ArticleJournal of cheminformatics2021
Using informative features in machine learning based method for COVID-19 drug repurposing.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Dangling centrality highlights critical nodes by evaluating network stability through link removal.Scientific reports · 2025Article
- A comprehensive large scale biomedical knowledge graph for AI powered data driven biomedical research.bioRxiv : the preprint server for biology · 2025Article
- AI-driven drug discovery and repurposing using multi-omics for myocardial infarction and heart failure.Exploration of medicine · 2025Article
- Repurposing of Anti-Infectives for the Management of Onchocerciasis Using Machine Learning and Protein Docking Studies.Bioinformatics and biology insights · 2025Article
- Systems biology approaches to identify driver genes and drug combinations for treating COVID-19.Scientific reports · 2024Article
- Integrating Transcriptomic and Structural Insights: Revealing Drug Repurposing Opportunities for Sporadic ALS.ACS omega · 2024Article
- How Deep Learning in Antiviral Molecular Profiling Identified Anti-SARS-CoV-2 Inhibitors.Biomedicines · 2023Article
- Prospects of Novel and Repurposed Immunomodulatory Drugs against Acute Respiratory Distress Syndrome (ARDS) Associated with COVID-19 Disease.Journal of personalized medicine · 2023Review
- CORN-Condition Orientated Regulatory Networks: bridging conditions to gene networks.Briefings in bioinformatics · 2022Article
- Comprehensive analysis of pathways in Coronavirus 2019 (COVID-19) using an unsupervised machine learning method.Applied soft computing · 2022Article
- A comprehensive review of artificial intelligence and network based approaches to drug repurposing in Covid-19.Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie · 2022Review
- Interaction of the new inhibitor paxlovid (PF-07321332) and ivermectin with the monomer of the main protease SARS-CoV-2: A volumetric study based on molecular dynamics, elastic networks, classical thermodynamics and SPT.Computational biology and chemistry · 2022Article
- Protein-protein interaction network of E. coli K-12 has significant high-dimensional cavities: new insights from algebraic topological studies.FEBS open bio · 2022Article
- Antiparasitic Drugs against SARS-CoV-2: A Comprehensive Literature Survey.Microorganisms · 2022Review
- Immunomodulatory Properties of Human Breast Milk: MicroRNA Contents and Potential Epigenetic Effects.Biomedicines · 2022Review
- COVID-19 Drug Repurposing: A Network-Based Framework for Exploring Biomedical Literature and Clinical Trials for Possible Treatments.Pharmaceutics · 2022Article
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Coronavirus disease 2019 (COVID-19) is caused by a novel virus named Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2). This virus induced a large number of deaths and millions of confirmed cases worldwide, creating a serious danger to public health. However, there are no specific therapies or drugs available for COVID-19 treatment. While new drug discovery is a long process, repurposing available drugs for COVID-19 can help recognize treatments with known clinical profiles. Computational drug repurposing methods can reduce the cost, time, and risk of drug toxicity. In this work, we build a graph as a COVID-19 related biological network. This network is related to virus targets or their associated biological processes. We select essential proteins in the constructed biological network that lead to a major disruption in the network. Our method from these essential proteins chooses 93 proteins related to COVID-19 pathology. Then, we propose multiple informative features based on drug-target and protein-protein interaction information. Through these informative features, we find five appropriate clusters of drugs that contain some candidates as potential COVID-19 treatments. To evaluate our results, we provide statistical and clinical evidence for our candidate drugs. From our proposed candidate drugs, 80% of them were studied in other studies and clinical trials.
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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.