sChemNET: a deep learning framework for predicting small molecules targeting microRNA function.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
23 Oct 2024
23 Oct 2024
Historique:
received:
18
08
2023
accepted:
14
06
2024
medline:
24
10
2024
pubmed:
24
10
2024
entrez:
23
10
2024
Statut:
epublish
Résumé
MicroRNAs (miRNAs) have been implicated in human disorders, from cancers to infectious diseases. Targeting miRNAs or their target genes with small molecules offers opportunities to modulate dysregulated cellular processes linked to diseases. Yet, predicting small molecules associated with miRNAs remains challenging due to the small size of small molecule-miRNA datasets. Herein, we develop a generalized deep learning framework, sChemNET, for predicting small molecules affecting miRNA bioactivity based on chemical structure and sequence information. sChemNET overcomes the limitation of sparse chemical information by an objective function that allows the neural network to learn chemical space from a large body of chemical structures yet unknown to affect miRNAs. We experimentally validated small molecules predicted to act on miR-451 or its targets and tested their role in erythrocyte maturation during zebrafish embryogenesis. We also tested small molecules targeting the miR-181 network and other miRNAs using in-vitro and in-vivo experiments. We demonstrate that our machine-learning framework can predict bioactive small molecules targeting miRNAs or their targets in humans and other mammalian organisms.
Identifiants
pubmed: 39443444
doi: 10.1038/s41467-024-49813-w
pii: 10.1038/s41467-024-49813-w
doi:
Substances chimiques
MicroRNAs
0
MIRN451 microRNA, human
0
Small Molecule Libraries
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
9149Informations de copyright
© 2024. The Author(s).
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