Genetic variants are currently annotated in the ChEMBL database by manually extracting this information from the original articles for data originating from the scientific literature. Since ChEMBL 22 this information has been mapped to protein targets (alongside their UniProt accessions) and made available in a structured format via the variant_sequences table. In this work, an orthogonal approach has been used to evaluate the fidelity and comprehensiveness of these annotations and to include as many variants as possible for the analysis of bioactivities against proteins carrying amino acid substitutions (Figure , steps 1-7). This approach is expert knowledge-agnostic and embodies an automatic pipeline based exclusively on data previously extracted from the database. Its first step consisted of the automatic extraction of amino acid substitution patterns from the assay descriptions of unique pairs of assays and protein targets, and their subsequent validation against the WT protein sequence (Figure , step 2). The extracted substitutions were then compared to the ChEMBL variant annotations in a feedback loop in which mismatches were semi-automatically classified and used to rescue or revert annotations (Figure , step 3). Finally, variant targets were annotated based on this feedback and mapped to ChEMBL bioactivity data. The final variantenhanced bioactivity dataset (VEBD) was constructed by keeping exclusively bioactivity data for proteins with at least one variant annotated and was lastly enriched with variant-annotated bioactivity data from the Christmann dataset. ) Extracted substitutions are compared to ChEMBL annotated variants, and the classification of mismatches is used to determine the final annotations. More details of this step are available in Supplementary Figure . (4) A variant target identifier is defined based on the final variant annotations. (5) The variant target identifier is mapped back to the ChEMBL bioactivity dataset. (6) Proteins with only WT data are filtered out. (7) The bioactivity dataset is standardized and curated similarly to, and enriched with variant data from the Papyrus dataset.