Evidence map›Paper›PMID 40859960›Full record

ReviewMedComm2025

Discovery of RNA-Targeting Small Molecules: Challenges and Future Directions.

Zhengguo Cai, Hongli Ma, Fengcan Ye, Dingwei Lei, Zhenfeng Deng, Yongge Li, Ruichu Gu, Han Wen

Abstract readReview
In one paragraph

Review in MedComm, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
  4. Machine Learning for RNA-Targeting Drug Design.Journal of chemical information and modeling · 2026
    Review
  5. Review
  6. Review
  7. Review
  8. Review
  9. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Zhengguo CaiDP Technology Beijing China.ORCID https://orcid.org/0000-0002-4819-2115
Hongli MaSchool of Mathematics Harbin Institute of Technology Harbin China.
Fengcan YeCenter for Quantitative Biology Academy for Advanced Interdisciplinary Studies Peking University Beijing China.
Dingwei LeiSchool of Pharmaceutical Sciences Peking University Beijing China.
Zhenfeng DengProgram in Molecular Medicine The Hospital for Sick Children Research Institute Toronto Ontario Canada.
Yongge LiDP Technology Beijing China.
Ruichu GuState Key Laboratory of Protein and Plant Gene Research School of Life Sciences Peking University Beijing China.
Han WenDP Technology Beijing China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

RNA-targeting small molecules represent a transformative frontier in drug discovery, offering novel therapeutic avenues for diseases traditionally deemed undruggable. This review explores the latest advancements in the development of RNA-binding small molecules, focusing on the current obstacles and promising avenues for future research. We highlight innovations in RNA structure determination, including X-ray crystallography, nuclear magnetic resonance spectroscopy, and cryo-electron microscopy, which provide the foundation for rational drug design. The role of computational approaches, such as deep learning and molecular docking, is emphasized for enhancing RNA structure prediction and ligand screening efficiency. Additionally, we discuss the utility of focused libraries, DNA-encoded libraries, and small-molecule microarrays in identifying bioactive ligands, alongside the potential of fragment-based drug discovery for exploring chemical space. Emerging strategies, such as RNA degraders and modulators of RNA-protein interactions, are reviewed for their therapeutic promise. Specifically, we underscore the pivotal role of artificial intelligence and machine learning in accelerating discovery and optimizing RNA-targeted therapeutics. By synthesizing these advancements, this review aims to inspire further research and collaboration, unlocking the full potential of RNA-targeting small molecules to revolutionize treatment paradigms for a wide range of diseases.

Indexed as

bioactive small moleculescomputer‐aided designmachine learningRNA‐degraderRNA:protein interactionsRNA‐targeting

Identifiers

PMID40859960
PMCPMC12375692

What OpenQuestion holds

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Registered trials

None linked

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.