ReviewComputational and structural biotechnology journal2023
Current understanding of nucleoside analogs inhibiting the SARS-CoV-2 RNA-dependent RNA polymerase.
Review in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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.
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
7 citing papers in PubMed.
- The nucleoside analog CMX521 inhibits coronavirus RNA-dependent RNA polymerase via a two-pronged mechanism.bioRxiv : the preprint server for biology · 2026Article
- DNA-dependent RNA polymerase incorporates β-D-N4-hydroxycytidine (NHC) linking Molnupiravir to host transcription-dependent mutagenesis.The Journal of biological chemistry · 2026Article
- Antivirals in COVID-19: A Focus on Pediatric Cardiac Patients.The Canadian journal of infectious diseases & medical microbiology = Journal canadien des maladies infectieuses et de la microbiologie medicale · 2025Review
- Navigating the COVID-19 Therapeutic Landscape: Unveiling Novel Perspectives on FDA-Approved Medications, Vaccination Targets, and Emerging Novel Strategies.Molecules (Basel, Switzerland) · 2024Review
- Main and papain-like proteases as prospective targets for pharmacological treatment of coronavirus SARS-CoV-2.RSC advances · 2023Review
- Assessing the Potential Contribution of In Silico Studies in Discovering Drug Candidates That Interact with Various SARS-CoV-2 Receptors.International journal of molecular sciences · 2023Review
- Transcription Kinetics in the Coronavirus Life Cycle.Wiley interdisciplinary reviews. RNAReview
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
No grant is acknowledged in the PubMed record.
Abstract
Since the outbreak of the COVID-19 pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA-dependent RNA polymerase (RdRp) has become a main target for antiviral therapeutics due to its essential role in viral replication and transcription. Thus, nucleoside analogs structurally resemble the natural RdRp substrate and hold great potential as inhibitors. Until now, extensive experimental investigations have been performed to explore nucleoside analogs to inhibit the RdRp, and concerted efforts have been made to elucidate the underlying molecular mechanisms further. This review begins by discussing the nucleoside analogs that have demonstrated inhibition in the experiments. Second, we examine the current understanding of the molecular mechanisms underlying the action of nucleoside analogs on the SARS-CoV-2 RdRp. Recent findings in structural biology and computational research are presented through the classification of inhibitory mechanisms. This review summarizes previous experimental findings and mechanistic investigations of nucleoside analogs inhibiting SARS-CoV-2 RdRp. It would guide the rational design of antiviral medications and research into viral transcriptional mechanisms.
Indexed as
Identifiers
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.