ArticleArtificial intelligence chemistry2024
Advances in machine-learning approaches to RNA-targeted drug design.
Article in Artificial intelligence chemistry, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Designing small molecules targeting a cryptic RNA binding site through base displacement.Nature chemical biology · 2026Article
- Targeting Non-Coding RNAs as a Potential Therapeutic and Delivery Strategy Against Neurodegenerative Diseases.International journal of molecular sciences · 2026Review
- Targeting Non-coding RNAs in Neurodegeneration: Advances in Therapeutic RNA Modalities and Next-Gen Delivery Technologies.Current Alzheimer research · 2026Review
- DeepRNA-DTI: a deep learning approach for RNA-compound interaction prediction with binding site interpretability.Journal of cheminformatics · 2025Article
- Machine learning approaches for predicting the small molecule-miRNA associations: a comprehensive review.Molecular diversity · 2025Review
- Porphyrins as Chiroptical Conformational Probes for Biomolecules.Molecules (Basel, Switzerland) · 2025Review
- DRLiPS: a novel method for prediction of druggable RNA-small molecule binding pockets using machine learning.Nucleic acids research · 2025Article
- Advances and Mechanisms of RNA-Ligand Interaction Predictions.Life (Basel, Switzerland) · 2025Review
- Harnessing Computational Approaches for RNA-Targeted Drug Discovery.RNA nanomed · 2024Article
- Pharmacokinetics-Pharmacodynamics Modeling for Evaluating Drug-Drug Interactions in Polypharmacy: Development and Challenges.Clinical pharmacokinetics · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
RNA molecules play multifaceted functional and regulatory roles within cells and have garnered significant attention in recent years as promising therapeutic targets. With remarkable successes achieved by artificial intelligence (AI) in different fields such as computer vision and natural language processing, there is a growing imperative to harness AI's potential in computer-aided drug design (CADD) to discover novel drug compounds that target RNA. Although machine-learning (ML) approaches have been widely adopted in the discovery of small molecules targeting proteins, the application of ML approaches to model interactions between RNA and small molecule is still in its infancy. Compared to protein-targeted drug discovery, the major challenges in ML-based RNA-targeted drug discovery stem from the scarcity of available data resources. With the growing interest and the development of curated databases focusing on interactions between RNA and small molecule, the field anticipates a rapid growth and the opening of a new avenue for disease treatment. In this review, we aim to provide an overview of recent advancements in computationally modeling RNA-small molecule interactions within the context of RNA-targeted drug discovery, with a particular emphasis on methodologies employing ML techniques.
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What OpenQuestion holds
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.