Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19

Tang, Xianfang; Cai, Liang; Meng, Yajie; Xu, Junlin; Lu, Chenjun; Yang, Jialiang

doi:10.3389/fimmu.2020.603615

Cited by 58 publications

(63 citation statements)

References 56 publications

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“…Since the outbreak of Corona Virus Disease 2019 (COVID- 19), it spreads rapidly all over the world. There is an urgent need for effective drugs to treat and alleviate the deterioration of this novel Coronavirus (11,12).…”

Section: Introductionmentioning

confidence: 99%

“…The treatment of COVID-19 relied on the experience of clinicians (15,16). So far, some drugs have been proved effective in relieving and improving the symptoms of novel coronavirus pneumonia (17)(18)(19)(20)(21)(22). The drugs against the Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-CoV), such as Lopinavir/ritonavir, have been proved to inhibit many viruses (22,23).…”

Section: Introductionmentioning

confidence: 99%

“…So far, some drugs have been proved effective in relieving and improving the symptoms of novel coronavirus pneumonia (17)(18)(19)(20)(21)(22). The drugs against the Middle East respiratory syndrome coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-CoV), such as Lopinavir/ritonavir, have been proved to inhibit many viruses (22,23). As a nucleoside drug and RNA polymerase (RdRp) inhibitor, remandsivir can inhibit SARS-CoV-2 RdRp, subgenomic mRNA and subviral genomic RNA to block the synthesis of negative chain RNA, thus inhibiting virus replication and antiviral effect (24)(25)(26).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds

Hou

Ren

et al. 2021

Front. Oncol.

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Drug repositioning is a new way of applying the existing therapeutics to new disease indications. Due to the exorbitant cost and high failure rate in developing new drugs, the continued use of existing drugs for treatment, especially anti-tumor drugs, has become a widespread practice. With the assistance of high-throughput sequencing techniques, many efficient methods have been proposed and applied in drug repositioning and individualized tumor treatment. Current computational methods for repositioning drugs and chemical compounds can be divided into four categories: (i) feature-based methods, (ii) matrix decomposition-based methods, (iii) network-based methods, and (iv) reverse transcriptome-based methods. In this article, we comprehensively review the widely used methods in the above four categories. Finally, we summarize the advantages and disadvantages of these methods and indicate future directions for more sensitive computational drug repositioning methods and individualized tumor treatment, which are critical for further experimental validation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds

Hou

Ren

et al. 2021

Front. Oncol.

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“…Various laboratory and computational studies are underway in multiple fields, and to date, several vaccines have been approved to control the virus. On October 22, 2020, Remdesivir was approved by the US Food and Drug Administration (FDA) as the first official treatment for COVID-19 [26].…”

Section: Introductionmentioning

confidence: 99%

“…With the spread of the coronavirus, the importance of using this method to find effective drugs for this new and dangerous virus has doubled. For example, in 2020, based on a study by Lim et al, It was found that ribavirin, previously used to treat infectious diseases such as hepatitis, would also be effective in treating Covid-19 [15,26]. The usefulness of drug repositioning compared to traditional drug discovery methods is to optimize the time and cost of drug production and reduce the potential risks associated with drug toxicity.…”

Section: Introductionmentioning

confidence: 99%

CSDVP: Compressed Sensing for Drug-Virus Prediction

Besharatifard

Gharagozlou

2021

Preprint

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The 2019 Coronavirus (COVID-19) epidemic has recently hit most countries hard. Therefore, many researchers around the world are looking for a way to control this virus. Examining existing medications and using them to prevent this epidemic can be helpful. Drug repositioning solutions can be effective because designing and discovering a drug can be very time-consuming. Although no drug has been definitively approved for the treatment of this disease, the effectiveness of a few drugs for the treatment of the disease has been observed. In this study, with the help of computational matrix factorization methods, the associations between drugs and viruses have been predicted. By combining the similarities between the drugs and the similarities between the viruses and using the compressed sensing technique, we investigated the association between the drug and the virus. The Compressed Sensing approach to Drug-Virus Prediction (CSDVP) can work well. We compared the proposed method with other methods in this field and found its accuracy is more desirable than other methods. In fact, the CSDVP approach with the HDVD dataset and evaluation through 5-fold CV, with AUC = 0.96 and AUPR = 0.85, can identify the relationship between drugs and viruses. We also investigated the effect of drug properties on model performance improvement using autoencoder. Thus, with each decrease in the size of the characteristics in different sizes, we examined the performance of the CSDVP model in predicting the drug-virus relationship. The relationship between drugs and coronavirus infection is also analyzed, and the results are presented.

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A Matrix Factorization-Based Drug-Virus Link Prediction Method for SARS-CoV-2 Drug Prioritization

Li¹,

Xu²,

Tsoka³

2023

Lecture Notes in Computer Science

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Matrix factorization (MF) has been widely used in drug discovery for link prediction, which aims to reveal new drug-target links by integrating drug-drug and target-target similarity information with a drug-target interaction matrix. The MF method is based on the assumption that similar drugs share similar targets and vice versa. However, one major disadvantage is that the similarity matrices used in MF models mostly only describe structural similarity which does not entirely represent the similarity between drugs or targets. In this work, we have developed a similarity fusion enhanced MF model to incorporate different types of similarity for novel drug-target link prediction. We have applied the proposed model on a drug-virus association dataset for COVID, and compared the performance with other existing MF models developed for this disease. The results show that the similarity fusion method can provide more information for drug-drug and virus-virus similarity, and hence improve the performance of MF models. Finally, we provide the top 10 drugs as prioritized by our proposed model, and discuss how they have been supported by literature.

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Indicator Regularized Non-Negative Matrix Factorization Method-Based Drug Repurposing for COVID-19

Cited by 58 publications

References 56 publications

A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds

A Review of Current In Silico Methods for Repositioning Drugs and Chemical Compounds

CSDVP: Compressed Sensing for Drug-Virus Prediction

A Matrix Factorization-Based Drug-Virus Link Prediction Method for SARS-CoV-2 Drug Prioritization

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