Evidence map›Paper›PMID 39860045›Full record

ReviewLife (Basel, Switzerland)2025

Advances and Mechanisms of RNA-Ligand Interaction Predictions.

Chen Zhuo, Chengwei Zeng, Haoquan Liu, Huiwen Wang, Yunhui Peng, Yunjie Zhao

Abstract readReview
In one paragraph

Review in Life (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Accurate RNA-Ligand Binding Site Prediction Based on a Multi-Channel Graph Neural Network.Interdisciplinary sciences, computational life sciences · 2026
    Article
  2. Article
  3. Review
  4. Article
  5. Review
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

6 authors.

Chen ZhuoInstitute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
Chengwei ZengInstitute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
Haoquan LiuInstitute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
Huiwen WangSchool of Physics and Engineering, Henan University of Science and Technology, Luoyang 471023, China.
Yunhui PengInstitute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.
Yunjie ZhaoInstitute of Biophysics and Department of Physics, Central China Normal University, Wuhan 430079, China.ORCID 0000-0002-5256-9456

Funding

National Natural Science Foundation of China 12175081
6 · The paper itself

Abstract

The diversity and complexity of RNA include sequence, secondary structure, and tertiary structure characteristics. These elements are crucial for RNA's specific recognition of other molecules. With advancements in biotechnology, RNA-ligand structures allow researchers to utilize experimental data to uncover the mechanisms of complex interactions. However, determining the structures of these complexes experimentally can be technically challenging and often results in low-resolution data. Many machine learning computational approaches have recently emerged to learn multiscale-level RNA features to predict the interactions. Predicting interactions remains an unexplored area. Therefore, studying RNA-ligand interactions is essential for understanding biological processes. In this review, we analyze the interaction characteristics of RNA-ligand complexes by examining RNA's sequence, secondary structure, and tertiary structure. Our goal is to clarify how RNA specifically recognizes ligands. Additionally, we systematically discuss advancements in computational methods for predicting interactions and to guide future research directions. We aim to inspire the creation of more reliable RNA-ligand interaction prediction tools.

Indexed as

RNA–ligand interaction mechanismRNA pocket geometric featureRNA secondary structure motifsstructure prediction

Identifiers

PMID39860045
PMCPMC11767038

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.