Integrating artificial intelligence and manual curation to enhance bioassay annotations in ChEMBL.

ABSTRACT: As the volume and diversity of bioactivity data in ChEMBL continues to grow, ensuring that assay metadata is standardized, interoperable, and machine-readable is critical for effective use in cheminformatics and ML applications. In this work, we present recent efforts to enhance the quality and granularity of bioassay annotations in ChEMBL through a combination of manual and semi-manual curation and AI-driven approaches. We introduce a "perfect assay description" template to guide consistent annotation and demonstrate how natural language processing techniques and multi-class classification can be used to automatically extract key assay parameters and assign broad assay categories for legacy data. We report on the development, validation, and application of a spaCy-based NER model that ide

SUBMITTER: Smit I 

PROVIDER: S-EPMC12903245 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature