English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The prediction of RNA-small molecule binding sites is crucial for thediscovery of effective drugs. Various computational methods have been developedto address this challenge, using information about the structure and sequenceof RNA. In this study, we introduce CplxCavity, a combination of a newalgorithm and a machine learning model specifically designed to predictRNA-small molecule binding sites. CplxCavity leverages the 3D structure of RNAor RNA complexes to identify surface cavities that have the potential to bindwith small molecules. Our results demonstrate that CplxCavity outperformsexisting methods by accurately identifying binding sites for small molecules onRNA or RNA complexes. The introduction of CplxCavity represents a significantadvancement in computational tools for studying RNA-ligand interactions, andoffers promising prospects for accelerating drug discovery and the developmentof therapies targeting RNA.

Predicting RNA-small molecule binding sites by 3D structure