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

Galeano, Diego; Imrat; Haltom, Jeffrey; Andolino, Chaylen; Yousey, Aliza; Zaksas, Victoria; Das, Saswati; Baylin, Stephen B.; Wallace, Douglas C.; Slack, Frank J.; Enguita, Francisco J.; Wurtele, Eve; Teegarden, Dorothy; Meller, Robert; Cifuentes, Daniel; Beheshti, Afshin

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

File

2024-Wurtele-sChemNETDeepLearning.pdf (10.63 MB)

Date

2024-10-23

Authors

Galeano, Diego

Imrat

Haltom, Jeffrey

Andolino, Chaylen

Yousey, Aliza

Zaksas, Victoria

Das, Saswati

Baylin, Stephen B.

Wallace, Douglas C.

Slack, Frank J.

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Publisher

Nature Research

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.

Academic or Administrative Unit

Bioinformatics and Computational Biology Program

Department of Genetics, Development, and Cell Biology (LAS)

Type

article

Comments

This article is published as Galeano, D., Imrat, Haltom, J. et al. sChemNET: a deep learning framework for predicting small molecules targeting microRNA function. Nat Commun 15, 9149 (2024). https://doi.org/10.1038/s41467-024-49813-w.

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