ArticleJournal of chemical information and modeling2012
Novel insights of structure-based modeling for RNA-targeted drug discovery.
Article in Journal of chemical information and modeling, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
21 citing papers in PubMed.
- Comparative Evaluation of Explicit Solvent Models for RNA-Ligand Docking.Journal of chemical information and modeling · 2026Article
- RLDOCKScore: A Scoring Function for RNA-Ligand Docking and Small Molecule Virtual Screening.Journal of chemical theory and computation · 2025Article
- Review
- Review on Advancement of AI in Cell Engineering and Molecular Biology.Methods in molecular biology (Clifton, N.J.) · 2025Review
- Small molecules targeting microRNAs: new opportunities and challenges in precision cancer therapy.Trends in cancer · 2024Review
- SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening.Journal of chemical theory and computation · 2024Article
- Advances in machine-learning approaches to RNA-targeted drug design.Artificial intelligence chemistry · 2024Article
- RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method.Briefings in bioinformatics · 2024Article
- Comparative Assessment of Docking Programs for Docking and Virtual Screening of Ribosomal Oxazolidinone Antibacterial Agents.Antibiotics (Basel, Switzerland) · 2023Article
- AnnapuRNA: A scoring function for predicting RNA-small molecule binding poses.PLoS computational biology · 2021Article
- How 'Protein-Docking' Translates into the New Emerging Field of Docking Small Molecules to Nucleic Acids?Molecules (Basel, Switzerland) · 2020Review
- Understanding the Contributions of Conformational Changes, Thermodynamics, and Kinetics of RNA-Small Molecule Interactions.ACS chemical biology · 2019Review
- High-performance virtual screening by targeting a high-resolution RNA dynamic ensemble.Nature structural & molecular biology · 2018Article
- Drugging tRNA aminoacylation.RNA biology · 2018Review
- SPA-LN: a scoring function of ligand-nucleic acid interactions via optimizing both specificity and affinity.Nucleic acids research · 2017Article
- Article
- Comprehensive Modeling and Discovery of Mebendazole as a Novel TRAF2- and NCK-interacting Kinase Inhibitor.Scientific reports · 2016Article
- Small molecule compounds targeting miRNAs for cancer therapy.Advanced drug delivery reviews · 2015Review
- Structural and energetic analysis of 2-aminobenzimidazole inhibitors in complex with the hepatitis C virus IRES RNA using molecular dynamics simulations.Journal of chemical information and modeling · 2014Article
- rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids.PLoS computational biology · 2014Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Substantial progress in RNA biology highlights the importance of RNAs (e.g., microRNAs) in diseases and the potential of targeting RNAs for drug discovery. However, the lack of RNA-specific modeling techniques demands the development of new tools for RNA-targeted rational drug design. Herein, we implemented integrated approaches of accurate RNA modeling and virtual screening for RNA inhibitor discovery with the most comprehensive evaluation to date of five docking and 11 scoring methods. For the first time, statistical analysis was heavily employed to assess the significance of our predictions. We found that GOLD:GOLD Fitness and rDock:rDock_solv could accurately predict the RNA ligand poses, and ASP rescoring further improved the ranking of ligand binding poses. Due to the weak correlations (R(2) < 0.3) of existing scoring with experimental binding affinities, we implemented two new RNA-specific scoring functions, iMDLScore1 and iMDLScore2, and obtained better correlations with R(2) = 0.70 and 0.79, respectively. We also proposed a multistep virtual screening approach and demonstrated that rDock:rDock_solv together with iMDLScore2 rescoring obtained the best enrichment on the flexible RNA targets, whereas GOLD:GOLD Fitness combined with rDock_solv rescoring outperformed other methods for rigid RNAs. This study provided practical strategies for RNA modeling and offered new insights into RNA-small molecule interactions for drug discovery.
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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.