Beyond bioactivity modelling with a focus on the WT protein, the dataset and results presented here can be exploited in variant bioactivity prediction with some precautions. First, variant data is still too sparse for large-scale modelling of new variants, as represented by the low performance of PCM models with LOVO validation. However, small-scale campaigns following data balancing strategies showed promising results and should be considered in light of each particular project's scope . Second, in this work only amino acid substitutions were considered, however, other aberrations such as deletions, insertions, amplifications, or copy number variations are known to be clinically relevant and affect both protein function and pharmacology . A protocol should therefore be devised to also map these variations in bioactivity databases accurately. Third, the biological context of the variants studiedactivating vs. resistance substitutions, as an exampleis correlated with the effect in bioactivity, and should be considered in database annotation and extrapolated to modelling.