CAPRICHO: Interpretable Quality Flagging and Flexible ChEMBL Bioactivity Curation for QSAR Modeling

Abstract

Preparing ChEMBL bioactivity data for quantitative structure–activity relationship (QSAR) modeling requires navigating curation decisions that often lack documentation and are hard to reproduce. We present CAPRICHO (ChEMBL Aggregation Package with Robust Inspection and Curation Handling Options), a Python command-line tool addressing two gaps in current workflows: transparency and flexibility. Unlike approaches that remove problematic data, CAPRICHO flags quality issues while preserving all data for inspection, letting users assess how each curation decision affects cross-assay comparability. Aggregation is configured via customizable grouping columns, supporting both traditional QSAR and emerging assay-aware modeling paradigms, with all parameters recorded in recipe files for reproducibility. Three case studies demonstrate the tool: (i) programmatic support of ChEMBL max-curation standards, (ii) CYP inhibition multitask curation, and (iii) Caco-2 permeability with unit standardization. Together, these show that preserving rather than removing flagged data lets practitioners identify which quality issues most affect comparability and balance data quality with availability for QSAR modeling. CAPRICHO is freely available at https://github.com/CDDLeiden/Capricho