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...

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Autores principales:	Ines Smit, Melissa F. Adasme, Emma Manners, Sybilla Corbett, Nicolas Bosc, Hoang-My-Anh Do, Andrew R. Leach, Noel M. O’Boyle, Barbara Zdrazil
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	BMC 2026-02-01
Colección:	Journal of Cheminformatics
Materias:	ChEMBL FAIR data Bioassay annotation Natural language processing Named entity recognition Machine learning
Acceso en línea:	https://doi.org/10.1186/s13321-026-01165-x
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