ArticleACS medicinal chemistry letters2021
Mining for Ligandable Cavities in RNA.
Article in ACS medicinal chemistry letters, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
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The trial behind it
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Who cites it
7 citing papers in PubMed.
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- Structure guided discovery of small molecule ligands targeting the oncomiR-1 NPSL2 hairpin.Scientific reports · 2025Article
- DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning.Nucleic acids research · 2025Article
- Advances in machine-learning approaches to RNA-targeted drug design.Artificial intelligence chemistry · 2024Article
- Small molecule approaches to targeting RNA.Nature reviews. Chemistry · 2024Review
- Structural and computational studies of HIV-1 RNA.RNA biology · 2024Review
- Virtual Screening of Hepatitis B Virus Pre-Genomic RNA as a Novel Therapeutic Target.Molecules (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying potential ligand binding cavities is a critical step in structure-based screening of biomolecular targets. Cavity mapping methods can detect such binding cavities; however, for ribonucleic acid (RNA) targets, determining which of the detected cavities are "ligandable" remains an unsolved challenge. In this study, we trained a set of machine learning classifiers to distinguish ligandable RNA cavities from decoy cavities. Application of our classifiers to two independent test sets demonstrated that we could recover ligandable cavities from decoys with an AUC > 0.83. Interestingly, when we applied our classifiers to a library of modeled structures of the HIV-1 transactivation response (TAR) element RNA, we found that several of the conformers that harbored cavities with high ligandability scores resembled known holo-TAR structures. On the basis of our results, we envision that our classifiers could find utility as a tool to parse RNA structures and prospectively mine for ligandable binding cavities and, in so doing, facilitate structure-based virtual screening efforts against RNA drug targets.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.