Activity, assay and target data curation and quality in the ChEMBL database.

Area of Science:

• Bioinformatics
• Cheminformatics
• Drug Discovery Data Management

Background:

• Publicly available bioactivity databases like ChEMBL, PubChem BioAssay, and BindingDB highlight the importance of data curation.
• Ensuring data quality and integrity is crucial for the reliable use of these resources.

Purpose of the Study:

• To provide an overview of current and future data curation approaches for the ChEMBL database.
• To enhance data accessibility, comparability, integrity, and accuracy for users, especially modelers.
• To discuss issues impacting data curation and integrity and propose mitigation strategies.

Main Methods:

• Manual and automated steps are employed in the ChEMBL data curation process.
• The process focuses on maximizing data accessibility and comparability.
• Strategies are implemented to improve data integrity by identifying and flagging outliers, ambiguities, and potential errors.

Main Results:

• The curation process aims to increase the usefulness and accuracy of ChEMBL data through curated annotations and mappings.
• Discussion includes potential impacts of data curation issues on users.
• Robust selection and filter strategies are presented to manage data quality based on application needs.

Conclusions:

• Effective data curation is essential for maximizing the value of bioactivity databases like ChEMBL.
• Addressing data integrity issues through robust curation and filtering enhances the reliability of scientific data.
• Future curation efforts will continue to improve the utility of ChEMBL for drug discovery and modeling.