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Sun, J.* ; Ru, J. ; Ramos-Mucci, L.* ; Qi, F.* ; Chen, Z.* ; Chen, S.* ; Cribbs, A.P.* ; Deng, L. ; Wang, X.*

DeepsmirUD: Prediction of regulatory effects on microRNA expression mediated by small molecules using deep learning.

Int. J. Mol. Sci. 24:23 (2023)

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	Open Access Gold

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Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated by miRNAs. In recent years, small molecules have demonstrated enormous potential as drugs to regulate miRNA expression (i.e., SM-miR). A clear understanding of the mechanism of action of small molecules on the upregulation and downregulation of miRNA expression allows precise diagnosis and treatment of oncogenic pathways. However, outside of a slow and costly process of experimental determination, computational strategies to assist this on an ad hoc basis have yet to be formulated. In this work, we developed, to the best of our knowledge, the first cross-platform prediction tool, DeepsmirUD, to infer small-molecule-mediated regulatory effects on miRNA expression (i.e., upregulation or downregulation). This method is powered by 12 cutting-edge deep-learning frameworks and achieved AUC values of 0.843/0.984 and AUCPR values of 0.866/0.992 on two independent test datasets. With a complementarily constructed network inference approach based on similarity, we report a significantly improved accuracy of 0.813 in determining the regulatory effects of nearly 650 associated SM-miR relations, each formed with either novel small molecule or novel miRNA. By further integrating miRNA-cancer relationships, we established a database of potential pharmaceutical drugs from 1343 small molecules for 107 cancer diseases to understand the drug mechanisms of action and offer novel insight into drug repositioning. Furthermore, we have employed DeepsmirUD to predict the regulatory effects of a large number of high-confidence associated SM-miR relations. Taken together, our method shows promise to accelerate the development of potential miRNA targets and small molecule drugs.

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Publication type Article: Journal article

Document type Scientific Article

Corresponding Author

Keywords Deep Learning ; Drug Discovery ; Mirnas ; Regulatory Effect Prediction ; Small Molecule Compounds; Targeting Micrornas; Mirna Associations; Cancer; Networks; Biogenesis; Signatures; Database; Genes

ISSN (print) / ISBN 1422-0067

e-ISSN 1661-6596

Journal International Journal of Molecular Sciences

Quellenangaben Volume: 24, Issue: 3, Article Number: 23

Publisher MDPI

Publishing Place Basel

Non-patent literature Publications

Reviewing status Peer reviewed

Institute(s) Institute of Virology (VIRO)

Grants European Research Council