DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning.

ABSTRACT: Ribonucleic Acid (RNA) is the central conduit for information transfer in the cell. Identifying potential RNA targets in disease conditions is a challenging task, given the vast repertoire of functional non-coding RNAs in a human cell. A potential druggable target must satisfy several criteria, including disease association, cellular accessibility, binding pockets for drug-like molecules, and minimal cross-reactivity. While several methods exist for prediction of druggable proteins, they cannot be repurposed for RNAs due to fundamental differences in their binding modality. Taking all these constraints into account, a new structure-based model, Druggable RNA-Ligand binding Pocket Selector (DRLiPS), is developed here to predict binding site-level druggability of any given RNA target. A nove

SUBMITTER: Krishnan SR 

PROVIDER: S-EPMC11963762 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature