ArticleJournal of cellular and molecular medicine2022
Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID-19.
Article in Journal of cellular and molecular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A deep drug prediction framework for viral infectious diseases using an optimizer-based ensemble of convolutional neural network: COVID-19 as a case study.Molecular diversity · 2025Article
- Molecular Dynamics-Guided Repositioning of FDA-Approved Drugs for PD-L1 Inhibition with In Vitro Anticancer Potential.International journal of molecular sciences · 2025Article
- GiGs: graph-based integrated Gaussian kernel similarity for virus-drug association prediction.Briefings in bioinformatics · 2025Article
- Protein structure prediction via deep learning: an in-depth review.Frontiers in pharmacology · 2025Review
- A comparative benchmarking and evaluation framework for heterogeneous network-based drug repositioning methods.Briefings in bioinformatics · 2024Article
- Drug repositioning based on weighted local information augmented graph neural network.Briefings in bioinformatics · 2023Article
- Guiding Drug Repositioning for Cancers Based on Drug Similarity Networks.International journal of molecular sciences · 2023Article
- A review of SARS-CoV-2 drug repurposing: databases and machine learning models.Frontiers in pharmacology · 2023Review
- The roles of primary care doctors in the COVID-19 pandemic: consistency and influencing factors of doctor's perception and actions and nominal definitions.BMC health services research · 2022Article
- Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID-19.Journal of cellular and molecular medicine · 2022Article
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Authors and funding
8 authors.
Funding
Abstract
Amid the COVID-19 crisis, we put sizeable efforts to collect a high number of experimentally validated drug-virus association entries from literature by text mining and built a human drug-virus association database. To the best of our knowledge, it is the largest publicly available drug-virus database so far. Next, we develop a novel weight regularization matrix factorization approach, termed WRMF, for in silico drug repurposing by integrating three networks: the known drug-virus association network, the drug-drug chemical structure similarity network, and the virus-virus genomic sequencing similarity network. Specifically, WRMF adds a weight to each training sample for reducing the influence of negative samples (i.e. the drug-virus association is unassociated). A comparison on the curated drug-virus database shows that WRMF performs better than a few state-of-the-art methods. In addition, we selected the other two different public datasets (i.e. Cdataset and HMDD V2.0) to assess WRMF's performance. The case study also demonstrated the accuracy and reliability of WRMF to infer potential drugs for the novel virus. In summary, we offer a useful tool including a novel drug-virus association database and a powerful method WRMF to repurpose potential drugs for new viruses.
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