sChemNET: a deep learning framework for predicting small molecules targeting microRNA function

Diego Galeano (), Imrat, Jeffrey Haltom, Chaylen Andolino, Aliza Yousey, Victoria Zaksas, Saswati Das, Stephen B. Baylin, Douglas C. Wallace, Frank J. Slack, Francisco J. Enguita, Eve Syrkin Wurtele, Dorothy Teegarden, Robert Meller, Daniel Cifuentes and Afshin Beheshti
Additional contact information
Diego Galeano: Universidad Nacional de Asunción - FIUNA
Imrat: Boston University Chobanian & Avedisian School of Medicine
Jeffrey Haltom: COVID-19 International Research Team
Chaylen Andolino: Purdue University
Aliza Yousey: COVID-19 International Research Team
Victoria Zaksas: COVID-19 International Research Team
Saswati Das: COVID-19 International Research Team
Stephen B. Baylin: COVID-19 International Research Team
Douglas C. Wallace: COVID-19 International Research Team
Frank J. Slack: Harvard Medical School
Francisco J. Enguita: COVID-19 International Research Team
Eve Syrkin Wurtele: Iowa State University
Dorothy Teegarden: Purdue University
Robert Meller: COVID-19 International Research Team
Daniel Cifuentes: Boston University Chobanian & Avedisian School of Medicine
Afshin Beheshti: COVID-19 International Research Team

Nature Communications, 2024, vol. 15, issue 1, 1-16

Abstract: Abstract 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.

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-024-49813-w Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-49813-w

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-49813-w

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().