ArticleJournal of chemical information and modeling2021
DrugPred_RNA-A Tool for Structure-Based Druggability Predictions for RNA Binding Sites.
Article in Journal of chemical information and modeling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 21 citations in OpenAlex.
- In silico designing of small molecules for targeting RNA: current landscape and future directions.Journal of computer-aided molecular design · 2026Review
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- Comparative Evaluation of Explicit Solvent Models for RNA-Ligand Docking.Journal of chemical information and modeling · 2026Article
- DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning.Nucleic acids research · 2025Article
- Exploring chemical space for "druglike" small molecules in the age of AI.Frontiers in molecular biosciences · 2025Review
- Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN.Nature communications · 2024Article
- Riboswitches as Drug Targets for Antibiotics.Antibiotics (Basel, Switzerland) · 2021Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA is an emerging target for drug discovery. However, like for proteins, not all RNA binding sites are equally suited to be addressed with conventional drug-like ligands. To this end, we have developed the structure-based druggability predictor DrugPred_RNA to identify druggable RNA binding sites. Due to the paucity of annotated RNA binding sites, the predictor was trained on protein pockets, albeit using only descriptors that can be calculated for both RNA and protein binding sites. DrugPred_RNA performed well in discriminating druggable from less druggable binding sites for the protein set and delivered predictions for selected RNA binding sites that agreed with manual assignment. In addition, most drug-like ligands contained in an RNA test set were found in pockets predicted to be druggable, further adding confidence to the performance of DrugPred_RNA. The method is robust against conformational and sequence changes in the binding sites and can contribute to direct drug discovery efforts for RNA targets.
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Registered trials
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