SMTRI: A deep learning-based web service for predicting small molecules that target miRNA-mRNA interactions.
Mol Ther Nucleic Acids
; 35(3): 102303, 2024 Sep 10.
Article
en En
| MEDLINE
| ID: mdl-39281703
ABSTRACT
Mature microRNAs (miRNAs) are short, single-stranded RNAs that bind to target mRNAs and induce translational repression and gene silencing. Many miRNAs discovered in animals have been implicated in diseases and have recently been pursued as therapeutic targets. However, conventional pharmacological screening for candidate small-molecule drugs can be time-consuming and labor-intensive. Therefore, developing a computational program to assist mature miRNA-targeted drug discovery in silico is desirable. Our previous work (https//doi.org/10.1002/advs.201903451) revealed that the unique functional loops formed during Argonaute-mediated miRNA-mRNA interactions have stable structural characteristics and may serve as potential targets for small-molecule drug discovery. Developing drugs specifically targeting disease-related mature miRNAs and their target mRNAs would avoid affecting unrelated ones. Here, we present SMTRI, a convolutional neural network-based approach for efficiently predicting small molecules that target RNA secondary structural motifs formed by interactions between miRNAs and their target mRNAs. Measured on three additional testing sets, SMTRI outperformed state-of-the-art algorithms by 12.9%-30.3% in AUC and 2.0%-18.4% in accuracy. Moreover, four case studies on the published experimentally validated RNA-targeted small molecules also revealed the reliability of SMTRI.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Idioma:
En
Revista:
Mol Ther Nucleic Acids
Año:
2024
Tipo del documento:
Article
País de afiliación:
China
Pais de publicación:
Estados Unidos