Data curation is a fundamental yet often underappreciated aspect of cheminformatics and computational drug discovery. Large public and proprietary databases now provide vast amounts of chemical structure, physicochemical, absorption, distribution, metabolism, excretion, and toxicity (ADMET), and bioactivity data. However, these resources contain structural inconsistencies, annotation errors, and heterogeneous experimental conditions that can limit model performance and reproducibility. This narrative review summarizes why and how data should be curated before use in cheminformatics workflows. We frame chemical data curation around two complementary pillars: structural curation and curation of experimental conditions. On the structural side, we review existing standardization and quantitative structure–activity relationship (QSAR)-ready workflows, including handling of salts and mixtures, parent–child policies, aromatization, tautomer handling, stereochemistry validation, and duplicate detection with conflict resolution. On the experimental side, we synthesize evidence that assay protocols, measurement methods, and reporting practices introduce substantial uncertainty and bias in physicochemical and ADMET endpoints as well as bioactivity data, and we outline practical strategies for assembling condition-aware datasets from the literature and public databases. Across case studies, we highlight how curated structure–condition pairs yield more accurate, robust, and reproducible models than raw, unfiltered collections. Rather than introducing a new predictive method or performing a formal statistical meta-analysis, we provide a structured narrative synthesis of current best practices, tools, and decision points for data curation in cheminformatics. This review offers practical, evidence-based guidance on the structural and experimental-condition curation required to build reliable cheminformatics models. Scientific Contribution: This article does not introduce a new algorithm but provides a practice-oriented, structured synthesis of data curation in cheminformatics. We (i) formulate a two-pillar framework that treats structural curation and experimental-condition curation as equally important components of cheminformatics workflows; (ii) consolidate scattered best practices into concrete workflows, checklists, and decision maps for building “QSAR-ready” and condition-aware datasets; and (iii) integrate endpoint-specific case studies showing that rigorous curation materially improves predictive performance and reproducibility. We also identify open challenges and research directions for scaling and automating curation, including the use of workflow technologies and large language models, and for establishing community standards for condition metadata.
Esaki et al. (Mon,) studied this question.