Graph neural networks for the identification of novel inhibitors of a small RNA.

ABSTRACT: MicroRNAs (miRNAs) play a crucial role in post-transcriptional gene regulation and have been implicated in various diseases, including cancers and lung disease. In recent years, Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing graph-structured data, making them well-suited for the analysis of molecular structures. In this work, we explore the application of GNNs in ligand-based drug screening for small molecules targeting miR-21. By representing a known dataset of small molecules targeting miR-21 as graphs, GNNs can learn complex relationships between their structures and activities, enabling the prediction of potential miRNA-targeting small molecules by capturing the structural features and similarity between known miRNA-targeting compounds. The use of GNNs in miR

SUBMITTER: Haga CL 

PROVIDER: S-EPMC12282601 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature