles across the tested variants, all of them occurring in the clinically-relevant R132 residue (Supplementary Figures ). Based on this information, model performance could be improved by pooling all variant data or designing protein descriptors able to capture the subtle differences in one residue. These results stress the importance of informed decision-making via the analysis of bioactivity trends to design relevant training sets and strategies for variant-aware modelling. database structures and make informed decisions to select and curate data for the modelling task at hand. This does of course also reflect the fact that developing, running and processing the data from bioactivity assays is a complex scientific endeavour. Careful selection of several fields in these databases, such as activity comments and assay types can have a big impact on the quality of the modelling data. Here, the effect of a commonly overlooked field in bioactivity databases, amino acid substitutions constituting protein variants, was extensively analysed. The genetic variability landscape in the ChEMBL database has been explored in detail here for the first time, including the annotation strategy, the extent of variant data at different levels, the effect on bioactivity distributions, and finally the effect on bioactivity modelling. The dataset and results from this are made available to facilitate modelling with consideration of genetic variants. Moreover, a full analysis Python package is made available at to promote variant analysis in proteins of interest to the user and thus help make informed decisions about data selection and curation for modelling.