ArticleDaru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences2024
Computational Screening Using a Combination of Ligand-Based Machine Learning and Molecular Docking Methods for the Repurposing of Antivirals Targeting the SARS-CoV-2 Main Protease.
Article in Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed, 10 citations in OpenAlex.
- A computational study of cardiac glycosides from Vernonia amygdalina as PI3K inhibitors for targeting HER2 positive breast cancer.Journal of computer-aided molecular design · 2025Article
- Citrus flavonoids for overcoming breast cancer resistance to methotrexate: identification of potential targets of nobiletin and sinensetin.Discover oncology · 2025Article
- Characterization of Potential Target Genes of Borneol in Increasing Trastuzumab Sensitivity in HER2+ Trastuzumab-Resistant Breast Cancer: Bioinformatics and In Vitro Studies.Asian Pacific journal of cancer prevention : APJCP · 2024Article
- Exploring the Therapeutic Potential of Virtual Screened Novel HER2 Inhibitors via QSAR, Molecular Docking and Dynamics Simulations.Dose-response : a publication of International Hormesis SocietyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCOVID-19 is an infectious disease caused by SARS-CoV-2, a close relative of SARS-CoV. Several studies have searched for COVID-19 therapies. The topics of these works ranged from vaccine discovery to natural products targeting the SARS-CoV-2 main protease (M
methodThis study aims to repurpose 10692 drugs in DrugBank by using ligand-based virtual screening (LBVS) machine learning (ML) with Konstanz Information Miner (KNIME) to seek potential therapeutics based on M
resultsThis study identified 3,166 compound candidates inhibiting M
conclusionResults demonstrated the efficiency of LBVS combined with MD. This combined strategy provided positive evidence showing that the top screened drugs, including CCX-140, which had the lowest MD score, can be reasonably advanced to the in vitro phase. This combined method may accelerate the discovery of therapies for novel or orphan diseases from existing drugs.
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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.