ArticleRNA (New York, N.Y.)2023
Recognizing the power of machine learning and other computational methods to accelerate progress in small molecule targeting of RNA.
Article in RNA (New York, N.Y.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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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
- TRNAU1AP and PRPF39 establish integrated control over processing of most abundant human non-coding RNAs.Nature communications · 2026Article
- 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
- Advances in machine-learning approaches to RNA-targeted drug design.Artificial intelligence chemistry · 2024Article
- Locked Nucleic Acid Oligonucleotides Facilitate RNA•LNA-RNA Triple-Helix Formation and ReduceInternational journal of molecular sciences · 2024Article
- Structural and computational studies of HIV-1 RNA.RNA biology · 2024Review
- Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.Briefings in bioinformatics · 2023Review
- Binding free-energy landscapes of small molecule binder and non-binder to FMN riboswitch: All-atom molecular dynamics.Biophysics and physicobiology · 2023Article
- Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
RNA structures regulate a wide range of processes in biology and disease, yet small molecule chemical probes or drugs that can modulate these functions are rare. Machine learning and other computational methods are well poised to fill gaps in knowledge and overcome the inherent challenges in RNA targeting, such as the dynamic nature of RNA and the difficulty of obtaining RNA high-resolution structures. Successful tools to date include principal component analysis, linear discriminate analysis, k-nearest neighbor, artificial neural networks, multiple linear regression, and many others. Employment of these tools has revealed critical factors for selective recognition in RNA:small molecule complexes, predictable differences in RNA- and protein-binding ligands, and quantitative structure activity relationships that allow the rational design of small molecules for a given RNA target. Herein we present our perspective on the value of using machine learning and other computation methods to advance RNA:small molecule targeting, including select examples and their validation as well as necessary and promising future directions that will be key to accelerate discoveries in this important field.
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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.