ArticleAngewandte Chemie (International ed. in English)2023
Machine Learning Informs RNA-Binding Chemical Space.
Article in Angewandte Chemie (International ed. in English), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers.
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Who cites it
42 citing papers in PubMed.
- Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026Review
- AGAPE (Computational G‑Quadruplex Stabilization Prediction): The First Machine Learning Workflow for G‑Quadruplex Stabilization Prediction.ACS omega · 2026Article
- Article
- AI-Augmented Iterative Screening of Libraries Against RNA Targets (AISLAR) Boosts Discovery of SAR-Tractable RNA Binders and Rational Analog Design.Small science · 2026Article
- RNA functional modulation by Mitoxantrone via RNA structural ensemble repartitioning.Nature communications · 2026Article
- Article
- Predicting small molecule-RNA interactions without RNA tertiary structures.Nature biotechnology · 2026Article
- Generative Chemistry Platform for Small Molecules Targeting RNA: A Case Study for Chemical Optimization.Computational and structural biotechnology journal · 2026Article
- Transcriptome-wide mapping reveals an RNA-dependent mechanism of platinum cancer drugs.bioRxiv : the preprint server for biology · 2025Article
- DeepRNA-DTI: a deep learning approach for RNA-compound interaction prediction with binding site interpretability.Journal of cheminformatics · 2025Article
- Docking and Molecular Dynamics Simulation-Based Analysis of Advanced Small-Molecule Kinase Inhibitors Identified pre-let-7 miRNA Binders.Chembiochem : a European journal of chemical biology · 2025Article
- Evaluation of single-template ligand-based methods for the discovery of small-molecule nucleic acid binders.Briefings in bioinformatics · 2025Article
- DistRMI: a deep distance-aware neural network for explainable RNA loop motif-small molecule interaction prediction.Briefings in bioinformatics · 2025Article
- RLDOCKScore: A Scoring Function for RNA-Ligand Docking and Small Molecule Virtual Screening.Journal of chemical theory and computation · 2025Article
- Discovery of small molecule probes targeting the 5' stem-loop in the yeast U4/U6 snRNA.bioRxiv : the preprint server for biology · 2025Article
- Discovery of RNA-Reactive Small Molecules Guides the Design of Electrophilic Modules for RNA-Specific Covalent Binders.Journal of the American Chemical Society · 2025Article
- Review
- Structure-informed design of an ultrabright RNA-activated fluorophore.Nature chemistry · 2025Article
- Mechanistic studies of small molecule ligands selective to RNA single G bulges.Nucleic acids research · 2025Article
- Ligand-binding pockets in RNA and where to find them.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Small molecule targeting of RNA has emerged as a new frontier in medicinal chemistry, but compared to the protein targeting literature our understanding of chemical matter that binds to RNA is limited. In this study, we reported Repository Of BInders to Nucleic acids (ROBIN), a new library of nucleic acid binders identified by small molecule microarray (SMM) screening. The complete results of 36 individual nucleic acid SMM screens against a library of 24 572 small molecules were reported (including a total of 1 627 072 interactions assayed). A set of 2 003 RNA-binding small molecules was identified, representing the largest fully public, experimentally derived library of its kind to date. Machine learning was used to develop highly predictive and interpretable models to characterize RNA-binding molecules. This work demonstrates that machine learning algorithms applied to experimentally derived sets of RNA binders are a powerful method to inform RNA-targeted chemical space.
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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.