ArticlePLoS computational biology2021
AnnapuRNA: A scoring function for predicting RNA-small molecule binding poses.
Article in PLoS computational biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
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Who cites it
33 citing papers in PubMed.
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- Multiscale Modeling of the Bacterial Ribosome to Identify Potential Peptide Modulators and Their Allosteric Effects.Biochemistry · 2026Article
- Critical Assessment of a Structure-Based Pipeline for Targeting the Long Noncoding RNA MALAT1.Journal of chemical information and modeling · 2026Article
- A multi-stage computational pipeline andRSC advances · 2026Article
- Article
- Therapeutic rewiring of ceRNA networks: a computational pipeline for drug repurposing in acute kidney injury via circRNA-miRNA-mRNA axis disruption.Human genomics · 2026Review
- DeepRNA-DTI: a deep learning approach for RNA-compound interaction prediction with binding site interpretability.Journal of cheminformatics · 2025Article
- RLDOCKScore: A Scoring Function for RNA-Ligand Docking and Small Molecule Virtual Screening.Journal of chemical theory and computation · 2025Article
- Small Molecule Approach to RNA Targeting Binder Discovery (SMARTBind) Using Deep Learning Without Structural Input.bioRxiv : the preprint server for biology · 2025Article
- Review
- Comment on "Contrastive pre-training and 3D convolution neural network for RNA and small molecule binding affinity prediction" by Sun and Gao.Bioinformatics (Oxford, England) · 2025Article
- 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
- Advances in machine-learning approaches to RNA-targeted drug design.Artificial intelligence chemistry · 2024Article
- Structure of the SARS-CoV-2 Frameshift Stimulatory Element with an Upstream Multibranch Loop.Biochemistry · 2024Article
- RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method.Briefings in bioinformatics · 2024Article
- Discovery of RNA secondary structural motifs using sequence-ordered thermodynamic stability and comparative sequence analysis.MethodsX · 2023Article
- Assessing Genetic Algorithm-Based Docking Protocols for Prediction of Heparin Oligosaccharide Binding Geometries onto Proteins.Biomolecules · 2023Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
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2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
RNA is considered as an attractive target for new small molecule drugs. Designing active compounds can be facilitated by computational modeling. Most of the available tools developed for these prediction purposes, such as molecular docking or scoring functions, are parametrized for protein targets. The performance of these methods, when applied to RNA-ligand systems, is insufficient. To overcome these problems, we developed AnnapuRNA, a new knowledge-based scoring function designed to evaluate RNA-ligand complex structures, generated by any computational docking method. We also evaluated three main factors that may influence the structure prediction, i.e., the starting conformer of a ligand, the docking program, and the scoring function used. We applied the AnnapuRNA method for a post-hoc study of the recently published structures of the FMN riboswitch. Software is available at https://github.com/filipspl/AnnapuRNA.
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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.