Evidence map›Paper›PMID 37293430›Full record

ArticleWiley interdisciplinary reviews. Computational molecular science

RNA-ligand molecular docking: advances and challenges.

Yuanzhe Zhou, Yangwei Jiang, Shi-Jie Chen

Abstract read
In one paragraph

Article in Wiley interdisciplinary reviews. Computational molecular science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.

0numbers the graph read from it
0cells of the map it votes in
44citing 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

44 citing papers in PubMed.

  1. Review
  2. Article
  3. Comparative Evaluation of Explicit Solvent Models for RNA-Ligand Docking.Journal of chemical information and modeling · 2026
    Article
  4. Article
  5. Article
  6. Review
  7. ACS chemical biology · 2026
    Article
  8. Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Article
  14. Review
  15. Review
  16. Article
  17. Article
  18. Article
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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

3 authors.

Yuanzhe ZhouDepartment of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA.ORCID 0000-0003-1858-3067
Yangwei JiangDepartment of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA.ORCID 0000-0001-7618-6874
Shi-Jie ChenDepartment of Physics and Astronomy, Department of Biochemistry, Institute of Data Sciences and Informatics, University of Missouri, Columbia, MO 65211-7010, USA.ORCID 0000-0002-8093-7244

Funding

New methods for computational modeling of RNA structuresR35GM134919 · NIGMS · UNIVERSITY OF MISSOURI-COLUMBIA · PI SHI-JIE CHEN · 2020 to 2026
$3.3M
New computational tools for predicting ion effects in RNA structuresR01GM117059 · NIGMS · UNIVERSITY OF MISSOURI-COLUMBIA · PI CHEN, SHI-JIE · 2017 to 2020
$1.2M
NIGMS NIH HHS R01 GM117059NIGMS NIH HHS R35 GM134919
6 · The paper itself

Abstract

With rapid advances in computer algorithms and hardware, fast and accurate virtual screening has led to a drastic acceleration in selecting potent small molecules as drug candidates. Computational modeling of RNA-small molecule interactions has become an indispensable tool for RNA-targeted drug discovery. The current models for RNA-ligand binding have mainly focused on the docking-and-scoring method. Accurate docking and scoring should tackle four crucial problems: (1) conformational flexibility of ligand, (2) conformational flexibility of RNA, (3) efficient sampling of binding sites and binding poses, and (4) accurate scoring of different binding modes. Moreover, compared with the problem of protein-ligand docking, predicting ligand binding to RNA, a negatively charged polymer, is further complicated by additional effects such as metal ion effects. Thermodynamic models based on physics-based and knowledge-based scoring functions have shown highly encouraging success in predicting ligand binding poses and binding affinities. Recently, kinetic models for ligand binding have further suggested that including dissociation kinetics (residence time) in ligand docking would result in improved performance in estimating

Identifiers

PMID37293430
PMCPMC10250017

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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.