ReviewInformatics in medicine unlocked2020
Applying computer simulations in battling with COVID-19,
Review in Informatics in medicine unlocked, 2020. 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.
- Artificial Intelligence-Driven Innovations in Oncology Drug Discovery: Transforming Traditional Pipelines and Enhancing Drug Design.Drug design, development and therapy · 2025Review
- Application of artificial intelligence (AI) to control COVID-19 pandemic: Current status and future prospects.Heliyon · 2024Review
- The Important Role of Interleukin-2 in COVID-19.Journal of immunology research · 2023Review
- COVID-19, from infection to cancer.Acta bio-medica : Atenei Parmensis · 2022Article
- Phosphopeptide enrichment using Phos-tag technology reveals functional phosphorylation of the nucleocapsid protein of SARS-CoV-2.Journal of proteomics · 2022Article
- Nature against Diabetic Retinopathy: A Review on Antiangiogenic, Antioxidant, and Anti-Inflammatory Phytochemicals.Evidence-based complementary and alternative medicine : eCAM · 2022Review
- Screening of Severe Acute Respiratory Syndrome Coronavirus 2 RNA-Dependent RNA Polymerase Inhibitors Using Computational Approach.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Drug repurposing for coronavirus (SARS-CoV-2) based on gene co-expression network analysis.Scientific reports · 2021Article
- Flavonoids as Promising Antiviral Agents against SARS-CoV-2 Infection: A Mechanistic Review.Molecules (Basel, Switzerland) · 2021Review
- A computational approach for the screening of potential antiviral compounds against SARS-CoV-2 protease: Ionic liquid vs herbal and natural compounds.Journal of molecular liquids · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease 2019 (COVID-19) has made many concerns for healthcare services especially, in finding useful therapeutic(s). Despite the scientists' struggle to find and/or creating possible drugs, so far there is no treatment with high efficiency for the disease. During the pandemic, researchers have performed some molecular analyses to find potential therapeutics out of both the natural and synthetic available medicines. Computer simulations and related data have shown a significant role in drug discovery and development before. In this field, antiviral drugs, phytochemicals, anti-inflammatory agents, etc. were essential groups of compounds tested against COVID-19, using molecular modeling, molecular dynamics (MD), and docking tools. The results indicate promising effects of such compounds to be used in further experimental and clinical trials; Chloroquine, Chloroquine-OH, and Umifenovir as viral entry inhibitors, Remdesivir, Ribavirin, Lopinavir, Ritonavir, and Darunavir as viral replication inhibitors, and Sirolimus are the examples, which were tested clinically on patients after comprehensive assessments of the available data on molecular simulation. This review summarizes the outcomes of various computer simulations data in the battle against COVID-19.
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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.