ChemScreener: an active learning enabled hit discovery workflow with WDR5 inhibitor case study

Abstract Active deep learning offers a promising approach for hit discovery starting from limited data by iteratively updating and improving models during screening by applying new data and adapting decisions. Key open questions include how best to explore chemical space, how it compares to non-iter...

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Autors principals:	Lingling Shen, Jian Fang, Lulu Liu, Rena Wang, Jeremy L. Jenkins, He Wang
Format:	Artigo
Idioma:	Inglês
Publicat:	BMC 2026-04-01
Col·lecció:	Journal of Cheminformatics
Matèries:	Hit discovery Drug discovery Active learning WDR5 ChemScreener Uncertainty
Accés en línia:	https://doi.org/10.1186/s13321-026-01204-7
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