ArticleMethods (San Diego, Calif.)2022
RLDOCK method for predicting RNA-small molecule binding modes.
Article in Methods (San Diego, Calif.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
What it found
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Who cites it
11 citing papers in PubMed.
- In silico designing of small molecules for targeting RNA: current landscape and future directions.Journal of computer-aided molecular design · 2026Review
- RLDOCKScore: A Scoring Function for RNA-Ligand Docking and Small Molecule Virtual Screening.Journal of chemical theory and computation · 2025Article
- Review
- Harnessing Computational Approaches for RNA-Targeted Drug Discovery.RNA nanomed · 2024Article
- SPRank─A Knowledge-Based Scoring Function for RNA-Ligand Pose Prediction and Virtual Screening.Journal of chemical theory and computation · 2024Article
- Identifying small-molecules binding sites in RNA conformational ensembles with SHAMAN.Nature communications · 2024Article
- Cracking the Code: Enhancing Molecular Tools for Progress in Nanobiotechnology.ACS applied bio materials · 2024Review
- Advances in machine-learning approaches to RNA-targeted drug design.Artificial intelligence chemistry · 2024Article
- Reliable method for predicting the binding affinity of RNA-small molecule interactions using machine learning.Briefings in bioinformatics · 2024Article
- Biomotors, viral assembly, and RNA nanobiotechnology: Current achievements and future directions.Computational and structural biotechnology journal · 2022Review
- RNA-ligand molecular docking: advances and challenges.Wiley interdisciplinary reviews. Computational molecular scienceArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
RNA molecules play critical roles in cellular functions at the level of gene expression and regulation. The intricate 3D structures and the functional roles of RNAs make RNA molecules ideal targets for therapeutic drugs. The rational design of RNA-targeted drug requires accurate modeling of RNA-ligand interactions. Recently a new computational tool, RLDOCK, was developed to predict ligand binding sites and binding poses. Using an iterative multiscale sampling and search algorithm and a energy-based evaluation of ligand poses, the method enables efficient and accurate predictions for RNA-ligand interactions. Here we present a detailed illustration of the computational procedure for the practical implementation of the RLDOCK method. Using Flavin mononucleotide (FMN) docking to F. nucleatum FMN riboswitch as an example, we illustrate the computational protocol for RLDOCK-based prediction of RNA- ligand interactions. The RLDOCK software is freely accessible at http://https://github.com/Vfold-RNA/RLDOCK.
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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.